The DOI matches its Crossref record; 268 of the 268 references it cites were found in a registry or an index; none of the 86 sources its claims cite is checked for support yet.
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
Open accessAbstract
Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties. In this context, many methods have been proposed for deep multi-modal perception problems. However, there is no general guideline for network architecture design, and questions of “what to fuse”, “when to fuse”, and “how to fuse” remain open. This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving. To this end, we first provide an overview of on-board sensors on test vehicles, open datasets, and background information for object detection and semantic segmentation in autonomous driving research. We then summarize the fusion methodologies and discuss challenges and open questions. In the appendix, we provide tables that summarize topics and methods. We also provide an interactive online platform to navigate each reference: https://boschresearch.github.io/multimodalperception/.
Overview
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TL;DR
This review systematically summarizes deep multi-modal object detection and semantic segmentation for autonomous driving, covering sensors, datasets, fusion methodologies, and challenges. It organizes fusion around “what to fuse,” “how to fuse,” and “when to fuse,” and highlights open questions in data diversity, alignment, uncertainty, and architecture design.
Key findings
- Most reviewed methods fuse RGB images with thermal images or LiDAR point clouds; only recently have Radar data been used, including nuScenes, Oxford Radar RobotCar, Astyx HiRes2019, and Chadwick et al.’s camera–Radar vehicle detection.
- Dataset size ranges from 1,569 frames up to over 11 million frames; the largest labeled dataset reviewed is nuScenes with nearly 1.4M frames, and multi-modal dataset size grew by two orders of magnitude between 2014 and 2019.
- The paper finds no conclusive evidence that one fusion method is better than others; performance is highly dependent on sensing modalities, data, and network architectures. Feature concatenation is the most common operation, especially at early and middle stages.
- Key challenges include limited data diversity, label errors, spatial/temporal misalignment, lack of uncertainty quantification, no guideline for optimal fusion architecture, metrics focused on accuracy rather than robustness, and current networks lacking temporal cues.
Methods
- Review/survey design: summarizes newly published multi-modal datasets (2013–2019) and fusion methodologies for deep multi-modal perception in autonomous driving, with background on sensors, test vehicles, deep object detection, and semantic segmentation.
- Data/sample: reviews datasets including KITTI, nuScenes, ApolloScape, KAIST, Waymo Open, and others; summarizes methods in tables and reports performance/runtime comparisons on the KITTI test set.
- Main analysis: organizes fusion by “what to fuse,” “how to fuse,” and “when to fuse”; discusses challenges and open questions in data preparation and fusion methodology; provides an interactive online platform for navigating references.
Limitations
- Stated in the paper: there is no conclusive evidence that one fusion method is better than others; the choice of when to fuse is mainly based on intuition and empirical results; no benchmark with standard hardware or programming languages makes inference-speed comparison difficult; most reviewed methods use simple fusion operations and do not explicitly model uncertainty; few studies fuse Radar, Ultrasonic, or V2X signals; no visual analytics research exists for deep multi-modal learning.
- Stated in the paper: common evaluation metrics summarize prediction accuracy but do not consider sensor behavior in different situations, open-set conditions, or degraded/defective sensors.
- Evident from the text: as a review, it does not present new experimental results or primary data; its performance comparisons rely on published leaderboard or author-reported numbers, which may not be directly comparable due to different hardware and settings.
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52 Own interpretation · 45 Not checked yet · 18 Sources not found · 17 No basis given · 3 Own result
None of the sources cited for this claim have been checked yet.
In the paper
“Only the very recently nuScenes [89], Oxford Radar RobotCar [85] and Astyx HiRes2019 Datasets [94] provide Radar data.”
What the sources say
- Not matchedcites [89] [89] H. Caesar et al. , 'nuScenes: A multimodal dataset for autonomous driving,' arXiv preprint arXiv:1903.11027 , 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [85] [85] D. Barnes, M. Gadd, P. Murcutt, P. Newman, and I. Posner, 'The Oxford radar RobotCar dataset: A radar extension to the Oxford RobotCar dataset,' arXiv preprint arXiv: 1909.01300 , 2019. [Online]. Available: https://arxiv.org/pdf/1909.01300 - source
- Not matchedcites [94] [94] M. Meyer and G. Kuschk, 'Automotive radar dataset for deep learning based 3d object detection,' in Proceedings of the 16th European Radar Conference , 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Most of the reviewed datasets provide ground-truth labels for 2D object detection and semantic segmentation tasks [60], [75], [88], [90]-[93].”
What the sources say
- Not checked yetcites [60] [60] X. Huang et al. , 'The ApolloScape dataset for autonomous driving,' in Workshop Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2018, pp. 954-960. - source
- Not checked yetcites [75] [75] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, 'Vision meets robotics: The KITTI dataset,' Int. J. Robotics Research , 2013. - source
- Not checked yetcites [88] [88] M. Braun, S. Krebs, F. B. Flohr, and D. M. Gavrila, 'EuroCity Persons: A novel benchmark for person detection in traffic scenes,' IEEE Trans. Pattern Anal. Mach. Intell. , pp. 1-1, 2019. - source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The distribution of objects is also very imbalanced, with much more objects being labeled as car than person or cyclist (Fig. 5).”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“In this regard, some papers including [98], [102]-[105], [107], [113], [115] combine RGB camera images and LiDAR point clouds for 3D object detection.”
What the sources say
- Not matchedcites [98] [98] X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, 'Multi-view 3D object detection network for autonomous driving,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2017, pp. 6526-6534. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [102]-[105]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [107] [107] X. Du, M. H. Ang Jr., S. Karaman, and D. Rus, 'A general pipeline for 3d detection of vehicles,' in IEEE Int. Conf. Robotics and Automation , 2018. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“The size of open multi-modal datasets is usually much smaller than the size of image datasets.”
What the sources say
- Not checked yetcites [6] [6] A. Geiger, P. Lenz, and R. Urtasun, 'Are we ready for autonomous driving? the KITTI vision benchmark suite,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2012. -
- Not checked yetcites [163] [163] O. Russakovsky et al. , 'ImageNet large scale visual recognition challenge,' Int. J. Computer Vision , vol. 115, no. 3, pp. 211-252, 2015. -
plainn = 1,034,908- Asserted: passes — The authors put it forward as true.
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In the paper
“While the interest in processing Radar signals via deep learning is growing, only a few papers discuss deep multi-modal perception with Radar for autonomous driving (e.g. [134]).”
What the sources say
- Not checked yetcites [134] [134] S. Chadwick, W. Maddern, and P. Newman, 'Distant vehicle detection using radar and vision,' in IEEE Int. Conf. Robotics and Automation , 2019. -
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Many works [61], [91], [99][101], [106], [108], [109], [111], [117]-[120], [123] deal with the 2D object detection problem on the front-view 2D image plane.”
What the sources say
- Not checked yetcites [61] [61] L. Schneider et al. , 'Multimodal neural networks: RGB-D for semantic segmentation and object detection,' in Scandinavian Conf. Image Analysis . Springer, 2017, pp. 98-109. - source
- Not matchedcites [91] [91] K. Takumi, K. Watanabe, Q. Ha, A. Tejero-De-Pablos, Y. Ushiku, and T. Harada, 'Multispectral object detection for autonomous vehicles,' in Proc. Thematic Workshops of ACM Multimedia , 2017, pp. 35-43. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [99][101]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The network is much more robust against labeling errors when they are randomly distributed, compared to biased labeling from the use of a deterministic pre-labeling.”
What the sources say
- Not checked yetcites [194] [194] C. Haase-Sch¨ utz, H. Hertlein, and W. Wiesbeck, 'Estimating labeling quality with deep object detectors,' in IEEE Intelligent Vehicles Symp. , June 2019, pp. 33-38. -
- Not checked yetcites [195] [195] S. Chadwick and P. Newman, 'Training object detectors with noisy data,' in IEEE Intelligent Vehicles Symp. , June 2019, pp. 1319-1325. -
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Compared to the object detection problem summarized in Sec. IV-A, there are fewer works on multi-modal semantic segmentation: [92], [119], [124] employ RGB and thermal images, [61] fuses RGB images and depth images from a stereo camera, [125]-[127] combine RGB, thermal, and depth images for semantic segmentation in diverse environments such as forests, [123] fuses RGB images and LiDAR point clouds”
What the sources say
- Not checked yetcites [92] [92] Q. Ha, K. Watanabe, T. Karasawa, Y. Ushiku, and T. Harada, 'MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes,' in IEEE/RSJ Int. Conf. Intelligent Robots and Systems , 2017, pp. 5108-5115. - source
- Not checked yetcites [119] [119] D. Guan, Y. Cao, J. Liang, Y. Cao, and M. Y. Yang, 'Fusion of multispectral data through illumination-aware deep neural networks for pedestrian detection,' Information Fusion , vol. 50, pp. 148-157, 2019. - source
- Not checked yetcites [124] [124] Y. Sun, W. Zuo, and M. Liu, 'RTFNet: Rgb-thermal fusion network for semantic segmentation of urban scenes,' IEEE Robotics and Automation Letters , 2019. - source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Most existing works combine RGB images from visual cameras with 3D LiDAR point clouds [98]-[116].”
What the sources say
- Not checked yetcites [98]-[116]
plain- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 41
- Probably not a substantial claim. 58
- The quote only partly states this claim. 1
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared to the image datasets in the computer vision community, the multi-modal datasets are still relatively small. However, the dataset size has grown by two orders of magnitudes between 2014 and 2019 (cf. Fig. 5(b)).”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Even though the KITTI dataset [75] is widely used for autonomous driving research, the diversity of its recording conditions is relatively low: it is recorded in Karlsruhe -a mid-sized city in Germany, only during daytime and on sunny days.”
What the sources say
- Not checked yetcites [75] [75] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, 'Vision meets robotics: The KITTI dataset,' Int. J. Robotics Research , 2013. - source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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In the paper
“All methods with the highest performance are based on deep learning, and many methods that fuse camera and LiDAR information produce better performance than those using either LiDAR or camera alone.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“All reviewed datasets include RGB camera images.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The recent Eurocity dataset [88] is the most diverse dataset we have reviewed. It is recorded in different cities from several European countries. All seasons are considered, as well as weather and daytime diversity. To date, the dataset is camera-only and other modalities (e.g. LiDARs) are announced.”
What the sources say
- Not checked yetcites [88] [88] M. Braun, S. Krebs, F. B. Flohr, and D. M. Gavrila, 'EuroCity Persons: A novel benchmark for person detection in traffic scenes,' IEEE Trans. Pattern Anal. Mach. Intell. , pp. 1-1, 2019. -
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“The dataset size ranges from only 1,569 frames up to over 11 million frames. The largest dataset with ground-truth labels that we have reviewed is the nuScenes Dataset [89] with nearly 1,4M frames.”
What the sources say
- Not matchedcites [89] [89] H. Caesar et al. , 'nuScenes: A multimodal dataset for autonomous driving,' arXiv preprint arXiv:1903.11027 , 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, many new multi-modal datasets have been released since 2016, and it is worth summarizing them.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In this regard, many methods have been proposed that employ deep learning to fuse multi-modal sensors for scene understanding in autonomous driving.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
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In the paper
“From the tables we have three observations: (1). Most works propose to fuse LiDAR and camera features extracted from 2D convolution neural networks. To do this, they project LiDAR points on the 2D plane and process the feature maps through 2D convolutions. Only a few works extract LiDAR features by PointNet (e.g. [104], [105], [128]) or 3D convolutions (e.g. [123]); (2). Several works on multi”
What the sources say
- Not checked yetcites [104] [104] D. Xu, D. Anguelov, and A. Jain, 'PointFusion: Deep sensor fusion for 3D bounding box estimation,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2018. - source
- Not checked yetcites [105] [105] C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, 'Frustum PointNets for 3d object detection from RGB-D data,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2018. - source
- Not checked yetcites [128] [128] F. Yang, J. Yang, Z. Jin, and H. Wang, 'A fusion model for road detection based on deep learning and fully connected CRF,' in 13th Annu. Conf. System of Systems Engineering . IEEE, 2018, pp. 29-36. - source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one. The quote only partly states this claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“LiDARs and cameras (visual cameras, thermal cameras) are the most common sensors for multi-modal perception in the literature.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Some other works focus on fusing the RGB images from visual cameras with images from thermal cameras [91], [117]-[119].”
What the sources say
- Not checked yetcites [91] [91] K. Takumi, K. Watanabe, Q. Ha, A. Tejero-De-Pablos, Y. Ushiku, and T. Harada, 'Multispectral object detection for autonomous vehicles,' in Proc. Thematic Workshops of ACM Multimedia , 2017, pp. 35-43. -
- Not checked yetcites [117]-[119]
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There are mainly three ways to process point clouds.”
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Additionally, there is a lack of research on multi-modal perception in openset conditions or with sensor failures.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“A deep neural network is a powerful tool for learning hierarchical feature representations given a large amount of data [5].”
What the sources say
- Not checked yetcites [5] [5] Y. LeCun, Y. Bengio, and G. Hinton, 'Deep learning,' Nature , vol. 521, no. 7553, p. 436, 2015. - source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, there is no general guideline for network architecture design, and questions of 'what to fuse', 'when to fuse', and 'how to fuse' remain open.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To the best of our knowledge, there is no survey that focuses on deep multi-modal object detection (2D or 3D) and semantic segmentation for autonomous driving, which makes it difficult for beginners to enter this research field.”
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although the middle fusion approach is highly flexible, it is not easy to find the 'optimal' way to fuse intermediate layers given a specific network architecture.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Middle fusion is the compromise of early and late fusion: It combines the feature representations from different sensing modalities at intermediate layers.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A CPM can be produced by projecting the 3D points into the camera coordinate system, provided the calibration matrix. A CPM can be directly fused with camera images, as their sizes are the same. However, this representation leaves many pixels empty.”
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This enables the network to learn cross modalities with different feature representations and at different depths.”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared to 2D detection, 3D detection is more challenging since the object's distance to the ego-vehicle needs to be estimated. Therefore, accurate depth information provided by LiDAR sensors is highly beneficial.”
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Deep neural networks represent features hierarchically and offer a wide range of choices to combine sensing modalities at early, middle, or late stages (Fig. 8).”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Based on the papers that we have reviewed, feature concatenation is the most common operation, especially at early and middle stages.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, using large multi-modal datasets with diverse driving conditions, object labels, and sensors can significantly improve the network's accuracy and robustness against changing environments.”
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A reliable perception system is the prerequisite for a driverless car to run safely in uncontrolled and complex driving environments.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, early fusion has low computation requirements and a low memory budget as it jointly processes the multiple sensing modalities.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As discussed in the Introduction (cf. Sec. I), developing deep multi-modal perception systems is especially challenging for autonomous driving because it has high requirements in accuracy, robustness, and real-time performance.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The predictions from object detection or semantic segmentation are usually transferred to other modules such as maneuver prediction and decision making.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, the network learns the joint features of multiple modalities at an early stage, fully exploiting the information of the raw data.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared to object detections from LiDAR point clouds, camera images have been well investigated with larger labeled dataset and better 2D detection performance.”
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“A spherical map is obtained by projecting each 3D point onto a sphere, characterized by azimuth and zenith angles. It has the advantage of representing each 3D point in a dense and compact way, making it a suitable representation for point cloud segmentation (e.g. [51]). However, the size of the representation can be different from camera images. Therefore, it is difficult to fuse them at an early”
What the sources say
- Not checked yetcites [51] [51] B. Wu, A. Wan, X. Yue, and K. Keutzer, 'SqueezeSeg: Convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3d lidar point cloud,' in IEEE Int. Conf. Robotics and Automation , May 2018, pp. 1887-1893. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared to the semantic segmentation where multimodal features are fused at different stages in FCN, there exist more diverse network architectures and more fusion variants in object detection.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, it suffers from high computation cost and memory requirements.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Camera images provide rich texture information of the driving surroundings. However, objects can be occluded and the scale of a single object can vary significantly in the camera image plane. For 3D environment inference, the bird's eye view that is commonly used for LiDAR point clouds might be a better representation.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, it discards rich intermediate features which may be highly beneficial when being fused.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared to the above-mentioned feature maps which encode LiDAR information in the front-view, a BEV map avoids occlusion problems because objects occupy different space in the map. In addition, the BEV preserves the objects' length and width, and directly provides the objects' positions on the ground plane, making the localization task easier. Therefore, the BEV map is widely applied to 3D”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Training a deep neural network on a complex task requires a huge amount of data.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This indicates that the networks implicitly learn to align features of different viewpoints. Therefore, a well-calibrated sensor setup with accurate spatial and temporal alignment is the prerequisite for accurate multi-modal perception, as will be discussed in Sec. VI-A2.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Late fusion has high flexibility and modularity.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There are various ways to fuse sensing modalities in neural networks, encompassing different sensor representations, cf. Sec. V-A, fusion operations cf. Sec. V-B, and fusion stages, cf. Sec. V-C.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“650 scans (dense), 34200 scans (sparse)”
What the sources say
- Not checked yetcites [77] [77] H. Jung, Y. Oto, O. M. Mozos, Y. Iwashita, and R. Kurazume, 'Multimodal panoramic 3d outdoor datasets for place categorization,' in IEEE/RSJ Int. Conf. Intelligent Robots and Systems , 2016, pp. 45454550. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“95 , 328 frames, 103 , 128 objects”
What the sources say
- Not matchedcites [90] [90] S. Hwang, J. Park, N. Kim, Y. Choi, and I. So Kweon, 'Multispectral pedestrian detection: Benchmark dataset and baseline,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2015, pp. 1037-1045. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Well-calibrated sensors are the prerequisite for accurate and robust multi-modal perception systems.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Visual and thermal camera”
What the sources say
- Not matchedcites [90] [90] S. Hwang, J. Park, N. Kim, Y. Choi, and I. So Kweon, 'Multispectral pedestrian detection: Benchmark dataset and baseline,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2015, pp. 1037-1045. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“2015”
What the sources say
- Not matchedcites [90] [90] S. Hwang, J. Park, N. Kim, Y. Choi, and I. So Kweon, 'Multispectral pedestrian detection: Benchmark dataset and baseline,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2015, pp. 1037-1045. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There is no conclusive evidence that one fusion scheme is better than the others.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“So far, we are not aware of any work fusing Ultrasonic sensor signals in deep multi-modal perception, despite its relevance for low-speed scenarios.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although much progress has been made for BNNs, to the best of our knowledge, so far they have not been introduced to multi-modal perception.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Car, Van, Truck, Pedestrian, Person (sitting), Cyclist, Tram, Misc”
What the sources say
- Not checked yetcites [6] [6] A. Geiger, P. Lenz, and R. Urtasun, 'Are we ready for autonomous driving? the KITTI vision benchmark suite,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2012. - source
- Not checked yetcites [75] [75] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, 'Vision meets robotics: The KITTI dataset,' Int. J. Robotics Research , 2013. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Seoul”
What the sources say
- Not checked yetcites [90] [90] S. Hwang, J. Park, N. Kim, Y. Choi, and I. So Kweon, 'Multispectral pedestrian detection: Benchmark dataset and baseline,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2015, pp. 1037-1045. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, there are very few studies for fusing LiDAR and camera outputs with signals from other sources such as Radars, ultrasonics or V2X communication.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“7481 frames (training) 80 . 256 objects”
What the sources say
- Not checked yetcites [6] [6] A. Geiger, P. Lenz, and R. Urtasun, 'Are we ready for autonomous driving? the KITTI vision benchmark suite,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2012. - source
- Not checked yetcites [75] [75] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, 'Vision meets robotics: The KITTI dataset,' Int. J. Robotics Research , 2013. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Visual camera, LiDAR, and GNSS”
What the sources say
- Not matchedcites [77] [77] H. Jung, Y. Oto, O. M. Mozos, Y. Iwashita, and R. Kurazume, 'Multimodal panoramic 3d outdoor datasets for place categorization,' in IEEE/RSJ Int. Conf. Intelligent Robots and Systems , 2016, pp. 45454550. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“No dynamic objects”
What the sources say
- Not matchedcites [77] [77] H. Jung, Y. Oto, O. M. Mozos, Y. Iwashita, and R. Kurazume, 'Multimodal panoramic 3d outdoor datasets for place categorization,' in IEEE/RSJ Int. Conf. Intelligent Robots and Systems , 2016, pp. 45454550. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Visual (Stereo) camera, 3D LiDAR, GNSS, and inertial sensors”
What the sources say
- Not checked yetcites [6] [6] A. Geiger, P. Lenz, and R. Urtasun, 'Are we ready for autonomous driving? the KITTI vision benchmark suite,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2012. - source
- Not checked yetcites [75] [75] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, 'Vision meets robotics: The KITTI dataset,' Int. J. Robotics Research , 2013. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Fukuoka”
What the sources say
- Not matchedcites [77] [77] H. Jung, Y. Oto, O. M. Mozos, Y. Iwashita, and R. Kurazume, 'Multimodal panoramic 3d outdoor datasets for place categorization,' in IEEE/RSJ Int. Conf. Intelligent Robots and Systems , 2016, pp. 45454550. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“2D bounding box”
What the sources say
- Not matchedcites [90] [90] S. Hwang, J. Park, N. Kim, Y. Choi, and I. So Kweon, 'Multispectral pedestrian detection: Benchmark dataset and baseline,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2015, pp. 1037-1045. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“2014”
What the sources say
- Not checked yetcites [76] [76] J.-L. Blanco-Claraco, F.- ´ A. Moreno-Due˜ nas, and J. Gonz´ alez-Jim´ enez, 'The M´ alaga urban dataset: High-rate stereo and lidar in a realistic urban scenario,' Int. J. Robotics Research , vol. 33, no. 2, pp. 207-214, 2014. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“2012, 2013, 2015”
What the sources say
- Not checked yetcites [6] [6] A. Geiger, P. Lenz, and R. Urtasun, 'Are we ready for autonomous driving? the KITTI vision benchmark suite,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2012. - source
- Not checked yetcites [75] [75] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, 'Vision meets robotics: The KITTI dataset,' Int. J. Robotics Research , 2013. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Person, People, Cyclist during day and night”
What the sources say
- Not matchedcites [90] [90] S. Hwang, J. Park, N. Kim, Y. Choi, and I. So Kweon, 'Multispectral pedestrian detection: Benchmark dataset and baseline,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2015, pp. 1037-1045. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The predictions in a frame are not dependent on previous frames, resulting in inconsistency over time.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Karlsruhe”
What the sources say
- Not checked yetcites [6] [6] A. Geiger, P. Lenz, and R. Urtasun, 'Are we ready for autonomous driving? the KITTI vision benchmark suite,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2012. - source
- Not checked yetcites [75] [75] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, 'Vision meets robotics: The KITTI dataset,' Int. J. Robotics Research , 2013. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Place categorization”
What the sources say
- Not checked yetcites [77] [77] H. Jung, Y. Oto, O. M. Mozos, Y. Iwashita, and R. Kurazume, 'Multimodal panoramic 3d outdoor datasets for place categorization,' in IEEE/RSJ Int. Conf. Intelligent Robots and Systems , 2016, pp. 45454550. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Visual (Stereo) camera, 5 × 2D LiDAR (yielding 3D information), GNSS and inertial sensors”
What the sources say
- Not checked yetcites [76] [76] J.-L. Blanco-Claraco, F.- ´ A. Moreno-Due˜ nas, and J. Gonz´ alez-Jim´ enez, 'The M´ alaga urban dataset: High-rate stereo and lidar in a realistic urban scenario,' Int. J. Robotics Research , vol. 33, no. 2, pp. 207-214, 2014. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Each method uses different hardware, and the inference time is reported only by the authors.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“no”
What the sources say
- Not checked yetcites [76] [76] J.-L. Blanco-Claraco, F.- ´ A. Moreno-Due˜ nas, and J. Gonz´ alez-Jim´ enez, 'The M´ alaga urban dataset: High-rate stereo and lidar in a realistic urban scenario,' Int. J. Robotics Research , vol. 33, no. 2, pp. 207-214, 2014. - source
plain- Asserted: fails — The authors may not put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, these metrics only summarize the prediction accuracy to a test dataset. They do not consider how sensor behaves in different situations.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Explicitly modeling uncertainty or informativeness of each sensing modality is important safe autonomous driving.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“2D/3D bounding box, visual odometry, road, optical flow, tracking, depth, 2D instance and pixel-level segmentation”
What the sources say
- Not checked yetcites [6] [6] A. Geiger, P. Lenz, and R. Urtasun, 'Are we ready for autonomous driving? the KITTI vision benchmark suite,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2012. - source
- Not checked yetcites [75] [75] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, 'Vision meets robotics: The KITTI dataset,' Int. J. Robotics Research , 2013. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“2016”
What the sources say
- Not matchedcites [77] [77] H. Jung, Y. Oto, O. M. Mozos, Y. Iwashita, and R. Kurazume, 'Multimodal panoramic 3d outdoor datasets for place categorization,' in IEEE/RSJ Int. Conf. Intelligent Robots and Systems , 2016, pp. 45454550. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“M´ alaga”
What the sources say
- Not checked yetcites [76] [76] J.-L. Blanco-Claraco, F.- ´ A. Moreno-Due˜ nas, and J. Gonz´ alez-Jim´ enez, 'The M´ alaga urban dataset: High-rate stereo and lidar in a realistic urban scenario,' Int. J. Robotics Research , vol. 33, no. 2, pp. 207-214, 2014. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, current methods are designed to propagate results to other modules in autonomous driving, such as localization, planning, and reasoning.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This could cause severe errors in training datasets and degrade the performance of networks, especially for those which are designed to implicitly learn the sensor alignment (e.g. networks that fuse LiDAR BEV feature maps and front view camera images cf. Sec. V-A3).”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“n.a.”
What the sources say
- Not checked yetcites [76] [76] J.-L. Blanco-Claraco, F.- ´ A. Moreno-Due˜ nas, and J. Gonz´ alez-Jim´ enez, 'The M´ alaga urban dataset: High-rate stereo and lidar in a realistic urban scenario,' Int. J. Robotics Research , vol. 33, no. 2, pp. 207-214, 2014. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Qi et al. , 2017 [105] | 70.39 % | 81.20 % | 62.19 % | 44.89 % | 51.21 % | 40.23 % | 56.77 % | 71.96 % | 50.39 % | 0.17 s | GPU @ 3.0 Ghz (Python) |”
What the sources say
- Not checked yetcites [105] [105] C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, 'Frustum PointNets for 3d object detection from RGB-D data,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2018. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Liang et al. , 2018 [150] | 66.22 % | 82.54 % | 64.04 % | - | - | - | - | - | - | 0.06 s | GPU @ 2.5 Ghz (Python) |”
What the sources say
- Not matchedcites [150] [150] M. Liang, B. Yang, S. Wang, and R. Urtasun, 'Deep continuous fusion for multi-sensor 3d object detection,' in Proc. Eur. Conf. Computer Vision , 2018, pp. 641-656. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“Sindagi et al. , 2019 [113] | 72.7 % | 83.2 % | 65.12 % | - | - | - | - | - | - | - | - |”
What the sources say
- Not checked yetcites [113] [113] V. A. Sindagi, Y. Zhou, and O. Tuzel, 'MVX-Net: Multimodal voxelnet for 3D object detection,' in IEEE Int. Conf. Robotics and Automation , 2019. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Ku et al. , 2017 [103] | 71.88 % | 81.94 % | 66.38 % | 42.81 % | 50.80 % | 40.88 % | 52.18 % | 64.00 % | 46.61 % | 0.1 s | GPU Titan X (Pascal) |”
What the sources say
- Not checked yetcites [103] [103] J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. Waslander, 'Joint 3d proposal generation and object detection from view aggregation,' in IEEE/RSJ Int. Conf. Intelligent Robots and Systems , Oct. 2018, pp. 1-8. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Liang et al. , 2019 [116] | 76.75 % | 86.81 % | 68.41 % | 45.61 % | 52.37 % | 41.49 % | 64.68 % | 79.58 % | 57.03 % | 0.08 s | GPU @ 2.5 Ghz (Python) |”
What the sources say
- Not matchedcites [116] [116] M. Liang, B. Yang, Y. Chen, R. Hu, and R. Urtasun, 'Multi-task multisensor fusion for 3D object detection,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2019, pp. 7345-7353. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“Qi et al. , 2017 [105] | 70.39 % | 81.20 % | 62.19 % | 44.89 % | 51.21 % | 40.23 % | 56.77 % | 71.96 % | 50.39 % | 0.17 s | GPU @ 3.0 Ghz (Python) |”
What the sources say
- Not checked yetcites [105] [105] C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, 'Frustum PointNets for 3d object detection from RGB-D data,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2018. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Shin et al. , 2018 [141] | 73.04 % | 83.71 % | 59.16 % | - | - | - | - | - | - | - | GPU Titan X (not Pascal) |”
What the sources say
- Not checked yetcites [141] [141] K. Shin, Y. P. Kwon, and M. Tomizuka, 'RoarNet: A robust 3D object detection based on region approximation refinement,' in IEEE Intelligent Vehicles Symp. , 2018. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“113 , 082frames, 5 , 654 . 6s (camera); > 220 , 000frames, 5 , 000s (LiDARs)”
What the sources say
- Not checked yetcites [76] [76] J.-L. Blanco-Claraco, F.- ´ A. Moreno-Due˜ nas, and J. Gonz´ alez-Jim´ enez, 'The M´ alaga urban dataset: High-rate stereo and lidar in a realistic urban scenario,' Int. J. Robotics Research , vol. 33, no. 2, pp. 207-214, 2014. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Liang et al. , 2019 [116] | 76.75 % | 86.81 % | 68.41 % | 45.61 % | 52.37 % | 41.49 % | 64.68 % | 79.58 % | 57.03 % | 0.08 s | GPU @ 2.5 Ghz (Python) |”
What the sources say
- Not matchedcites [116] [116] M. Liang, B. Yang, Y. Chen, R. Hu, and R. Urtasun, 'Multi-task multisensor fusion for 3D object detection,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2019, pp. 7345-7353. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“Ku et al. , 2017 [103] | 71.88 % | 81.94 % | 66.38 % | 42.81 % | 50.80 % | 40.88 % | 52.18 % | 64.00 % | 46.61 % | 0.1 s | GPU Titan X (Pascal) |”
What the sources say
- Not checked yetcites [103] [103] J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. Waslander, 'Joint 3d proposal generation and object detection from view aggregation,' in IEEE/RSJ Int. Conf. Intelligent Robots and Systems , Oct. 2018, pp. 1-8. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Chen et al. , 2017 [98] | 62.35 % | 71.09 % | 55.12 % | - | - | - | - | - | - | 0.36 s | GPU @ 2.5 Ghz (Python + C/C++) |”
What the sources say
- Not matchedcites [98] [98] X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, 'Multi-view 3D object detection network for autonomous driving,' in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2017, pp. 6526-6534. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper relies on this without support.
Recent advancements in perception for autonomous driving are driven by deep learning.
“Recent advancements in perception for autonomous driving are driven by deep learning.”
The paper relies on this without support.
Multiple sensing modalities can be fused to exploit their complementary properties.
“In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties.”
The paper relies on this without support.
Autonomous driving requires high accuracy, robustness, and real-time performance.
“developing deep multi-modal perception systems is especially challenging for autonomous driving because it has high requirements in accuracy, robustness, and real-time performance.”
The paper relies on this without support.
A reliable perception system is prerequisite for safe driving.
“A reliable perception system is the prerequisite for a driverless car to run safely in uncontrolled and complex driving environments.”
The paper relies on this without support.
Training a deep neural network on a complex task requires a huge amount of data.
“Training a deep neural network on a complex task requires a huge amount of data.”
The paper relies on this without support.
Data quality significantly affects the performance of a deep multi-modal perception system.
“Besides data diversity and the size of the training dataset, data quality significantly affects the performance of a deep multi-modal perception system as well.”
The paper relies on this without support.
Well-calibrated sensors are prerequisite for accurate and robust multi-modal perception systems.
“Well-calibrated sensors are the prerequisite for accurate and robust multi-modal perception systems.”
The paper relies on this without support.
Explicitly modeling uncertainty or informativeness of each sensing modality is important for safe autonomous driving.
“Explicitly modeling uncertainty or informativeness of each sensing modality is important safe autonomous driving.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Other datasets focus exclusively on pedestrian annotations on images [20, 25, 79, 24, 88, 23, 58].”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [25]
Experimental — accuracy not yet measuredIn the citing paper
“early concatenation that can encode the global multimodal context well [188]”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [188]
In the citing paper
“As discussed in [188], two-stream crossattention can learn cross-modal interaction, whereas there is no self-attention to the self-context inside each modality.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [188]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A. Deep Multi-modal Object Detection · p. 5
“Many works [61], [91], [99][101], [106], [108], [109], [111], [117]-[120], [123] deal with the 2D object detection problem on the front-view 2D image plane.”
§ TABLE III: SUMMARY OF MULTI-MODAL OBJECT DECTECTION METHODS · p. 26
“| Kim et al. , 2018 [123] | LiDAR, visual camera | 2D Off-road terrains”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ C. Dataset Size · p. 5
“The dataset size ranges from only 1,569 frames up to over 11 million frames.”
§ TABLE II: OVERVIEW OF MULTI-MODAL DATASETS · p. 21
“| Astyx HiRes2019 [94] | Radar, Visual camera, 3D LiDAR | 2019 | (benchmark) 3D bounding boxes | n.a. | 500 frames (5000 annotated objects)”
Citations
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Sorted by citations
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SELMA: SEmantic Large-Scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints
Testolina, P., Barbato, F., Michieli, U., Giordani, M., Zanuttigh, P., & Zorzi, M. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3257086 · Cited by 41
Dispersed Pixel Perturbation-Based Imperceptible Backdoor Trigger for Image Classifier Models
Wang, Y., Zhao, M., Li, S., Yuan, X., & Ni, W. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3202687 · Cited by 26
Transformer-based sensor fusion for autonomous vehicles: A comprehensive review
Abdulmaksoud, A., & Ahmed, R. (2025) · IEEE Access · doi:10.1109/access.2025.3545032 · Cited by 25
The Static Fusion Paradox: A Degradation-Centric Survey of Multimodal Perception in Adverse Weather
Godugu, S. A. (2026) · 2026 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/iv66570.2026.11624098
Metadata
- Authors
- Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K.
- Affiliations
- Universität Ulm (DE)
- Karlsruhe Institute of Technology (DE)
- Robert Bosch (Germany) (DE)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2021-03
- Volume
- 22
- Issue
- 3
- Pages
- 1341-1360
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W3008115128
- Cited by
- 1,425
- References
- 268
- Citations by year
- 2026: 178 · 2025: 274 · 2024: 308 · 2023: 263 · 2022: 181 · 2021: 144
- FWCI
- 60.51
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Robert Bosch GmbH
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- multisensory perception, autonomous driving, object detection, semantic segmentation, sensor fusion, LiDAR, radar, camera, fusion method, onboard sensors, benchmark dataset, scene understanding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-08, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-08 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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