The DOI matches its Crossref record; 57 of the 61 references it cites were found in a registry or an index.
MP3: A Unified Model to Map, Perceive, Predict and Plan
Open accessAbstract
High-definition maps (HD maps) are a key component of most modern self-driving systems due to their valuable semantic and geometric information. Unfortunately, building HD maps has proven hard to scale due to their cost as well as the requirements they impose in the localization system that has to work everywhere with centimeter-level accuracy. Being able to drive without an HD map would be very beneficial to scale self-driving solutions as well as to increase the failure tolerance of existing ones (e.g., if localization fails or the map is not up-to-date). Towards this goal, we propose MP3, an end-to-end approach to mapless1driving where the input is raw sensor data and a high-level command (e.g., turn left at the intersection). MP3 predicts intermediate representations in the form of an online map and the current and future state of dynamic agents, and exploits them in a novel neural motion planner to make interpretable decisions taking into account uncertainty. We show that our approach is significantly safer, more comfortable, and can follow commands better than the baselines in challenging long-term closed-loop simulations, as well as when compared to an expert driver in a large-scale real-world dataset.
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“These works have shown that MTL achieves better data utilization at lower computation cost.”
From Towards learning-based planning: The nuPlan benchmark for real-world autonomous driving (2024) · cites this paper as [58]
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Are we ready for autonomous driving? The KITTI vision benchmark suite
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Self-citationMulti-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
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PointPillars: Fast Encoders for Object Detection From Point Clouds
Lang, A. H., Vora, S., Caesar, H., Zhou, L., Yang, J., & Beijbom, O. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01298 · Cited by 4,673
End to End Learning for Self-Driving Cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al. (2016) · arXiv · doi:10.48550/arxiv.1604.07316 · Cited by 3,198
Using occupancy grids for mobile robot perception and navigation
Elfes, A. (1989) · Computer · doi:10.1109/2.30720 · Cited by 2,287
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
Ross, S., Gordon, G. J., & Bagnell, J. A. (2018) · Carnegie Mellon University · doi:10.1184/r1/6550949 · Cited by 1,941
ALVINN, an autonomous land vehicle in a neural network
Pomerleau, D. (2018) · doi:10.1184/r1/6603146 · Cited by 1,369
Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D
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PIXOR: Real-time 3D Object Detection from Point Clouds
Yang, B., Luo, W., & Urtasun, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00798 · Cited by 1,249
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Research works printed but not linked: 1
- zhao, 2019, Multiagent tensor fusion for contextual trajectory prediction, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 12126
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Planning-oriented Autonomous Driving
Hu, Y., Yang, J., Chen, L., Li, K., Sima, C., Zhu, X., Chai, S., Du, S., Lin, T., Wang, W., et al. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01712 · Cited by 1,139
End-to-End Autonomous Driving: Challenges and Frontiers
Chen, L., Wu, P., Chitta, K., Jaeger, B., Geiger, A., & Li, H. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3435937 · Cited by 843
3D Object Detection for Autonomous Driving: A Comprehensive Survey
Mao, J., Shi, S., Wang, X., & Li, H. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01790-1 · Cited by 529
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
Jiang, B., Chen, S., Xu, Q., Liao, B., Chen, J., Zhou, H., Zhang, Q., Liu, W., Huang, C., & Wang, X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00766 · Cited by 471
Atakishiyev, S., Salameh, M., Yao, H., & Goebel, R. G. (2024) · IEEE Access · doi:10.1109/access.2024.3431437 · Cited by 459
Metadata
- Authors
- Casas, S., Sadat, A., & Urtasun, R.
- Affiliations
- University of Toronto (CA)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 14398-14407
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3125926538
- Cited by
- 228
- References
- 61
- Citations by year
- 2026: 17 · 2025: 49 · 2024: 59 · 2023: 59 · 2022: 34 · 2021: 10
- FWCI
- 40.11
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- autonomous vehicles, end-to-end driving, online maps, decision making under uncertainty, high-definition maps, closed-loop simulation
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