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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Multi-task learning jointly training perception, prediction and planning achieves better data utilization at lower computation cost.
“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]
The Training details (p. 13) describe multi-task training of mapping, perception and prediction, while Learning (p. 6) says the planner is trained separately after those components are frozen. Neither passage shows that this approach improves data utilization or lowers computation cost, so the claimed benefits are unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · see citing works
Self-citationMulti-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738 · see citing works
Congested traffic states in empirical observations and microscopic simulations
Treiber, M., Hennecke, A., & Helbing, D. (2000) · Physical Review E · doi:10.1103/physreve.62.1805 · Cited by 4,762 · see citing works
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 · see citing works
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 · see citing works
Using occupancy grids for mobile robot perception and navigation
Elfes, A. (1989) · Computer · doi:10.1109/2.30720 · Cited by 2,235 · see citing works
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 · see citing works
ALVINN, an autonomous land vehicle in a neural network
Pomerleau, D. A. (2018) · doi:10.1184/r1/6603146 · Cited by 1,403 · see citing works
Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D
Philion, J., & Fidler, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58568-6_12 · Cited by 1,271 · see citing works
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 · see citing works
Self-citationOptimal trajectory generation for dynamic street scenarios in a Frenét Frame
Werling, M., Ziegler, J., Kammel, S., & Thrun, S. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509799 · Cited by 806 · see citing works
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Liu, R., Lehman, J., Molino, P., Such, F. P., Frank, E., Sergeev, A., & Yosinski, J. (2018) · arXiv · doi:10.48550/arxiv.1807.03247 · Cited by 784 · see citing works
Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J.-M., Lam, V.-D., Bewley, A., & Shah, A. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8793742 · Cited by 694 · cited 1× · see citing works
“Subsequent to the success of deep learning, direct control based methods have advanced with deeper networks, richer sensors, and scalable learning methods [5, 11, 24, 35].”
BackgroundLuo, W., Yang, B., & Urtasun, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00376 · Cited by 644 · see citing works
Self-citationRatliff, N., Bagnell, J. A., & Zinkevich, M. (2006) · International Conference on Machine Learning · doi:10.1145/1143844.1143936 · Cited by 617 · see citing works
DeepRoadMapper: Extracting Road Topology from Aerial Images
Máttyus, G., Luo, W., & Urtasun, R. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.372 · Cited by 568 · see citing works
Self-citationExploring the Limitations of Behavior Cloning for Autonomous Driving
Codevilla, F., Santana, E., López, A. M., & Gaidon, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00942 · Cited by 562 · see citing works
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction
Zhao, T., Xu, Y., Monfort, M., Choi, W., Baker, C. L., Zhao, Y., Wang, Y., & Wu, Y. N. (2019) · 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/cvpr.2019.01240 · Cited by 492 · see citing works
Learning Occupancy Grid Maps with Forward Sensor Models
Thrun, S. (2003) · Autonomous Robots · doi:10.1023/a:1025584807625 · Cited by 483 · see citing works
MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior PredictionPreprint
Chai, Y., Sapp, B., Bansal, M., & Anguelov, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05449 · Cited by 482 · see citing works
CoverNet: Multimodal Behavior Prediction Using Trajectory Sets
Phan-Minh, T., Grigore, E. C., Boulton, F. A., Beijbom, O., & Wolff, E. M. (2020) · 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/cvpr42600.2020.01408 · Cited by 456 · see citing works
Polack, P., Altché, F., d’Andréa-Novel, B., & Fortelle, A. D. L. (2017) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2017.7995816 · Cited by 423 · see citing works
End-To-End Interpretable Neural Motion Planner
Zeng, W., Luo, W., Suo, S., Sadat, A., Yang, B., Casas, S., & Urtasun, R. (2019) · 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/cvpr.2019.00886 · Cited by 406 · see citing works
Self-citationRoadTracer: Automatic Extraction of Road Networks from Aerial Images
Bastani, F., He, S., Abbar, S., Alizadeh, M., Balakrishnan, H., Chawla, S., Madden, S. R., & DeWitt, D. J. (2018) · 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/cvpr.2018.00496 · Cited by 383 · see citing works
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
Meyer, G. P., Laddha, A., Kee, E., Vallespi-Gonzalez, C., & Wellington, C. K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01296 · Cited by 374 · see citing works
Multiple Futures PredictionPreprint
Tang, Y., & Salakhutdinov, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00997 · Cited by 265 · see citing works
IntentNet: Learning to Predict Intention from Raw Sensor DataPreprint
Casas, S., Luo, W., & Urtasun, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.07907 · Cited by 248 · see citing works
Self-citationLiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
Manivasagam, S., Wang, S., Wong, K., Zeng, W., Sazanovich, M., Tan, S., Yang, B., Ma, W.-C., & Urtasun, R. (2020) · 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/cvpr42600.2020.01118 · Cited by 241 · see citing works
Self-citationEnd-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point CloudsPreprint
Zhou, Y., Sun, P., Zhang, Y., Anguelov, D., Gao, J., Ouyang, T. Y., Guo, J. C. Y., Ngiam, J., & Vasudevan, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.06528 · Cited by 237 · see citing works
3D-LaneNet: End-to-End 3D Multiple Lane Detection
Garnett, N., Cohen, R., Pe'er, T., Lahav, R., & Levi, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00301 · Cited by 212 · see citing works
r2p2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting
Rhinehart, N., Kitani, K., & Vernaza, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_47 · Cited by 212 · see citing works
PnPNet: End-to-End Perception and Prediction With Tracking in the Loop
Liang, M., Yang, B., Zeng, W., Chen, Y., Hu, R., Casas, S., & Urtasun, R. (2020) · 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/cvpr42600.2020.01157 · Cited by 190 · see citing works
Self-citationWu, P., Chen, S., & Metaxas, D. (2020) · 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/cvpr42600.2020.01140 · Cited by 188 · see citing works
The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction
Liang, J., Jiang, L., Murphy, K. P., Yu, T., & Hauptmann, A. G. (2020) · 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/cvpr42600.2020.01052 · Cited by 183 · see citing works
Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
Sadat, A., Casas, S., Ren, M., Wu, X., Dhawan, P., & Urtasun, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58592-1_25 · Cited by 183 · see citing works
Self-citationSpAGNN: Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data
Casas, S., Gulino, C., Liao, R., & Urtasun, R. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196697 · Cited by 181 · see citing works
Self-citationHoermann, S., Bach, M., & Dietmayer, K. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460874 · Cited by 171 · see citing works
Driving Policy Transfer via Modularity and AbstractionPreprint
Müller, M., Dosovitskiy, A., Ghanem, B., & Koltun, V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.09364 · Cited by 167 · see citing works
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
Casas, S., Gulino, C., Suo, S., Luo, K. Z., Liao, R., & Urtasun, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58592-1_37 · Cited by 152 · see citing works
Self-citationUrban Driving with Conditional Imitation Learning
Hawke, J., Shen, R., Gurău, C., Sharma, S., Reda, D., Nikolov, N., Mazur, P., Micklethwaite, S., Griffiths, N., Shah, A., et al. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197408 · Cited by 146 · see citing works
End-to-End Driving Via Conditional Imitation Learning
Codevilla, F., Muller, M., Lopez, A., Koltun, V., & Dosovitskiy, A. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8460487 · Cited by 129 · see citing works
Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection
Guo, Y., Chen, G., Zhao, P., Zhang, W., Miao, J., Wang, J., & Choe, T. E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58589-1_40 · Cited by 127 · see citing works
End-to-end Contextual Perception and Prediction with Interaction Transformer
Li, L. L., Yang, B., Liang, M., Zeng, W., Ren, M., Segal, S., & Urtasun, R. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341392 · Cited by 127 · see citing works
Self-citationDAGMapper: Learning to Map by Discovering Lane Topology
Homayounfar, N., Liang, J., Ma, W.-C., Fan, J., Wu, X., & Urtasun, R. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00300 · Cited by 109 · see citing works
Self-citationDeep Multi-Sensor Lane Detection
Bai, M., Máttyus, G., Homayounfar, N., Wang, S., Lakshmikanth, S. K., & Urtasun, R. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594388 · Cited by 102 · see citing works
Self-citationSearch-Based Optimal Motion Planning for Automated Driving
Ajanović, Z., Lačević, B., Shyrokau, B. N., Stolz, M., & Horn, M. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593813 · Cited by 100 · see citing works
Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles
Sadat, A., Ren, M., Pokrovsky, A., Lin, Y.-C., Yumer, E., & Urtasun, R. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967615 · Cited by 87 · see citing works
Self-citationHierarchical Recurrent Attention Networks for Structured Online Maps
Homayounfar, N., Ma, W.-C., Lakshmikanth, S. K., & Urtasun, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00360 · Cited by 71 · see citing works
Self-citationDiscrete Residual Flow for Probabilistic Pedestrian Behavior PredictionPreprint
Jain, A., Casas, S., Liao, R., Xiong, Y., Feng, S., Segal, S., & Urtasun, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.08041 · Cited by 64 · see citing works
Self-citationDeep Imitative Models for Flexible Inference, Planning, and ControlPreprint
Rhinehart, N., McAllister, R., & Levine, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.06544 · Cited by 56 · see citing works
Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data
Casas, S., Gulino, C., Liao, R., & Urtasun, R. (2019) · arXiv · doi:10.48550/arxiv.1910.08233 · Cited by 44 · see citing works
Self-citationThe Importance of Prior Knowledge in Precise Multimodal Prediction
Casas, S., Gulino, C., Suo, S., & Urtasun, R. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341199 · Cited by 43 · see citing works
Self-citationLearning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning
Banzhaf, H., Sanzenbacher, P., Baumann, U. A., & Zöllner, J. M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2893975 · Cited by 31 · see citing works
StrObe: Streaming Object Detection from LiDAR PacketsPreprint
Frossard, D., Suo, S., Casas, S., Tu, J., Hu, R., & Urtasun, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.06425 · Cited by 15 · see citing works
Self-citationScene Compliant Trajectory Forecast With Agent-Centric Spatio-Temporal Grids
Ridel, D. A., Deo, N., Wolf, D. F., & Trivedi, M. M. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2974393 · Cited by 15 · see citing works
An Auto-tuning Framework for Autonomous VehiclesPreprint
Fan, H., Xia, Z., Liu, C., Chen, Y., & Kong, Q. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.04913 · Cited by 14 · see citing works
Ma, H., Wang, Y., Tang, L., Kodagoda, S., & Xiong, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02468 · Cited by 11 · see citing works
Flagged first, then by citations
4 of the 61 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [52][52] Yuxin Wu and Kaiming He. Group normalization. In Proceedings of the European conference on computer vision (ECCV) , pages 3-19, 2018. 11 -
Cited by
Sorted by citations
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 969 · see citing works
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 673 · see citing works
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 492 · see citing works
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 353 · see citing works
Atakishiyev, S., Salameh, M., Yao, H., & Goebel, R. G. (2024) · IEEE Access · doi:10.1109/access.2024.3431437 · Cited by 405 · see citing works
MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction
Varadarajan, B., Hefny, A., Srivastava, A., Refaat, K. S., Nayakanti, N., Cornman, A., Chen, K., Douillard, B., Lam, C. P., Anguelov, D., et al. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812107 · Cited by 338 · see citing works
Recent Advancements in End-to-End Autonomous Driving Using Deep Learning: A Survey
Chib, P. S., & Singh, P. P. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3318070 · Cited by 326 · see citing works
ST-P3: End-to-End Vision-Based Autonomous Driving via Spatial-Temporal Feature Learning
Hu, S., Chen, L., Wu, P., Li, H., Yan, J., & Tao, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19839-7_31 · Cited by 273 · see citing works
NEAT: Neural Attention Fields for End-to-End Autonomous Driving
Chitta, K., Prakash, A., & Geiger, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01550 · Cited by 229 · see citing works
Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges
Zablocki, É., Ben-Younes, H., Pérez, P., & Cord, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01657-x · Cited by 227 · see citing works
Chen, D., & Krähenbühl, P. (2022) · 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/cvpr52688.2022.01671 · Cited by 207 · see citing works
Diffusion-based Generation, Optimization, and Planning in 3D Scenes
Huang, S., Wang, Z., Li, P., Jia, B., Liu, T., Zhu, Y., Liang, W., & Zhu, S.-C. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01607 · Cited by 192 · see citing works
Drive Like a Human: Rethinking Autonomous Driving with Large Language Models
Fu, D., Li, X., Wen, L., Dou, M., Cai, P., Shi, B., & Qiao, Y. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00102 · Cited by 195 · see citing works
MTR++: Multi-Agent Motion Prediction With Symmetric Scene Modeling and Guided Intention Querying
Shi, S., Jiang, L., Dai, D., & Schiele, B. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3352811 · Cited by 189 · see citing works
MotionDiffuser: Controllable Multi-Agent Motion Prediction Using Diffusion
Jiang, C. M., Cornman, A., Park, C., Sapp, B., Zhou, Y., & Anguelov, D. (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/cvpr52729.2023.00930 · Cited by 172 · see citing works
Huang, Z., Liu, H., & Lv, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00361 · Cited by 162 · see citing works
Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior
Rempe, D., Philion, J., Guibas, L. J., Fidler, S., & Litany, O. (2022) · 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/cvpr52688.2022.01679 · Cited by 152 · see citing works
Huang, Z., Liu, H., Wu, J., & Lv, C. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3283542 · Cited by 136 · see citing works
NuPlan: A closed-loop ML-based planning benchmark for autonomous vehiclesPreprint
Caesar, H., Kabzan, J., Tan, K. S., Fong, W. K., Wolff, E. M., Lang, A., Fletcher, L., Beijbom, O., & Omari, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.11810 · Cited by 204 · see citing works
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving
Cui, A., Casas, S., Sadat, A., Liao, R., & Urtasun, R. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01580 · Cited by 123 · see citing works
Self-citationThink Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving
Jia, X., Wu, P., Chen, L., Xie, J., He, C., Yan, J., & Li, H. (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/cvpr52729.2023.02105 · Cited by 120 · see citing works
Structured Bird’s-Eye-View Traffic Scene Understanding from Onboard Images
Can, Y. B., Liniger, A., Paudel, D. P., & Gool, L. V. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01537 · Cited by 116 · see citing works
Learning to drive from a world on rails
Chen, D., Koltun, V., & Krähenbühl, P. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01530 · Cited by 114 · see citing works
Behavioral Intention Prediction in Driving Scenes: A Survey
Fang, J., Wang, F., Xue, J., & Chua, T. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3374342 · Cited by 119 · see citing works
KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients
Hanselmann, N., Renz, K., Chitta, K., Bhattacharyya, A., & Geiger, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19839-7_20 · Cited by 101 · see citing works
GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting
Cui, A., Casas, S., Wong, K., Suo, S., & Urtasun, R. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160984 · Cited by 92 · see citing works
Self-citationIs Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?
Li, Z., Yu, Z., Lan, S., Li, J., Kautz, J., Lü, T., & Álvarez, J. M. (2024) · 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/cvpr52733.2024.01408 · Cited by 88 · see citing works
LTP: Lane-based Trajectory Prediction for Autonomous Driving
Wang, J., Ye, T., Gu, Z., & Chen, J. (2022) · 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/cvpr52688.2022.01662 · Cited by 86 · see citing works
ADAPT: Action-aware Driving Caption Transformer
Jin, B., Liu, X., Zheng, Y., Li, P., Zhao, H., Zhang, T., Zheng, Y., Zhou, G., & Liu, J. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160326 · Cited by 87 · see citing works
Hidden Biases of End-to-End Driving Models
Jaeger, B., Chitta, K., & Geiger, A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00757 · Cited by 82 · see citing works
SafetyNet: Safe Planning for Real-World Self-Driving Vehicles Using Machine-Learned Policies
Vitelli, M., Chang, Y., Ye, Y., Ferreira, A., Wołczyk, M., Osiński, B., Niendorf, M., Grimmett, H., Huang, Q., Jain, A., et al. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811576 · Cited by 79 · see citing works
VLP: Vision Language Planning for Autonomous Driving
Pan, C., Yaman, B., Nesti, T., Mallik, A., Allievi, A., Velipasalar, S., & Ren, L. (2024) · 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/cvpr52733.2024.01398 · Cited by 83 · see citing works
Occupancy Flow Fields for Motion Forecasting in Autonomous Driving
Mahjourian, R., Kim, J., Chai, Y., Tan, M., Sapp, B., & Anguelov, D. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3151613 · Cited by 77 · see citing works
PARA-Drive: Parallelized Architecture for Real-Time Autonomous Driving
Weng, X., Ivanovic, B., Wang, Y., Wang, Y., & Pavone, M. (2024) · 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/cvpr52733.2024.01463 · Cited by 73 · see citing works
BEV-V2X: Cooperative Birds-Eye-View Fusion and Grid Occupancy Prediction via V2X-Based Data Sharing
Chang, C., Zhang, J., Zhang, K., Zhong, W., Peng, X., Li, S., & Li, L. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3293954 · Cited by 70 · see citing works
Liu, H., Huang, Z., Mo, X., & Lv, C. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3372625 · Cited by 73 · see citing works
SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation
Sun, W., Lin, X., Shi, Y., Zhang, C., Wu, H., & Zheng, S. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128800 · Cited by 85 · see citing works
Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking
Pang, Z., Li, J., Tokmakov, P., Chen, D., Zagoruyko, S., & Wang, Y.-X. (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/cvpr52729.2023.01719 · Cited by 66 · see citing works
Differentiable Raycasting for Self-Supervised Occupancy Forecasting
Khurana, T., Hu, P., Dave, A., Ziglar, J., Held, D., & Ramanan, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19839-7_21 · Cited by 60 · see citing works
The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review
Hagedorn, S., Hallgarten, M., Stoll, M., & Condurache, A. P. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3459071 · Cited by 51 · see citing works
End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps
Guo, K., Liu, W., & Pan, J. (2022) · 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/cvpr52688.2022.00228 · Cited by 50 · see citing works
Towards learning-based planning: The nuPlan benchmark for real-world autonomous driving
Karnchanachari, N., Geromichalos, D., Tan, K. S., Li, N., Eriksen, C., Yaghoubi, S., Mehdipour, N., Bernasconi, G., Fong, W. K., Guo, Y., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10610077 · Cited by 46 · see citing works
“In contrast, various studies consider multi-task learning (MTL) where they jointly train models to perform perception, prediction and planning simultaneously [62], [63], [58], [38].”
BackgroundMotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments
Wilson, J., Song, J., Fu, Y., Zhang, A., Capodieci, A., Jayakumar, P., Barton, K. L., & Ghaffari, M. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3188435 · Cited by 46 · see citing works
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving
Bronstein, E., Palatucci, M., Notz, D., White, B., Kuefler, A., Lu, Y., Paul, S., Nikdel, P., Mougin, P., Chen, H., et al. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981695 · Cited by 46 · see citing works
High-Definition Map Representation Techniques for Automated Vehicles
Soorchaei, B. E., Razzaghpour, M., Valiente, R., Raftari, A., & Fallah, Y. P. (2022) · Electronics · doi:10.3390/electronics11203374 · Cited by 47 · see citing works
From Prediction to Planning With Goal Conditioned Lane Graph Traversals
Hallgarten, M., Stoll, M., & Zell, A. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10421854 · Cited by 44 · see citing works
Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting
Khurana, T., Hu, P., Held, D., & Ramanan, D. (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/cvpr52729.2023.00114 · Cited by 40 · see citing works
Topology Preserving Local Road Network Estimation from Single Onboard Camera Image
Can, Y. B., Liniger, A., Paudel, D. P., & Gool, L. V. (2022) · 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/cvpr52688.2022.01675 · Cited by 40 · see citing works
Fang, S., Wang, Z., Zhong, Y., Ge, J., & Chen, S. (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/cvpr52729.2023.00138 · Cited by 38 · see citing works
Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review
Shi, Y., Jiang, K., Li, J., Qian, Z., Wen, J., Yang, M., Wang, K., & Yang, D. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3495045 · Cited by 38 · see citing works
PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird’s-Eye View
Li, P., Ding, S., Chen, X., Hanselmann, N., Cordts, M., & Gall, J. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/120 · Cited by 34 · see citing works
Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications
Ma, J., Chen, X., Huang, J., Xu, J., Luo, Z., Xu, J., Gu, W., Ai, R., & Wang, H. (2024) · 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/cvpr52733.2024.02030 · Cited by 34 · see citing works
Embodied Understanding of Driving Scenarios
Zhou, Y., Huang, L., Bu, Q., Zeng, J., Li, T., Qiu, H., Zhu, H., Guo, M., Qiao, Y., & Li, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73033-7_8 · Cited by 31 · see citing works
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving
Atakishiyev, S., Salameh, M., & Goebel, R. G. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3574738 · Cited by 34 · see citing works
Lethal autonomous weapons systems & artificial intelligence: Trends, challenges, and policies
Longpre, S., Storm, M., & Shah, R. (2022) · MIT Science Policy Review · doi:10.38105/spr.360apm5typ · Cited by 29 · see citing works
Lin, K., Li, Y., Chen, S., Li, D., & Wu, X. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3273857 · Cited by 27 · see citing works
Hybrid-Prediction Integrated Planning for Autonomous Driving
Liu, H., Huang, Z., Huang, W., Yang, H., Mo, X., & Lv, C. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3526936 · Cited by 26 · see citing works
Toward Collaborative Autonomous Driving: Simulation Platform and End-to-End System
Liu, G., Hu, Y., Xu, C., Mao, W., Ge, J., Huang, Z., Lu, Y., Xu, Y., Xia, J., Wang, Y., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3560327 · Cited by 25 · see citing works
Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving
Agro, B., Sykora, Q., Casas, S., & Urtasun, R. (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/cvpr52729.2023.00139 · Cited by 25 · see citing works
Self-citationEfficient Baselines for Motion Prediction in Autonomous Driving
Gómez-Huélamo, C., Conde, M. V., Barea, R., Ocaña, M., & Bergasa, L. M. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3331143 · Cited by 24 · see citing works
Prospective Role of Foundation Models in Advancing Autonomous Vehicles
Wu, J., Gao, B., Gao, J., Yu, J., Chu, H., Yu, Q., Gong, X., Chang, Y. M., Tseng, H. E., Chen, H., et al. (2024) · Research · doi:10.34133/research.0399 · Cited by 24 · see citing works
Distilling Multi-Modal Large Language Models for Autonomous Driving
Hegde, D., Yasarla, R., Cai, H., Han, S., Bhattacharyya, A., Mahajan, S., Liu, L., Garrepalli, R., Patel, V. M., & Porikli, F. (2025) · 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/cvpr52734.2025.02568 · Cited by 24 · see citing works
StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving
Kim, J., Mahjourian, R., Ettinger, S. M., Bansal, M., White, B., Sapp, B., & Anguelov, D. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811830 · Cited by 23 · see citing works
Mao, Z., Yang, L., & Qu, X. (2024) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2024.104606 · Cited by 24 · see citing works
Developments in 3-D Object Detection for Autonomous Driving: A Review
Wang, Y., Wang, S., Li, Y., & Liu, M. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3562284 · Cited by 22 · see citing works
LiDAR-as-Camera for End-to-End Driving
Tampuu, A., Aidla, R., van Gent, J. A., & Matiisen, T. (2023) · Sensors · doi:10.3390/s23052845 · Cited by 22 · see citing works
Circular Accessible Depth: A Robust Traversability Representation for UGV Navigation
Xie, S., Song, R., Zhao, Y., Huang, X., Li, Y., & Zhang, W. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3308780 · Cited by 21 · see citing works
EDA: Evolving and Distinct Anchors for Multimodal Motion Prediction
Lin, L., Lin, X., Lin, T., Huang, L., Xiong, R., & Wang, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i4.28130 · Cited by 20 · see citing works
Rosero, L. A., Gomes, I. P., da Silva, J. A. R., Przewodowski, A., Wolf, D. F., & Osório, F. S. (2024) · Sensors · doi:10.3390/s24072097 · Cited by 20 · see citing works
Towards Driving-Oriented Metric for Lane Detection Models
Sato, T., & Chen, Q. A. (2022) · 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/cvpr52688.2022.01664 · Cited by 19 · see citing works
On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving
Feng, K., Li, C., Ren, D., Yuan, Y., & Wang, G. (2024) · 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/cvpr52733.2024.01430 · Cited by 19 · see citing works
Tan, X., Wu, G., Li, Z., Liu, K., & Zhang, C. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3519766 · Cited by 18 · see citing works
ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning
Li, C., Ji, Z., Chen, Z., Qin, T., & Yang, M. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801763 · Cited by 18 · see citing works
JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes
Zhao, H., Zhang, J., Zhang, S., & Tao, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19839-7_41 · Cited by 18 · see citing works
LaRa: Latents and Rays for Multi-Camera Bird's-Eye-View Semantic SegmentationPreprint
Bartoccioni, F., Zablocki, É., Bursuc, A., Pérez, P., Cord, M., & Alahari, K. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.13294 · Cited by 17 · see citing works
DeepInteraction++: Multi-Modality Interaction for Autonomous Driving
Yang, Z., Song, N., Li, W., Zhu, X., Zhang, L., & Torr, P. H. S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3565194 · Cited by 16 · see citing works
Predictive Neural Motion Planner for Autonomous Driving Using Graph Networks
Mo, X., & Lv, C. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3234370 · Cited by 15 · see citing works
A Review of Trajectory Prediction Methods for the Vulnerable Road User
Schuetz, E., & Flohr, F. B. (2023) · Robotics · doi:10.3390/robotics13010001 · Cited by 15 · see citing works
MapPrior: Bird’s-Eye View Map Layout Estimation with Generative Models
Zhu, X., Zyrianov, V., Liu, Z., & Wang, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00756 · Cited by 15 · see citing works
OMU: A Probabilistic 3D Occupancy Mapping Accelerator for Real-time OctoMap at the Edge
Jia, T., Yang, E.-Y., Hsiao, Y.-S., Cruz, J., Brooks, D., Wei, G.-Y., & Reddi, V. J. (2022) · Design, Automation, and Test in Europe · doi:10.23919/date54114.2022.9774508 · Cited by 14 · see citing works
Encoding the Intrinsic Interaction Information for Vehicle Trajectory Prediction
Lian, J., Li, S., Yang, D., Zhang, J., & Li, L. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3288976 · Cited by 14 · see citing works
Occupancy Prediction-Guided Neural Planner for Autonomous Driving
Liu, H., Huang, Z., & Lv, C. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422055 · Cited by 14 · see citing works
UnO: Unsupervised Occupancy Fields for Perception and Forecasting
Agro, B., Sykora, Q., Casas, S., Gilles, T., & Urtasun, R. (2024) · 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/cvpr52733.2024.01373 · Cited by 14 · see citing works
Self-citationLiDAR-based 4D Occupancy Completion and Forecasting
Liu, X., Gong, M., Fang, Q., Xie, H., Li, Y., Zhao, H., & Feng, C. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801302 · Cited by 14 · see citing works
MPNP: Multi-Policy Neural Planner for Urban Driving
Cheng, J., Ren, X., Wang, S., & Liu, M. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9982111 · Cited by 13 · see citing works
Real-Time Spatial Trajectory Planning for Urban Environments Using Dynamic Optimization
Ruof, J., Mertens, M. B., Buchholz, M., & Dietmayer, K. (2023) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55152.2023.10186535 · Cited by 13 · see citing works
A Cost-Efficient Failure-Tolerant Scheme for Distributed DNN Training
Chen, M., Hua, Y., Bai, R., & Huang, J. (2023) · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD) · doi:10.1109/iccd58817.2023.00031 · Cited by 12 · see citing works
Network-Level Safety Metrics for Overall Traffic Safety Assessment: A Case Study
Chen, X., Wang, H., Razi, A., Russo, B. J., Pacheco, J. L., Roberts, J., Wishart, J. D., Head, L., & Granados, A. (2022) · IEEE Access · doi:10.1109/access.2022.3223046 · Cited by 14 · see citing works
TIP: Task-Informed Motion Prediction for Intelligent Vehicles
Huang, X., Rosman, G., Jasour, A. M. Z., McGill, S. G., Leonard, J. J., & Williams, B. C. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9982100 · Cited by 12 · see citing works
Roadside Sensor Systems for Vulnerable Road User Protection: A Review of Methods and Applications
Zhang, T. T., Cheng, L., Bang, T., Guo, L., Hajij, M., Cao, S., Harris, A., & Sartipi, M. (2025) · IEEE Access · doi:10.1109/access.2025.3558174 · Cited by 11 · see citing works
Safety-Oriented Pedestrian Occupancy Forecasting
Luo, K. Z., Casas, S., Liao, R., Yan, X., Xiong, Y., Zeng, W., & Urtasun, R. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636691 · Cited by 11 · see citing works
Self-citationMoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences
Li, Y., Qi, C. R., Zhou, Y., Liu, C., & Anguelov, D. (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/cvpr52729.2023.00900 · Cited by 11 · see citing works
Human-Centric Intelligent Driving: Collaborating with the Driver to Improve Safety
Balachandran, A., Chen, T. L. B., Goh, J. Y. M., McGill, S. G., Rosman, G., Stent, S., & Leonard, J. J. (2022) · Lecture notes in mobility · doi:10.1007/978-3-031-11112-9_8 · Cited by 10 · see citing works
InteractionNet: Joint Planning and Prediction for Autonomous Driving with Transformers
Fu, J., Shen, Y., Jian, Z., Chen, S., Xin, J., & Zheng, N. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342367 · Cited by 10 · see citing works
Motion Planning Using Feasible and Smooth Tree for Autonomous Driving
Chen, W., Chen, Y., Wang, S., Kong, F., Zhang, X., & Sun, H. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3343694 · Cited by 10 · see citing works
Learning by Erasing: Conditional Entropy Based Transferable Out-of-Distribution Detection
Xing, M., Feng, Z., Su, Y., & Oh, C. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i6.28444 · Cited by 10 · see citing works
OFMPNet: Deep end-to-end model for occupancy and flow prediction in urban environment
Murhij, Y., & Yudin, D. A. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127649 · Cited by 10 · see citing works
Zhang, J., Keung, J., Xiao, Y., Liao, Y., Li, Y., & Ma, X. (2024) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3394894 · Cited by 10 · see citing works
Paparusso, L., Melzi, S., & Braghin, F. (2023) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2023.104329 · Cited by 10 · see citing works
Multi-Granular Transformer for Motion Prediction with LiDAR
Gan, Y., Xiao, H., Zhao, Y., Zhang, E., Huang, Z., Ye, X., & Ge, L. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610704 · Cited by 10 · see citing works
- 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 › Engineering › Aerospace Engineering › 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
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.
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 ›
Are you an author, or do you see a problem with this page? Request a review
