Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
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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 [62]
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Cites
Generative adversarial imitation learning
Ho, J., & Ermon, S. (2016) · arXiv · doi:10.48550/arxiv.1606.03476 · Cited by 1,942 · 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-citationKendall, 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 · see citing works
Detect to Track and Track to Detect
Feichtenhofer, C., Pinz, A., & Zisserman, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.330 · Cited by 595 · see citing works
Neural Map: Structured Memory for Deep Reinforcement Learning
Parisotto, E., & Salakhutdinov, R. (2017) · arXiv · doi:10.48550/arxiv.1702.08360 · Cited by 178 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [1]1. Liang, M., Yang, B., Chen, Y., Hu, R., Urtasun, R.: Multi-task multi-sensor fusion for 3d object detection. In: CVPR. (2019)
- [2]2. Luo, W., Yang, B., Urtasun, R.: Fast and furious: Real time end-to-end 3d detec- tion, tracking and motion forecasting with a single convolutional net. In: CVPR. (2018)
- [3]3. Casas, S., Luo, W., Urtasun, R.: Intentnet: Learning to predict intention from raw sensor data. In: CoRL. (2018)
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MP3: A Unified Model to Map, Perceive, Predict and Plan
Casas, S., Sadat, A., & Urtasun, R. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01417 · Cited by 228 · see citing works
Self-citationExplainability 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
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].”
BackgroundInterpretable Goal-based Prediction and Planning for Autonomous Driving
Albrecht, S. V., Brewitt, C., Wilhelm, J., Gyevnar, B., Eiras, F., Dobre, M., & Ramamoorthy, S. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9560849 · Cited by 12 · see citing works
- Authors
- Sadat, A., Casas, S., Ren, M., Wu, X., Dhawan, P., & Urtasun, R.
- Affiliations
- University of Toronto (CA)
- Corresponding author
- Sadat, Abbas
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 414-430
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3048966129
- Cited by
- 183
- References
- 5
- Citations by year
- 2026: 12 · 2025: 36 · 2024: 45 · 2023: 37 · 2022: 20 · 2021: 31 · 2020: 2
- FWCI
- 18.54
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (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 › Robotic Path Planning Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- autonomous vehicles, interpretable representations, end-to-end learning, trajectory prediction, closed-loop simulation
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