The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
End-To-End Interpretable Neural Motion Planner
Open accessAbstract
In this paper, we propose a neural motion planner for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a holistic model that takes as input raw LIDAR data and a HD map and produces interpretable intermediate representations in the form of 3D detections and their future trajectories, as well as a cost volume defining the goodness of each position that the self-driving car can take within the planning horizon. We then sample a set of diverse physically possible trajectories and choose the one with the minimum learned cost. Importantly, our cost volume is able to naturally capture multi-modality. We demonstrate the effectiveness of our approach in real-world driving data captured in several cities in North America. Our experiments show that the learned cost volume can generate safer planning than all the baselines.
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(2016) · Cited by 31,590
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084
CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718
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
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles
Paden, B., Cap, M., Yong, S. Z., Yershov, D., & Frazzoli, E. (2016) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2016.2578706 · Cited by 2,585
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.312 · Cited by 1,876
Maximum Entropy Inverse Reinforcement Learning
Ziebart, B. D., Maas, A. L., Bagnell, J. A., & Dey, A. K. (2018) · doi:10.1184/r1/6555512 · Cited by 1,869
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Luo, W., Li, Y., Urtasun, R., & Zemel, R. (2017) · arXiv · doi:10.48550/arxiv.1701.04128 · Cited by 1,575
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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) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01712 · Cited by 931
Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., van Keulen, M., & Seifert, C. (2023) · ACM Computing Surveys · doi:10.1145/3583558 · Cited by 590
CoverNet: Multimodal Behavior Prediction Using Trajectory Sets
Phan-Minh, T., Grigore, E. C., Boulton, F. A., Beijbom, O., & Wolff, E. M. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01408 · Cited by 438
Metadata
- Authors
- Zeng, W., Luo, W., Suo, S., Sadat, A., Yang, B., Casas, S., & Urtasun, R.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 8652-8661
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2968008415
- Cited by
- 398
- References
- 35
- FWCI
- 46.42
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zeng, W., Luo, W., Suo, S., Sadat, A., Yang, B., Casas, S., & Urtasun, R. (2019). End-To-End Interpretable Neural Motion Planner. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00886
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