The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
Open accessAbstract
Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle.We find that standard behavior cloning is insufficient for handling complex driving scenarios, even when we leverage a perception system for preprocessing the input and a controller for executing the output on the car: 30 million examples are still not enough.We propose exposing the learner to synthesized data in the form of perturbations to the expert's driving, which creates interesting situations such as collisions and/or going off the road.Rather than purely imitating all data, we augment the imitation loss with additional losses that penalize undesirable events and encourage progress -the perturbations then provide an important signal for these losses and lead to robustness of the learned model.We show that the ChauffeurNet model can handle complex situations in simulation, and present ablation experiments that emphasize the importance of each of our proposed changes and show that the model is responding to the appropriate causal factors.Finally, we demonstrate the model driving a real car at our test facility.† Work done while at Google Brain & Waymo. 1 Please see https://sites.google.
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Cites
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
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
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
ALVINN, an autonomous land vehicle in a neural network
Pomerleau, D. (2018) · doi:10.1184/r1/6603146 · Cited by 1,338
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
Pei, K., Cao, Y., Yang, J., & Jana, S. (2017) · Proceedings of the 26th Symposium on Operating Systems Principles · doi:10.1145/3132747.3132785 · Cited by 1,217
DeepTest: automated testing of deep-neural-network-driven autonomous cars
Tian, Y., Pei, K., Jana, S., & Ray, B. (2018) · Proceedings of the 40th International Conference on Software Engineering · doi:10.1145/3180155.3180220 · Cited by 1,217
End-to-End Learning of Driving Models from Large-Scale Video Datasets
Xu, H., Gao, Y., Yu, F., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.376 · Cited by 884
Luo, 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
Cited by
Deep reinforcement learning for autonomous driving: A survey
Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Sallab, A. A. A., Yogamani, S., & Perez, P. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3054625 · Cited by 2,342
Center-based 3D Object Detection and Tracking
Yin, T., Zhou, X., & Krahenbuhl, P. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01161 · Cited by 2,082
Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2972974 · Cited by 1,425
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
Synthetic Data for Deep Learning
Nikolenko, S. I. (2021) · Springer Optimization and Its Applications · doi:10.1007/978-3-030-75178-4 · Cited by 423
Metadata
- Authors
- Bansal, M., Krizhevsky, A., & Ogale, A.
- Venue
- Robotics: Science and Systems XV
- Published
- 2019-06-22
- OpenAlex
- W2905173465
- Cited by
- 731
- References
- 25
- FWCI
- 100.74
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Bansal, M., Krizhevsky, A., & Ogale, A. (2019). ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst. Robotics: Science and Systems XV. https://doi.org/10.15607/rss.2019.xv.031
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