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.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving
Shalev-Shwartz, S., Shammah, S., & Shashua, A. (2016) · arXiv · doi:10.48550/arxiv.1610.03295 · Cited by 509
Imitating driver behavior with generative adversarial networks
Kuefler, A., Morton, J., Wheeler, T., & Kochenderfer, M. (2017) · 2017 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2017.7995721 · Cited by 423
Virtual to Real Reinforcement Learning for Autonomous Driving
Pan, X., You, Y., Wang, Z., & Lu, C. (2017) · Procedings of the British Machine Vision Conference 2017 · doi:10.5244/c.31.11 · Cited by 326 · cited 2×
“Pan et al. (2017) also describes achieving transfer of an agent trained in simulation to the real world using a learned intermediate scene labeling representation.”
BackgroundCIRL: Controllable Imitative Reinforcement Learning for Vision-Based Self-driving
Liang, X., Wang, T., Yang, L., & Xing, E. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_36 · Cited by 285
Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving
Djuric, N., Radosavljevic, V., Cui, H., Nguyen, T., Chou, F.-C., Lin, T.-H., Singh, N., & Schneider, J. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv45572.2020.9093332 · Cited by 225
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
Bojarski, M., Yeres, P., Choromanska, A., Choromański, K., Firner, B., Jackel, L. D., & Müller, U. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.07911 · Cited by 223
HDNET: Exploiting HD Maps for 3D Object Detection
Yang, B., Liang, M. X., & Urtasun, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.11704 · Cited by 212
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars
Tian, Y., Pei, K., Jana, S., & Ray, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.08559 · Cited by 208
Traffic light mapping and detection
Fairfield, N., & Urmson, C. (2011) · 2011 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980164 · Cited by 193
Driving Policy Transfer via Modularity and Abstraction
Müller, M., Dosovitskiy, A., Ghanem, B., & Koltun, V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.09364 · Cited by 166
Convolution neural network-based lane change intention prediction of surrounding vehicles for ACC
Lee, D., Kwon, Y. P., McMains, S., & Hedrick, J. K. (2017) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317874 · Cited by 131
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
Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations
Laskey, M., Chuck, C., Lee, J., Mahler, J., Krishnan, S., Jamieson, K., Dragan, A. D., & Goldberg, K. Y. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989046 · Cited by 63
Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks.Preprint
Djuric, N., Radosavljević, V., Cui, H., Nguyen, T., Chou, F., Lin, T.-H., & Schneider, J. (2018) · arXiv (Cornell University) · Cited by 49
Learning Driving Models with a Surround-View Camera System and a Route Planner.Preprint
Hecker, S., Dai, D., & Gool, L. V. (2018) · arXiv (Cornell University) · Cited by 12
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
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
Prakash, A., Chitta, K., & Geiger, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00700 · Cited by 584
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
- 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
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
