Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data
- ENS Paris-Saclay, Paris-Saclay University,Department of Computer Science
- UC Berkeley,Department of Mechanical Engineering
- Vanderbilt University,Department of Civil and Environmental Engineering
- Temple University,Department of Mathematics
- Department of Electrical Engineering and Computer Science at UC Berkeley
Autonomous vehicle-based traffic smoothing con- trollers are often not transferred to real-world use due to challenges in calibrating many-agent traffic simulators. We show a pipeline to sidestep such calibration issues by collecting trajectory data and learning controllers directly from trajectory data that are then deployed zero-shot onto the highway. We construct a dataset of 772.3 kilometers of recorded drives on the I-24. We then construct a simple simulator using the recorded drives as the lead vehicle in front of a simulated platoon consisting of one autonomous vehicle and five human followers. Using policy-gradient methods with an asymmetric critic to learn the controller, we show that we are able to improve average MPG by 11% in simulation on congested trajectories. We deploy this controller to a mixed platoon of 4 autonomous Toyota RAV-4’s and 7 human drivers in a validation experiment and demonstrate that the expected time-gap of the controller is maintained in the real world test. Finally, we release the driving dataset [1], the simulator, and the trained controller at https://github.com/nathanlct/trajectory-training-icra.
- Research Organization:
- Vanderbilt Univ., Nashville, TN (United States); University of California at Berkeley; Temple Univ., Philadelphia, PA (United States); Paris-Saclay University
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO)
- Contributing Organization:
- CIRCLES Consortium
- DOE Contract Number:
- EE0008872
- OSTI ID:
- 1969511
- Journal Information:
- 2022 International Conference on Robotics and Automation (ICRA), Conference: 2022 IEEE International Conference on Robotics and Automation. May 23-27, 2022. Philadelphia, PA, USA.; Related Information: Nice, M., Lichtle, N., Gumm, G., Roman, M., Vinitsky, E., Elmadani, S., and Bunting, M., Bhadani, R., Gunter,G., Ku- mar, M., and McQuade, S., Denaro, C., Delorenzo, R., Pic- coli, B., Work, D. Bayen, A. Lee, J., Sprinkle, J. and Sei- bold, B., “The I-24 trajectory dataset,” doi.org/10.5281/zenodo.6366761
- Country of Publication:
- United States
- Language:
- English
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