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From Sim to Real: A Pipeline for Training and Deploying Traffic Smoothing Cruise Controllers

Journal Article · · IEEE Transactions on Robotics
 [1];  [2];  [3];  [4];  [3];  [2];  [5];  [6];  [3];  [2];  [3];  [2]
  1. University of California, Berkeley, CA (United States); University of California Berkeley
  2. University of California, Berkeley, CA (United States)
  3. Vanderbilt University, Nashville, TN (United States)
  4. University of Alabama in Huntsville, AL (United States)
  5. Rutgers University-Camden, NJ (United States)
  6. Temple University, Philadelphia, PA (United States)
+ Show Author Affiliations

Designing and validating controllers for connected and automated vehicles to enhance traffic flow presents significant challenges, from the complexity of replicating real-world stop-and-go traffic dynamics in simulation, to the intricacies involved in transitioning from simulation to actual deployment. In this work, we present a full pipeline from data collection to controller deployment. Specifically, we collect 772 km of driving data from the I-24 in Tennessee, and use it to build a one-lane simulator, placing simulated vehicles behind real-world trajectories. Using policy-gradient methods with an asymmetric critic, we improve fuel efficiency by over 10% when simulating congested scenarios. Our comprehensive approach includes reinforcement learning for controller training, software verification, hardware validation and setup, and navigating various sim-to-real challenges. Furthermore, we analyze the controller's behavior and wave-smoothing properties, and deploy it on four Toyota Rav4’s in a real-world validation experiment on the I-24. Lastly, we release the driving dataset, the simulator and the trained controller, to enable future benchmarking and controller design.

Research Organization:
University of California, Berkeley, CA (United States); Vanderbilt University, Nashville, TN (United States); Rutgers University-Camden, NJ (United States); Temple University, Philadelphia, PA (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO); National Science Foundation (NSF)
Grant/Contract Number:
EE0008872
OSTI ID:
2545922
Journal Information:
IEEE Transactions on Robotics, Journal Name: IEEE Transactions on Robotics Vol. 40; ISSN 1552-3098
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

42 ENGINEERING
Autonomous vehicles
Cruise control
Energy and environment-aware automation
Hardware
Intelligent transporation systems
Intelligent transportation systems
Navigation
Reinforcement learning (RL)
Road transportation
Smoothing methods
Training
Trajectory