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

Nathan Lichtlé, Eugene Vinitsky, Matthew Nice, Rahul Bhadani, Matthew R. Bunting, Fangyu Wu, Benedetto Piccoli, Benjamin Seibold, Daniel B. Work, Jonathan W. Lee, Jonathan Sprinkle, Alexandre M. Bayen

Abstract: 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. Finally, we release the driving dataset (Nice et al., 2021), the simulator and the trained controller (Lichtlé et al., 2022), to enable future benchmarking and controller design.