Stochastic Time-Optimal Trajectory Planning for Connected and Automated Vehicles in Mixed-Traffic Merging Scenario

Authors: Viet-Anh Le, Behdad Chalaki, Filippos N. Tzortzoglou, and Andreas A. Malikopoulos

Summary

This is the complementary website for the paper. In this paper, we develop a stochastic time-optimal trajectory planning framework for coordinating multiple connected and automated vehicles (CAVs) in mixed-traffic merging scenarios. We propose a data-driven model for efficiently learning the driving behavior of human drivers online, which combines Newell's car-following model with Bayesian linear regression. Using the prediction model and uncertainty quantification, a stochastic time-optimal control problem with chance constraints is formulated to find robust trajectories for CAVs. We also integrate a replanning mechanism that determines when deriving new trajectories for CAVs is needed, based on the accuracy of the Bayesian linear regression predictions. Finally, we demonstrate the performance of our proposed framework through simulations using a realistic traffic simulator.