Virtual Event
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Lane Changing of Autonomous Vehicles in Mixed Traffic Environments: A Reinforcement Learning Approach
Virtual Event
Lane Changing of Autonomous Vehicles in Mixed Traffic Environments: A Reinforcement Learning Approach
The emergence of connected and autonomous vehicles (CAVs) presents increased opportunities to mitigate traffic congestion, improve safety and reduce accidents. Professor Zhong-Ping Jiang, and researchers Leilei Cui and Sayan Chakraborty are applying innovative reinforcement learning control methods to one challenging aspect of CAV control: lane changing in mixed traffic. The team takes a novel approach by reducing the trajectory planning and tracking problem down to the minimization of a cost function that depends on a target way-point in the lane a CAV is targeting.