Deep Learning-Based Vehicle Control Strategist for Autonomous Vehicles


At a glance

In this project, we propose to build a mid-level control algorithm called “Vehicle Control Strategist” (VCS) through the adoption of deep reinforcement learning. The VCS receives inputs from motion planning, vehicle states, and environment perception, then it utilizes a neural network (NN) to map the inputs to the control commands for steering, accelerating/braking, and traction distributing to the low-level controllers. The NN is designed and trained to achieve the desired performance based on a reward function that seeks to maximize driving performance, safety, and ride comfort. The NN model implicitly handles the coupling of longitudinal dynamics and lateral dynamics of the vehicle to derive integrated control policy to optimize overall performance.

principal investigatorsresearchersthemes
Ching-Yao Chan
Masayoshi Tomizuka
 Deep Learning, Vehicle Control, Automated Vehicle