Automated Lane Changing Through Learning-Based Control: An Experimental Study

This paper presents a learning-based methodology for developing an optimal lane-changing control policy for a Remote Controlled (RC) car using real-time sensor data. The RC car is equipped with sensors including GPS, IMU devices, and a camera integrated in an Nvidia Jetson AGX Xavier board. By a novel Adaptive Dynamic Programming (ADP) algorithm, our RC car is capable of learning the optimal lane-changing strategies based on the real-time processed measurement from the sensors. The experimental outcomes show that our learning-based control algorithm can be effectively implemented, adapt to parameter changes, and complete the lane changing tasks in a short learning time with satisfactory performance.