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Self-driving cars and autonomous driving research has been receivingconsiderable attention as major promising prospects in modern artificialintelligence applications. According to the evolution of advanced driverassistance system (ADAS), the design of self-driving vehicle and autonomousdriving systems becomes complicated and safety-critical. In general, theintelligent system simultaneously and efficiently activates ADAS functions.Therefore, it is essential to consider reliable ADAS function coordination tocontrol the driving system, safely. In order to deal with this issue, thispaper proposes a randomized adversarial imitation learning (RAIL) algorithm.The RAIL is a novel derivative-free imitation learning method for autonomousdriving with various ADAS functions coordination; and thus it imitates theoperation of decision maker that controls autonomous driving with various ADASfunctions. The proposed method is able to train the decision maker that dealswith the LIDAR data and controls the autonomous driving in multi-lane complexhighway environments. The simulation-based evaluation verifies that theproposed method achieves desired performance.

Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles