Deep reinforcement learning for autonomous vehicles: lane keep and overtaking scenarios with collision avoidance

Numerous accidents and fatalities occur every year across the world as a result of the reckless driving of drivers and the ever-increasing number of vehicles on the road. Due to these factors, autonomous cars have attracted enormous attention as a potentially game-changing technology to address a number of persistent problems in the transportation industry. Autonomous vehicles need to be modeled as intelligent agents with the capacity to observe, and perceive the complex and dynamic environment on the road, and decide an action with the highest priority to the lives of people in every scenarios. The proposed deep deterministic policy gradient-based sequential decision algorithm models the autonomous vehicle as a learning agent and trains it to drive on a lane, overtake a static and a moving vehicle, and avoid collisions with obstacles on the front and right side. The proposed work is simulated using a TORC simulator and has shown the expected performance under the above-said scenarios.