Autonomous driving system using proximal policy optimization in deep reinforcement learning

Abstract

Autonomous driving is one solution that can minimize and even prevent accidents. In autonomous driving, the vehicle must know the surrounding environment and move under the provisions and situations. We build an autonomous driving system using proximal policy optimization (PPO) in deep reinforcement learning, with PPO acting as an instinct for the agent to choose an action. The instinct will be updated continuously until the agent reaches the destination from the initial point. We use five sensory inputs for the agent to accelerate, turn the steer, hit the brakes, avoid the walls, detect the initial point, and reach the destination point. We evaluated our proposed autonomous driving system in a simulation environment with several branching tracks, reflecting a real-world setting. For our driving simulation purpose in this research, we use the Unity3D engine to construct the dataset (in the form of a road track) and the agent model (in the form of a car). Our experimental results firmly indicate our agent can successfully control a vehicle to navigate to the destination point.