Risk assessment of autonomous vehicle using Markov decision process

Autonomous decision making of intelligent vehicle is one of the most critical and challenging module due to the fact that traffic of real world is uncertain, complex, continuous and vehicles interact with each other. In this thesis, a decision making based on reinforcement learning algorithms is proposed to represent ego vehicle behaviors interacting with the stochastic behaviors of the environmental vehicles in highway traffic. The presented solver algorithms are formulated as Markov Decision Process (MDP) for autonomous vehicle problems. Proposed algorithms are implemented in a simulation environment so that they are tested and analyzed with different scenarios. Then, efficiency of different implemented algorithms are compared based on speci fed criteria. The simulation results of tested scenarios show that ego car is capable of lane change and accelerate or decelerate in order to perform safe driving without any collision with other cars which have uncertain behavior in highway.