Level-$K$ Reasoning, Deep Reinforcement Learning, and Monte Carlo Decision Process for Fast and Safe Automated Lane Change and Speed Management

This paper presents a decision process model for real-time automated lane change and speed management in highway traffic. The presented algorithm is developed based on level- $K$ game theory to model and predict the interaction between the vehicles. Using deep reinforcement learning, this algorithm encodes and memorizes the past experiences that are recurrently used to reduce the computations and speed up motion planning. Also, we use Monte Carlo Tree Search (MCTS) as an effective tool that is employed nowadays for fast planning in complex and dynamic game environments. This development leverages the computation power efficiently and showcases promising outcomes for maneuver planning and predicting the environment's dynamics. In the absence of traffic connectivity that may be due to either passenger's choice of privacy or the vehicle's lack of technology, this development can be extended and employed in fully-automated vehicles for real-world and practical applications.