Optimal reinforcement learning and probabilistic-risk-based path planning and following of autonomous vehicles with obstacle avoidance

In this paper, a novel algorithm is proposed for the motion planning and path following automated cars with the incorporation of a collision avoidance strategy. This approach is aligned with an optimal reinforcement learning (RL) coupled with a new risk assessment approach. For this purpose, a probabilistic function-based collision avoidance strategy is developed, and the proposed RL approach learns the probability distributions of the adjacent and leading vehicles. Subsequently, the nonlinear model predictive control (NMPC) algorithm approximates the optimal steering input and the required yaw moment to follow the safest and shortest path through the optimal RL-based probabilistic risk function framework. Additionally, it is attempted to maintain the travel speed for the ego vehicle stable such that the ride comfort is also offered for the vehicle occupants. For this purpose, the steering system dynamics are also incorporated to provide a thorough understanding of the vehicle dynamics characteristic. Different driving scenarios are employed in the present paper to evaluate the proposed algorithm's effectiveness.

Automated summary

In this paper, a novel algorithm combining optimal reinforcement learning and risk assessment is proposed for motion planning and path following of automated cars. The algorithm utilizes a probabilistic function-based collision avoidance strategy and nonlinear model predictive control (NMPC) to approximate optimal steering input and ensure stable travel speed for the ego vehicle. The proposed algorithm is evaluated using different driving scenarios.