Integrated Decision-Making and Planning Method for Autonomous Vehicles Based on an Improved Driving Risk Field

The driving risk field model offers a feasible approach for assessing driving risks and planning safe trajectory in complex traffic scenarios. However, the conventional risk field fails to account for the vehicle size and acceleration, results in the same trajectories are generated when facing different vehicle types and unable to make safe decisions in emergency situations. Therefore, this paper firstly introduces the acceleration and vehicle size of surrounding vehicles for improving the driving risk model. Then, an integrated decision-making and planning model is proposed based on the combination of the novelty risk field and model predictive control (MPC), in which driving risk and vehicle dynamics constraints are taken into consideration. Finally, the multiple driving scenarios are designed and analyzed for validate the proposed model. The results demonstrate that the proposed decision-making and planning method exhibits superior performance in addressing discrepancies related to vehicle acceleration and geometric. Besides, the improved driving risk field model is able to effectively model the various driving behavior in complex traffic scenarios, and has superior performance for reflecting the realistic driving risk distribution.