Toward Safe and Personalized Autonomous Driving: Decision-Making and Motion Control With DPF and CDT Techniques
Chao Huang, Chen Lv, Peng Hang, Yang Xing
Abstract
In this article, a novel approach of decision-making and motion control is designed for realizing safe and personalized driving of autonomous vehicles. A new lane-change intention generation model and a new lane-change decision-making algorithm are proposed. The feature of the proposed decision-making module is that the interactions between the ego vehicle and other surrounding vehicles are represented by the dynamic potential field (DPF) and embedded in the gap acceptance model to ensure the safety and personalization during driving. In addition, an integrated trajectory planning and tracking control algorithm, which incorporates the artificial potential field and constrained Delaunay triangulation (CDT) into the model predictive control framework, is developed. The newly developed integrated controller allows efficient execution of the expected motion. The proposed approach is tested under different driving conditions and further compared with an existing baseline method. The results show that the proposed approach is able to make safe and personalized decisions, and execute motion control more efficiently for automated driving under dynamic situations, validating its feasibility and effectiveness.