Performance Pursuit Behavior of Autonomous Vehicles: An Area-Based Driving Strategy for Autonomous Vehicles Considering Multi-Objective Optimization at Signalized Intersections

Abstract

Autonomous driving technology enables precise motion control and creates substantial opportunities for improving the operation of signalized intersections. However, the performance trade-offs among autonomous vehicles at signalized intersections, and their impacts on the operation process, will directly affect the further application of autonomous driving in the intelligent transportation system. Addressing this research focus, this paper proposes a multi-objective driving strategy for autonomous vehicles based on the scene characteristics of signalized intersections. Firstly, the intersection and its adjacent control area are treated as an integrated decision region, and an autonomous vehicle driving performance model at signalized intersections is established to evaluate economy, comfort, and efficiency performance. Secondly, a multi-stage trajectory generation method combining phase division, candidate trajectory generation, and real-time trajectory adjustment is further developed to adapt the ego vehicle to traffic conditions while maintaining safe and smooth motion. Thirdly, a multi-objective optimization problem is formulated to generate optimal trajectories for each autonomous vehicle within the region. Finally, weight sensitivity analysis, application adaptability analysis, and an analysis of system-level key factors and system-level impacts are conducted to explore the strategy optimization potential. The case studies reveal that the proposed strategy achieves improvements of 50.0%, 33.3%, and 21.6% in comfort, economy, and efficiency, respectively, compared with the common strategy. In future research, more specific and complex practical factors will be incorporated into the proposed strategy. The strategy helps to reveal the performance-oriented behavior of autonomous vehicles at signalized intersections and provides methodological support for the wider application of autonomous driving in intelligent transportation systems.