One of the fundamental challenges for autonomous vehicles in complex dynamic scenarios is planning human-like trajectories in a socially acceptable manner. Existing methods, focused solely on self-interest and implicitly modeling a singular driving style, prove inadequate for the deployment of autonomous vehicles in mixed traffic environments while maintaining social acceptability. This paper presents the Socially-Compatible Motion Planning (SCMP) framework, which dynamically captures the spatiotemporal interaction characteristics of traffic participants for various driving tasks and adaptively adjusts the driving style of autonomous vehicles. Guided by driving style, the motion planning module considers vehicle physical constraints, safety constraints and comfort constraints, thereby generating human-like trajectories that incorporate multiple driving styles. The proposed method was rigorously validated and evaluated using the real-world dataset nuPlan, where it demonstrated exceptional performance in various driving scenarios and tasks.
Socially-Compatible Motion Planning of Autonomous Vehicles with Verification on Real Human Data
2024-09-24
4590187 byte
Conference paper
Electronic Resource
English
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