Teaching Autonomous Vehicles to Express Interaction Intent during Unprotected Left Turns: A Human-Driving-Prior-Based Trajectory Planning Approach

Incorporating Autonomous Vehicles (AVs) into existing transportation systems necessitates examining their coexistence with Human-driven Vehicles (HVs) in mixed traffic environments. Central to this coexistence is the AVs' ability to emulate human-like interaction intentions within traffic scenarios. We introduce a novel framework for planning unprotected left-turn trajectories for AVs, designed to mirror human driving behaviors and effectively communicate social intentions. This framework consists of three phases: trajectory generation, evaluation, and selection.In the trajectory generation phase, we utilize real human-driving trajectory data to establish constraints for a predicted trajectory space, creating candidate motion trajectories that reflect intent. The evaluation phase incorporates maximum entropy inverse reinforcement learning (ME-IRL) to gauge human trajectory preferences, considering aspects like traffic efficiency, driving comfort, and interactive safety. During the selection phase, a Boltzmann distribution-based approach is employed to assign rewards and probabilities to the candidate trajectories, promoting human-like decision-making. We validate our framework using an authentic trajectory dataset and conduct a comparative analysis with various baseline methods. Our results, derived from simulator tests and human-in-the-loop driving experiments, affirm our framework's superiority in mimicking human-like driving, expressing intent, and computational efficiency. For additional information of this research, please visit https://shorturl.at/jqu35.