  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.
