S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for
  Autonomous Vehicles
Xiao Wang Ke Tang Xingyuan Dai Jintao Xu Quancheng Du Rui Ai Yuxiao Wang Weihao Gu
In public roads, autonomous vehicles (AVs) face the challenge of frequent
interactions with human-driven vehicles (HDVs), which render uncertain driving
behavior due to varying social characteristics among humans. To effectively
assess the risks prevailing in the vicinity of AVs in social interactive
traffic scenarios and achieve safe autonomous driving, this article proposes a
social-suitable and safety-sensitive trajectory planning (S4TP) framework.
Specifically, S4TP integrates the Social-Aware Trajectory Prediction (SATP) and
Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to
effectively encode the driving scene and incorporates an AV's planned
trajectory during the prediction decoding process. SADRF assesses the expected
surrounding risk degrees during AVs-HDVs interactions, each with different
social characteristics, visualized as two-dimensional heat maps centered on the
AV. SADRF models the driving intentions of the surrounding HDVs and predicts
trajectories based on the representation of vehicular interactions. S4TP
employs an optimization-based approach for motion planning, utilizing the
predicted HDVs'trajectories as input. With the integration of SADRF, S4TP
executes real-time online optimization of the planned trajectory of AV within
lowrisk regions, thus improving the safety and the interpretability of the
planned trajectory. We have conducted comprehensive tests of the proposed
method using the SMARTS simulator. Experimental results in complex social
scenarios, such as unprotected left turn intersections, merging, cruising, and
overtaking, validate the superiority of our proposed S4TP in terms of safety
and rationality. S4TP achieves a pass rate of 100% across all scenarios,
surpassing the current state-of-the-art methods Fanta of 98.25% and
Predictive-Decision of 94.75%.