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%.

翻译：在公共道路上，自动驾驶车辆（AV）面临与人类驾驶车辆（HDV）频繁交互的挑战，由于人类具有不同的社会特征，导致其驾驶行为存在不确定性。为有效评估社会交互交通场景中AV周围的风险态势并实现安全自动驾驶，本文提出了一种面向社交适宜性与安全敏感性的轨迹规划（S4TP）框架。具体而言，S4TP集成了社会感知轨迹预测（SATP）与社会感知驾驶风险场（SADRF）模块。SATP利用Transformer有效编码驾驶场景，并在预测解码阶段引入AV的规划轨迹。SADRF评估具有不同社会特征的AV-HDV交互过程中的预期周围风险程度，并以AV为中心可视化为二维热力图。SADRF基于车辆交互表征建模周围HDV的驾驶意图并预测轨迹。S4TP采用基于优化的运动规划方法，将预测的HDV轨迹作为输入。通过集成SADRF，S4TP在低风险区域内对AV规划轨迹进行实时在线优化，从而提升了规划轨迹的安全性与可解释性。我们利用SMARTS模拟器对所提方法进行了全面测试。在无保护左转交叉口、合流、巡航及超车等复杂社会场景中的实验结果验证了S4TP在安全性与合理性方面的优越性。S4TP在所有场景中实现了100%的通关率，超越了当前最先进方法Fanta（98.25%）与Predictive-Decision（94.75%）。