Abstract
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%.
Abstract (translated)
在公共道路上，自动驾驶车辆（AVs）面临着与人类驾驶车辆（HDVs）频繁互动的挑战，这使得不确定性的驾驶行为变得不可预测，因为人类之间的社会特点有所不同。为了有效评估周围AV在社交交互交通场景中的风险，并实现安全的自动驾驶，本文提出了一个社交合适和安全敏感的轨迹规划（S4TP）框架。具体来说，S4TP整合了社交意识轨迹预测（SATP）和社会意识驾驶风险场（SADRF）模块。SATP利用Transformer有效地编码驾驶场景，并预测AV在预测解码过程中计划的轨迹。SADRF评估了AV与HDV互动时的预期周围风险程度，每个具有不同社会特点，以二维热力图为中心绘制。SADRF建模了周围HDV的驾驶意图，并预测轨迹，基于车辆交互的表示。S4TP采用基于优化的方法进行运动规划，利用预测HDV的轨迹作为输入。通过整合SADRF，S4TP在低风险区域执行AV计划轨迹的实时在线优化，从而提高安全和计划轨迹的可解释性。我们使用SMARTS仿真器对所提出的方案进行了全面的测试。复杂的社会场景，如未保护左转路口、汇入、巡航和超车等，证实了我们的S4TP在安全和理性方面的优越性。S4TP在所有场景下的通过率为100%，超过了现有技术的98.25%和预测决策的94.75%。
URL
https://arxiv.org/abs/2404.11946
