Current advanced driver assistance systems (ADASs) do not consider drivers’ preferences of evasive behavior types and risk levels under rear-end near-crash scenarios, which undermines driver satisfaction, trust, and use of ADASs. Additionally, spatio-temporal interactions between vehicles are not fully involved in current evasive behavior prediction models, and the influence of evasive behavior is ignored while predicting collision risk. To address these issues, this study aims to propose an ADAS with better driver satisfaction under rear-end near-crash scenarios by establishing a spatio-temporal graph transformer-based prediction framework of evasive behavior and collision risk. A total of 822 evasive events are extracted from 108,000 real vehicle trajectories on highways, and variables from three sources (i.e., road environment features, evading vehicle features, and interactive behavior features) are used to construct rear-end near-crash scenario knowledge graphs (RNSKGs). By utilizing RNSKGs embedding and multi-head self-attention mechanism, spatio-temporal graph transformer networks can effectively capture the spatio-temporal interactions between vehicles. The results show that the prediction accuracy of evasive behavior (i.e., braking-only or braking and steering) and collision risk (lower, medium, or higher risk) is 96.34% and 92.12%, respectively, superior to other commonly-used methods. After including the selected evasive behavior in predicting collision risk, the overall accuracy increases by 10.91%. Then, an autonomous evasive takeover system (AET) based on the prediction framework is developed, and its effectiveness and satisfaction are verified by driving simulation experiments. According to the self-reported data of participants, the safety, comfort, usability, and acceptability of AET proposed in this study all significantly outperform existing autonomous takeover systems (i.e., autonomous emergency braking and autonomous emergency steering). The findings of this study might contribute to the optimization of ADASs, the enhancement of mutual understanding between ADASs and human drivers, and the improvement of active driving safety.

目前的高级驾驶员辅助系统（ADAS）没有考虑驾驶员在追尾险情情况下对规避行为类型和风险水平的偏好，这损害了驾驶员对 ADAS 的满意度、信任度和使用。此外，当前的避让行为预测模型并未完全涉及车辆之间的时空相互作用，在预测碰撞风险时忽略了避让行为的影响。为了解决这些问题，本研究旨在通过建立基于时空图变压器的避让行为和碰撞风险预测框架，提出一种在追尾接近碰撞场景下具有更好驾驶员满意度的 ADAS。从高速公路上108,000条真实车辆轨迹中总共提取822个避让事件，并利用道路环境特征、避让车辆特征和交互行为特征三个来源的变量构建追尾近撞场景知识图谱（RNSKG）。通过利用 RNSKG 嵌入和多头自注意力机制，时空图转换器网络可以有效捕获车辆之间的时空交互。结果表明，避让行为（即仅制动或制动和转向）和碰撞风险（低、中、高风险）的预测精度分别为 96.34% 和 92.12%，优于其他常用方法。将选定的规避行为纳入预测碰撞风险后，整体准确率提高了 10.91%。然后，开发了基于预测框架的自主规避接管系统（AET），并通过驾驶仿真实验验证了其有效性和满意度。根据参与者自我报告的数据，本研究提出的AET的安全性、舒适性、可用性和可接受性均显着优于现有的自动接管系统（即自动紧急制动和自动紧急转向）。这项研究的结果可能有助于ADAS的优化、增强ADAS与人类驾驶员之间的相互理解以及提高主动驾驶安全性。