发表机构
Karlsruhe Institute of Technology (KIT); University of Liechtenstein; Halmstad University（卡尔斯鲁厄理工学院; 列支敦士登大学; 哈尔姆斯塔德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对信号交叉口驾驶员行为，提出物理约束决策条件自回归Transformer两阶段模型，预测停车/通过决策与纵向轨迹，优于基线并实现低误差。
AI 中文摘要
信号交叉口的闯红灯和急刹车是交通事故的主要原因。本文分析并预测了交通信号灯切换期间人类驾驶员的决策和纵向轨迹行为。我们收集了一个多样化的真实世界数据集，包含在不同速度和距离条件下的449次接近运行。车辆运动使用RTK校正的GNSS记录，精度达到厘米级，同时监测驾驶员心率和多级舒适度评分。空间和时间校准确保了车辆状态与信号时序之间的精确对齐。统计分析确定所需减速度是停车-通过决策的主要单一预测因子，对峰值减速度的异方差高斯建模揭示了从人类停车行为中得出的五个经验舒适度范围。基于这一见解，我们提出了一个两阶段建模框架。第一阶段预测二元操作决策，第二阶段使用具有物理约束的决策条件自回归Transformer生成纵向加速度轨迹，包括目标状态条件和加加速度限制。所提出的架构优于基线方法，实现了0.49m/s²的加速度平均绝对误差和0.62m的距离平均绝对误差。它还能从单个黄灯起始快照中估计人类驾驶员未来的停车舒适度水平。定性结果展示了逼真的人类制动行为。数据集和源代码公开可用。
英文摘要
Red-light violations and harsh braking at signalized intersections are major contributors to traffic accidents. This paper analyzes and predicts human driver decision-making and longitudinal trajectory behavior during traffic light signal transitions. We collected a diverse real-world dataset comprising 449 approach runs under varying speed and distance conditions. Vehicle motion was recorded using RTK-corrected GNSS with centimeter-level accuracy, and driver heart rate and multi-level comfort ratings were monitored. Spatial and temporal calibration ensured precise alignment between vehicle state and signal timing. Statistical analysis identifies required deceleration as the dominant single predictor of the stop-go decision, and heteroscedastic Gaussian modeling of peak deceleration reveals five empirical comfort ranges derived from human stopping behavior. Based on this insight, we propose a two-stage modeling framework. Stage 1 predicts the binary maneuver decision, and Stage 2 generates the longitudinal acceleration trajectory using a decision-conditioned autoregressive Transformer with physics constraints, including target-state conditioning and jerk limits. The proposed architecture outperforms baseline methods and achieves 0.49m/s^2 acceleration MAE and 0.62m distance MAE. It also estimates the future stopping-comfort level of the human driver from a single yellow-onset snapshot. Qualitative results demonstrate realistic human-like braking behavior. The dataset and source code are publicly available.
CommentsAccepted for publication at the 2026 IEEE International Conference on Intelligent Transportation Systems (ITSC 2026)
