基于碰撞时间和穿越区域上下文的城市交叉口安全导向行人轨迹预测
Safety-oriented pedestrian trajectory prediction at urban intersections using time-to-collision and crossing-zone context
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中文总结 AI 辅助
本研究提出一种安全导向的行人轨迹预测框架，结合碰撞时间与穿越区域上下文，利用加权损失和池化LSTM，在inD数据集上显著降低预测误差及超限次数。
中文摘要 AI 辅助
准确的行人轨迹预测对于主动式道路安全应用至关重要，尤其是在城市交叉口，行人的运动受到车辆交互和穿越情境的共同影响。本研究提出了一种安全导向的轨迹预测框架，该框架将行人运动历史与碰撞时间（TTC）信息及穿越区域指示器相结合。利用inD（交叉口无人机）数据集中一个城市交叉口的自然轨迹数据，评估了多种神经网络架构，观测时长为1.6秒，预测时长为2.4秒。一个池化长短期记忆（LSTM）网络分别编码TTC历史和穿越区域上下文，然后将其与行人位置整合。除了传统的平均位移误差（ADE）和最终位移误差（FDE）外，还使用超过研究定义的1米容差的误差频率和幅度来评估预测性能。此外，引入了一种加权损失函数，以在训练中更加重视坐标上的较大误差。将该损失应用于仅位置LSTM后，ADE从0.210米降至0.190米，FDE从0.550米降至0.503米，同时ADE和FDE超限次数分别减少了34.8%和19.8%。最终结合TTC和穿越区域信息的池化配置实现了ADE为0.184米、FDE为0.491米，相对于安全导向的仅位置LSTM，ADE和FDE超限次数进一步减少了33.5%和6.3%。结果表明，安全导向的训练以及交互和上下文信息的结构化整合可以减少较大的轨迹预测误差，但仍需在更多行人、地点和数据集上进行更广泛的验证。
英文摘要
Accurate pedestrian trajectory prediction is important for proactive road-safety applications, particularly at urban intersections where pedestrian motion is shaped by both vehicle interactions and crossing context. This study presents a safety-oriented trajectory-prediction framework that combines pedestrian motion history with Time-to-Collision (TTC) information and crossing-zone indicators. Using naturalistic trajectories from one urban intersection in the inD (Intersection Drone) dataset, several neural architectures were evaluated with 1.6 s observation and 2.4 s prediction horizons. A pooled Long Short-Term Memory (LSTM) separately encodes TTC histories and crossing-zone context before integrating them with pedestrian positions. In addition to conventional Average Displacement Error (ADE) and Final Displacement Error (FDE), prediction performance was assessed using the frequency and magnitude of errors exceeding a study-defined 1 m tolerance. A weighted loss was also introduced to place greater training emphasis on large coordinate-wise errors. Applying this loss to the position-only LSTM reduced ADE from 0.210 to 0.190 m and FDE from 0.550 to 0.503 m, while reducing ADE and FDE exceedance counts by 34.8% and 19.8%, respectively. The final pooled configuration incorporating TTC and crossing-zone information achieved an ADE of 0.184 m and FDE of 0.491 m, with further reductions of 33.5% and 6.3% in ADE and FDE exceedance counts relative to the safety-oriented position-only LSTM. The results indicate that safety-oriented training and structured integration of interaction and contextual information can reduce large trajectory-prediction errors, although broader validation across pedestrians, sites, and datasets is required.
发表机构
- Afeka Academic College of Engineering（阿费卡工程学院）
机构由 AI 辅助整理，请以论文原文为准。
