MotoSafety：结合学习到的时间重要性的边缘AI，用于时间压力下两轮车碰撞风险评估
MotoSafety: Edge-AI with Learned Temporal Importance for Two-Wheeler Collision Risk Assessment Under Time Pressure
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中文总结 AI 辅助
本文针对时间压力下两轮车碰撞风险评估问题，提出基于学习到的时间重要性的边缘AI架构MotoSafety，在多数据集验证下性能优于基线模型，适合低成本边缘部署，还具备跨领域迁移能力。
中文摘要 AI 辅助
在中低收入国家，机动两轮车（PTW）骑行者面临严峻的安全挑战，但针对时间压力（TP）等认知压力因素如何影响碰撞风险的研究十分有限。为解决这一缺口，本文构建了一个大规模数据集，包含51名参与者在无、低、高三种时间压力条件下完成的153次模拟器骑行所产生的129000余条带标签的多变量时间序列序列，涵盖车辆动力学、控制输入、接近度及行为违规等64项特征。基于该数据集，本文提出MotoSafety，一种基于学习到的时间重要性原理的新型边缘AI架构。MotoSafety的准确率达94.97%，ROC曲线下面积（ROC AUC）达99.33%，优于TimesNet、LLM4TS在内的10种基线模型；其预测的均方误差（MSE）为0.039、平均绝对误差（MAE）为0.094，误差仅为Time-LLM和iTransformer的1/4.4。该模型仅含115万参数，推理延迟为0.135毫秒，适合在低成本CPU硬件上进行边缘部署。将真实时间压力作为归纳偏置可使准确率从94.09%提升至94.97%，而预测时间压力可达到94.82%；仅使用21项IMU+GPS特征即可实现93.91%的准确率，具备实际部署潜力。除PTW安全领域外，该架构在人类活动（准确率97.66%）和临床（准确率99.65%）领域也展现出更优的迁移能力。这款轻量型框架推进了PTW碰撞风险评估技术，为智能交通系统的安全体系方法提供支撑。
英文摘要
Powered two-wheeler riders face critical safety challenges in low- and middle-income countries, yet limited studies exist on how cognitive stressors such as Time Pressure influence collision risk. We address this gap by introducing a comprehensive dataset consisting of over 129,000 labeled multivariate time-series feature windows, gathered across 153 simulator rides from 51 participants under No, Low, and High TP scenarios. Across each sequence, we capture 64 distinct attributes covering vehicle motion, rider control actions, spatial proximity, and rule compliance indicators. Using this dataset, we introduce MotoSafety, a new edge-AI framework built on the Learned Temporal Importance (LTI) concept. MotoSafety achieves 94.97% accuracy and 99.33% ROC AUC, outperforming ten baselines, including TimesNet and LLM4TS, and achieves 0.039 MSE and 0.094 MAE for forecasting (4.4x lower error than Time-LLM and iTransformer). With only 1.15M parameters and 0.135 ms latency, it is suitable for edge deployment on low-cost CPU hardware. Using ground truth TP as an inductive bias improves accuracy from 94.09% to 94.97%, while predicted TP achieves 94.82%. Using only 21 IMU+GPS features, it achieves 93.91% accuracy, indicating practical deployment. Beyond PTW safety, the architecture shows better transferability to human activity (97.66%) and clinical (99.65%) domains. This lightweight framework advances PTW collision risk assessment, supporting the Safe System Approach for Intelligent Transportation Systems.
发表机构
- Indian Institute of Technology Indore（印度理工学院印多尔分校）
- Texas State University（德克萨斯州立大学）
机构由 AI 辅助整理，请以论文原文为准。
