Yao Xinpeng, Lyu Nengchao, Liu Mengfei
Shandong Hi-Speed Group Innovation Research Institute, Jinan 250014, China.
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China.
Sensors (Basel). 2025 May 20;25(10):3213. doi: 10.3390/s25103213.
Advanced driving assistance systems (ADASs) provide rich data on vehicles and their surroundings, enabling early detection and warning of driving risks. This study proposes a short-term risk prediction method based on in-vehicle perception data, aiming to support real-time risk identification in ADAS environments. A variable sliding window approach is employed to determine the optimal prediction window lead length and duration. The method incorporates Monte Carlo simulation for threshold calibration, Boruta-based feature selection, and multiple machine learning models, including the light gradient-boosting machine (LGBM), with performance interpretation via SHAP analysis. Validation is conducted using data from 90 real-world driving sessions. Results show that the optimal prediction lead time and window length are 1.6 s and 1.2 s, respectively, with LGBM achieving the best predictive performance. Risk prediction effectiveness is enhanced when integrating information across the human-vehicle-road environment system. Key features influencing prediction include vehicle speed, accelerator operation, braking deceleration, and the reciprocal of time to collision (TTCi). The proposed approach provides an effective solution for short-term risk prediction and offers algorithmic support for future ADAS applications.
先进驾驶辅助系统（ADAS）可提供有关车辆及其周围环境的丰富数据，从而实现对驾驶风险的早期检测和预警。本研究提出了一种基于车载感知数据的短期风险预测方法，旨在支持ADAS环境下的实时风险识别。采用可变滑动窗口方法来确定最佳预测窗口提前长度和持续时间。该方法结合了用于阈值校准的蒙特卡罗模拟、基于Boruta的特征选择以及包括轻梯度提升机（LGBM）在内的多种机器学习模型，并通过SHAP分析进行性能解释。使用来自90次实际驾驶会话的数据进行验证。结果表明，最佳预测提前时间和窗口长度分别为1.6秒和1.2秒，LGBM实现了最佳预测性能。整合人车路环境系统中的信息可提高风险预测有效性。影响预测的关键特征包括车速、油门操作、制动减速度以及碰撞时间倒数（TTCi）。所提出的方法为短期风险预测提供了有效解决方案，并为未来ADAS应用提供了算法支持。
