Abstract

This study aims at modelling unassisted drivers’ speed at the yellow onset to enhance Connected and Autonomous Vehicles applications at signalised intersections and maximise drivers’ comfort. For this purpose, a total of 2442 real-life vehicle trajectories were analysed to extract driver behavioural measures (i.e. speed, acceleration, and distance to intersection) at different times before the yellow onset. These behavioural measures were used to integrate drivers’ perceptual ability into modelling drivers’ speed at the yellow onset. To develop these models, three machine learning techniques; namely, linear regression, Support Vector Machine, and Neural Networks have been adopted. The best model was a neural network model and was selected based on the goodness-of-fit of the test dataset which has an R-squared value of 0.97. The results indicate that the speed at the yellow onset can be estimated based on behavioural measures while accounting for drivers’ perceptual ability. Also, the model can contribute to a V2I application by assisting the driver in a partially autonomous vehicle to avoid trapping in the dilemma zone and stop safely at signalised intersections. Also, the model can be used to recommend a comfortable riding speed, from a rider’s perspective to a fully autonomous vehicle.

摘要

本研究旨在模拟黄灯开始时的无人驾驶速度，以增强联网和自动驾驶汽车在信号交叉口的应用，并最大限度地提高驾驶员的舒适度。为此，共分析了 2442 条现实生活中的车辆轨迹，以提取黄色发作前不同时间的驾驶员行为测量值（即速度、加速度和到交叉路口的距离）。这些行为测量用于将驾驶员的感知能力整合到黄发时的驾驶员速度建模中。为了开发这些模型，三种机器学习技术；即采用了线性回归、支持向量机和神经网络。最佳模型是神经网络模型，并根据 R 平方值为 0.97 的测试数据集的拟合优度进行选择。结果表明，在考虑驾驶员感知能力的同时，可以根据行为测量来估计黄色发作时的速度。此外，该模型还可以帮助部分自动驾驶车辆中的驾驶员避免陷入困境并在信号交叉口安全停车，从而为 V2I 应用做出贡献。此外，该模型可用于推荐舒适的骑行速度，从骑手的角度到全自动驾驶汽车。