Gabaire Mahdi, Ghomi Haniyeh, Hussein Mohamed
Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4L7.
Accid Anal Prev. 2025 Mar;211:107898. doi: 10.1016/j.aap.2024.107898. Epub 2024 Dec 18.
With the imminent widespread integration of Autonomous Vehicles (AVs) into our traffic ecosystem, understanding the factors that impact their safety is a vital research area. To that end, this study assessed the impact of a wide range of factors on the frequency of AV-road user conflicts. The study utilized the Woven prediction and validation dataset, which contains over 1000 h of data collected from the onboard sensors of 20 AVs in California. Two Copula-based models were developed to investigate the contributing factors to total and severe AV conflicts in road segments (model M1) and intersections (model M2). For road segments, results indicated that road characteristics (direction, number of lanes, road length, speed limit, the presence of a dividing median) and road infrastructure (presence of bus stops, presence of cycle lanes, and presence of on-street parking) have a significant impact on the hourly conflict rates. Regarding the rate of severe conflicts, road user volume, road characteristics (direction, road type, access point density, the presence of a dividing median), and the presence of cycle lanes were identified as the most influential factors. For intersections, the road user volume and the presence of a physical median were found to be positively associated with the hourly conflict rates, while road user volume, intersection characteristics (posted speed limit, lack of traffic control signals, presence of pedestrian crossing, presence of cycle lane, presence of a dividing median, and truck percentage), and the dominant land use at the intersection area were the most impactful variables on the frequency of severe conflicts.
随着自动驾驶汽车（AVs）即将广泛融入我们的交通生态系统，了解影响其安全性的因素是一个至关重要的研究领域。为此，本研究评估了多种因素对自动驾驶汽车与道路使用者冲突频率的影响。该研究使用了Woven预测和验证数据集，其中包含从加利福尼亚州20辆自动驾驶汽车的车载传感器收集的超过1000小时的数据。开发了两个基于Copula的模型，以研究路段（模型M1）和十字路口（模型M2）中导致自动驾驶汽车总体冲突和严重冲突的因素。对于路段，结果表明道路特征（方向、车道数量、道路长度、限速、中央分隔带的存在）和道路基础设施（公交站的存在、自行车道的存在和路边停车位的存在）对每小时冲突率有显著影响。关于严重冲突率，道路使用者数量、道路特征（方向、道路类型、接入点密度、中央分隔带的存在）和自行车道的存在被确定为最有影响力的因素。对于十字路口，发现道路使用者数量和实体中央分隔带的存在与每小时冲突率呈正相关，而道路使用者数量、十字路口特征（公布的限速、缺乏交通控制信号、人行横道的存在、自行车道的存在、中央分隔带的存在和卡车百分比）以及十字路口区域的主要土地用途是对严重冲突频率影响最大的变量。
