Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai, 201804, China; College of Transportation Engineering, Tongji University, Shanghai, 201804, China.
Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai, 201804, China; College of Transportation Engineering, Tongji University, Shanghai, 201804, China.
Accid Anal Prev. 2019 Mar;124:127-137. doi: 10.1016/j.aap.2019.01.006. Epub 2019 Jan 10.
Cut-in maneuvers, when vehicles change lane and move closely in front of a vehicle in the adjacent lane, are very common but adversely affect roadway capacity and traffic safety. Yet little research has comprehensively explored cut-in behavior, particularly in China, which has a challenging driving environment and is often used for connected and autonomous vehicle testing. This study developed an extraction algorithm to retrieve 5608 cut-in events from the Shanghai Naturalistic Driving Study. The data were used to identify cut-in characteristics, including motivation, turn signal usage, duration, urgency, and impact. Results showed that almost half of drivers did not use a turn signal when cutting in, and that cut-ins had a shorter time to collision (TTC) than other lane changing. A lognormal distribution was found to produce the best fit for cut-in duration, which varied from 0.7 s to 12.4 s. As characteristics were found to vary by roadway type and motivation, multilevel mixed-effects linear models were developed to examine the influencing factors of cut-in gap acceptance. Acceptance of lead and lag gaps was significantly affected by environmental variables, vehicle type, and kinematic parameters, which has important implications for microsimulation, as does the large variance in duration that makes specifying duration essential when setting scenarios. Improvement in safety education is warranted by the high degrees of risk and aggression shown by TTC and turn signal usage; but the ability of drivers, who needed to yield to the cut-in, to predict danger and adopt safe, suitable, and timely strategies suggests that advanced driver assistance systems and connected and autonomous vehicles can learn similar responses.
车辆变道并切入相邻车道前车前方近距离行驶的切入动作非常常见，但会对道路通行能力和交通安全产生不利影响。然而，很少有研究全面探讨切入行为，特别是在中国，中国的驾驶环境具有挑战性，并且经常用于联网和自动驾驶车辆测试。本研究开发了一种提取算法，从上海自然驾驶研究中提取了 5608 个切入事件。该数据用于识别切入特征，包括动机、转向灯使用、持续时间、紧迫性和影响。结果表明，几乎一半的驾驶员在切入时没有使用转向灯，并且切入的碰撞时间（TTC）比其他变道更短。发现对数正态分布最适合切入持续时间，切入持续时间从 0.7s 到 12.4s 不等。由于特征因道路类型和动机而异，因此开发了多层次混合效应线性模型来检验切入间隙接受的影响因素。对领先和滞后间隙的接受受到环境变量、车辆类型和运动学参数的显著影响，这对微观模拟具有重要意义，因为持续时间的方差很大，在设置场景时指定持续时间至关重要。TTC 和转向灯使用所显示的高风险和攻击性需要加强安全教育；但是，需要让切入的驾驶员能够预测危险并采取安全、合适和及时的策略，这表明先进的驾驶员辅助系统和联网和自动驾驶车辆可以学习类似的反应。
