Chen Rong, Kusano Kristofer D, Gabler Hampton C
a Virginia Tech Center for Injury Biomechanics , Virginia Tech, Blacksburg, Virginia.
Traffic Inj Prev. 2015;16 Suppl 2:S176-81. doi: 10.1080/15389588.2015.1057281.
Lane changes with the intention to overtake the vehicle in front are especially challenging scenarios for forward collision warning (FCW) designs. These overtaking maneuvers can occur at high relative vehicle speeds and often involve no brake and/or turn signal application. Therefore, overtaking presents the potential of erroneously triggering the FCW. A better understanding of driver behavior during lane change events can improve designs of this human-machine interface and increase driver acceptance of FCW. The objective of this study was to aid FCW design by characterizing driver behavior during lane change events using naturalistic driving study data.
The analysis was based on data from the 100-Car Naturalistic Driving Study, collected by the Virginia Tech Transportation Institute. The 100-Car study contains approximately 1.2 million vehicle miles of driving and 43,000 h of data collected from 108 primary drivers. In order to identify overtaking maneuvers from a large sample of driving data, an algorithm to automatically identify overtaking events was developed. The lead vehicle and minimum time to collision (TTC) at the start of lane change events was identified using radar processing techniques developed in a previous study. The lane change identification algorithm was validated against video analysis, which manually identified 1,425 lane change events from approximately 126 full trips.
Forty-five drivers with valid time series data were selected from the 100-Car study. From the sample of drivers, our algorithm identified 326,238 lane change events. A total of 90,639 lane change events were found to involve a closing lead vehicle. Lane change events were evenly distributed between left side and right side lane changes. The characterization of lane change frequency and minimum TTC was divided into 10 mph speed bins for vehicle travel speeds between 10 and 90 mph. For all lane change events with a closing lead vehicle, the results showed that drivers change lanes most frequently in the 40-50 mph speed range. Minimum TTC was found to increase with travel speed. The variability in minimum TTC between drivers also increased with travel speed.
This study developed and validated an algorithm to detect lane change events in the 100-Car Naturalistic Driving Study and characterized lane change events in the database. The characterization of driver behavior in lane change events showed that driver lane change frequency and minimum TTC vary with travel speed. The characterization of overtaking maneuvers from this study will aid in improving the overall effectiveness of FCW systems by providing active safety system designers with further understanding of driver action in overtaking maneuvers, thereby increasing system warning accuracy, reducing erroneous warnings, and improving driver acceptance.
意图超越前方车辆的变道操作对于前碰撞预警(FCW)设计而言是极具挑战性的场景。这些超车动作可能在较高的车辆相对速度下发生,并且通常不涉及制动和/或转向灯的使用。因此,超车存在错误触发FCW的可能性。更好地理解变道事件中的驾驶员行为能够改进这种人机界面的设计,并提高驾驶员对FCW的接受度。本研究的目的是通过使用自然驾驶研究数据来刻画变道事件中的驾驶员行为,从而辅助FCW设计。
分析基于弗吉尼亚理工大学交通研究所收集的100辆汽车自然驾驶研究的数据。100辆汽车研究包含约120万车辆行驶英里数以及从108名主要驾驶员收集的43000小时数据。为了从大量驾驶数据样本中识别超车动作,开发了一种自动识别超车事件的算法。使用先前研究中开发的雷达处理技术来识别变道事件开始时的前车和最小碰撞时间(TTC)。变道识别算法通过视频分析进行了验证,视频分析从大约126次完整行程中手动识别出1425次变道事件。
从100辆汽车研究中选取了45名具有有效时间序列数据的驾驶员。在驾驶员样本中,我们的算法识别出326238次变道事件。总共发现90639次变道事件涉及一辆逐渐靠近的前车。变道事件在左侧变道和右侧变道之间均匀分布。变道频率和最小TTC的刻画针对车辆行驶速度在10至90英里/小时之间划分为10英里/小时的速度区间。对于所有涉及逐渐靠近前车的变道事件,结果表明驾驶员在40 - 50英里/小时的速度范围内变道最为频繁。发现最小TTC随行驶速度增加。驾驶员之间最小TTC的变异性也随行驶速度增加。
本研究开发并验证了一种算法,用于在100辆汽车自然驾驶研究中检测变道事件,并刻画了数据库中的变道事件。变道事件中驾驶员行为的刻画表明,驾驶员变道频率和最小TTC随行驶速度而变化。本研究对超车动作的刻画将有助于提高FCW系统的整体有效性,为主动安全系统设计师提供对超车动作中驾驶员行为的进一步理解,从而提高系统警告准确性、减少错误警告并提高驾驶员接受度。