Transport Canada, Ottawa, Canada.
PMG Technologies Inc, Blainville, Canada.
Traffic Inj Prev. 2023;24(sup1):S62-S67. doi: 10.1080/15389588.2022.2159762.
Advanced Driving Assistance Systems (ADAS) have the potential to reduce occurrences and severity of collisions with Vulnerable Road Users (VRUs). However, the nearly infinite number of possible VRU visual appearances (e.g., size, clothing, accessories) represent a technical challenge as systems need to correctly detect and identify VRUs to take adequate mitigation measures. The aim of this study was to determine, through track testing, which parameters affect systems' capabilities in detecting pedestrians.
The standardized articulated adult male pedestrian (EPTa) and seven-year-old articulated child pedestrian (EPTc) targets were used as the control group. Evaluations on the track followed the Euro NCAP AEB-VRU test protocols, and derivatives thereof. An iterative test approach was used to benchmark the detection capabilities of systems with variations in target configuration and environmental conditions against the control group (baseline condition). Over 1,000 track tests using 24 configurations and 13 vehicles (model years 2019-2021) were conducted. The environmental conditions included nighttime and snow-covered roads. Pedestrians were dressed in winter clothing and/or equipped with accessories, including a hat, jackets of different colors, backpack, umbrella, and a scooter. Other scenarios involved parked vehicles as obstructions or using multiple pedestrian targets (to simulate a parent crossing the road with child or a crowd waiting at an intersection) to challenge the vehicles with realistic urban-like scenarios.
This study illustrates how the variation of parameters outside the baseline condition can affect a vehicles' safety performance. Weather conditions and urban-like scenarios on the test track affected some systems more than others. The validity of these findings is however limited by the small vehicle sample size and number of tests performed per scenario.
In Canada, vehicles are exposed to less-than-ideal road conditions and a wide range of pedestrian profiles. The vehicles tested demonstrated various levels of performance and capabilities when mitigating collisions with pedestrians. The research illustrates the safety risks associated with weather and types of VRUs.
高级驾驶辅助系统(ADAS)有可能减少与弱势道路使用者(VRU)发生碰撞的次数和严重程度。然而,VRU 的外观可能有无数种变化(例如,大小、衣着、配饰等),这对系统来说是一个技术挑战,因为系统需要正确检测和识别 VRU,以便采取适当的缓解措施。本研究的目的是通过轨道测试确定哪些参数会影响系统检测行人的能力。
标准化的铰接式成年男性行人(EPTa)和 7 岁的铰接式儿童行人(EPTc)目标被用作对照组。轨道评估遵循 Euro NCAP AEB-VRU 测试协议及其衍生协议。采用迭代测试方法,针对系统在目标配置和环境条件变化下的检测能力与对照组(基线条件)进行基准测试。使用 24 种配置和 13 辆车(2019-2021 年车型)进行了超过 1000 次轨道测试。环境条件包括夜间和积雪道路。行人穿着冬季服装和/或配备配饰,包括帽子、不同颜色的夹克、背包、雨伞和滑板车。其他场景涉及停放的车辆作为障碍物,或使用多个行人目标(模拟父母带孩子过马路或一群人在路口等待),以具有现实城市场景的方式挑战车辆。
本研究说明了基线条件以外的参数变化如何影响车辆的安全性能。测试轨道上的天气条件和类似城市的场景对一些系统的影响大于其他系统。然而,由于车辆样本量小和每个场景进行的测试次数有限,这些发现的有效性受到限制。
在加拿大,车辆面临不理想的道路条件和广泛的行人特征。测试的车辆在减轻与行人碰撞的风险方面表现出不同的性能和能力。该研究说明了与天气和 VRU 类型相关的安全风险。