Savino Giovanni, Rizzi Matteo, Brown Julie, Piantini Simone, Meredith Lauren, Albanese Bianca, Pierini Marco, Fitzharris Michael
a University of Florence , Florence , Italy.
Traffic Inj Prev. 2014;15 Suppl 1:S165-72. doi: 10.1080/15389588.2014.926009.
In 2006, Motorcycle Autonomous Emergency Braking (MAEB) was developed by a European Consortium (Powered Two Wheeler Integrated Safety, PISa) as a crash severity countermeasure for riders. This system can detect an obstacle through sensors in the front of the motorcycle and brakes automatically to achieve a 0.3 g deceleration if the collision is inevitable and the rider does not react. However, if the rider does brake, full braking force is applied automatically. Previous research into the potential benefits of MAEB has shown encouraging results. However, this was based on MAEB triggering algorithms designed for motorcycle crashes involving impacts with fixed objects and rear-end crashes. To estimate the full potential benefit of MAEB, there is a need to understand the full spectrum of motorcycle crashes and further develop triggering algorithms that apply to a wider spectrum of crash scenarios.
In-depth crash data from 3 different countries were used: 80 hospital admittance cases collected during 2012-2013 within a 3-h driving range of Sydney, Australia, 40 crashes with Injury Severity Score (ISS)>15 collected in the metropolitan area of Florence, Italy, during 2009-2012, and 92 fatal crashes that occurred in Sweden during 2008-2009. In the first step, the potential applicability of MAEB among the crashes was assessed using a decision tree method. To achieve this, a new triggering algorithm for MAEB was developed to address crossing scenarios as well as crashes involving stationary objects. In the second step, the potential benefit of MAEB across the applicable crashes was examined by using numerical computer simulations. Each crash was reconstructed twice-once with and once without MAEB deployed.
The principal finding is that using the new triggering algorithm, MAEB is seen to apply to a broad range of multivehicle motorcycle crashes. Crash mitigation was achieved through reductions in impact speed of up to approximately 10 percent, depending on the crash scenario and the initial vehicle pre-impact speeds.
This research is the first attempt to evaluate MAEB with simulations on a broad range of crash scenarios using in-depth data. The results give further insights into the feasibility of MAEB in different speed ranges. It is clear then that MAEB is a promising technology that warrants further attention by researchers, manufacturers, and regulators.
2006年，一个欧洲联盟（两轮机动车综合安全，PISa）开发了摩托车自动紧急制动（MAEB）系统，作为一种降低骑手碰撞严重程度的对策。该系统可通过摩托车前部的传感器检测障碍物，若碰撞不可避免且骑手未做出反应，系统会自动制动，实现0.3g的减速度。然而，如果骑手已经制动，系统会自动施加全力制动。此前关于MAEB潜在益处的研究已取得令人鼓舞的成果。但这些研究基于为涉及与固定物体碰撞及追尾碰撞的摩托车事故设计的MAEB触发算法。为评估MAEB的全部潜在益处，有必要了解摩托车事故的全貌，并进一步开发适用于更广泛事故场景的触发算法。
使用来自3个不同国家的深入事故数据：2012 - 2013年在澳大利亚悉尼3小时驾驶范围内收集的80例医院收治病例，2009 - 2012年在意大利佛罗伦萨市区收集的40例损伤严重度评分（ISS）>15的事故，以及2008 - 2009年在瑞典发生的92例致命事故。第一步，使用决策树方法评估MAEB在这些事故中的潜在适用性。为此，开发了一种新的MAEB触发算法，以应对交叉场景以及涉及静止物体的事故。第二步，通过数值计算机模拟研究MAEB在适用事故中的潜在益处。对每起事故进行两次重建——一次部署MAEB，一次不部署。
主要发现是，使用新的触发算法，MAEB适用于广泛的多车辆摩托车事故。根据事故场景和车辆碰撞前的初始速度，通过将碰撞速度降低约10%实现了事故缓解。
本研究首次尝试使用深入数据，通过模拟在广泛的事故场景中评估MAEB。结果进一步深入了解了MAEB在不同速度范围内的可行性。显然，MAEB是一项有前景的技术，值得研究人员、制造商和监管机构进一步关注。
