Quantitative characterization of AEB pulses across the modern fleet

Characteristics of specific Automatic Emergency Braking (AEB) pulses can result in increased motion of the occupant, which can lead to the occupant being out-of-position such that when a crash occurs protection may be compromised. Quantifying these variations across the modern fleet is crucial to understand the loading environment to which vehicle occupants are exposed. Therefore, we categorized the AEB pulses based on acceleration pulse features such as deceleration magnitude, jerk, and ramp time. A total of 2278 AEB vehicle tests (years 2013–2019) were extracted from the Insurance Institute for Highway Safety (IIHS) database and analyzed. The following pulse characteristics were extracted: Jerk (g/s), Ramp-time (s), and Maximum deceleration (g). A subset of tests in which the tested vehicle did not contact the foam target (n = 1665) was analyzed further, with the following additional variables extracted: Deceleration time (s), Steady-state deceleration (g), and Duration (s). Other non-pulse related features were also considered: Test speed (20 and 40 km/h), Curb weight (Kg), and Vehicle Model Year. Using machine learning methods, the pulses were categorized into clusters. One-way ANOVAs for continuous variables and X² for categorical features were used to assess differences between clusters (p ≤ 0.05). Using the entirety of the AEB vehicle tests extracted (n = 2278), a total of 3 clusters were selected. The three clusters showed significantly different Jerk, Ramp-time, and Maximum deceleration (p < 0.001). Target contact decreased in AEB tests with more recent vehicle model years (rate of contact 66% in 2014 vs 1.7% in 2019). In one cluster, Jerk and Maximum deceleration increased with vehicle model year. Using the subset of tests in which there was no contact with the foam target (n = 1665), 4 categories of pulses were selected. In both sets of clusters, Ramp-time and Jerk showed moderate inverse correlation (r = –0.7), while all other features showed a low correlation. These results show that AEB technology improved over the years in obstacle avoidance. The identification of AEB pulse clusters is important in order to describe distinct approaches to achieving AEB and to be able to reproduce representative AEB pulses in the laboratory and understand the influences of those pulses on occupants’ motion.

特定自动紧急制动（Automatic Emergency Braking, AEB）脉冲的特性可能会增大乘员的运动幅度，进而导致乘员处于不当体位，一旦发生碰撞，乘员防护效果便可能受损。量化当前量产车型中这类脉冲的差异，对于明确车辆乘员所承受的载荷环境至关重要。因此，我们基于减速度幅值、加加速度（jerk）以及斜坡时间（ramp time）等加速度脉冲特征，对AEB脉冲进行了分类。我们从公路安全保险协会（Insurance Institute for Highway Safety, IIHS）的数据库中提取了2013至2019年间的2278次AEB车辆测试数据并开展分析。本次提取的脉冲特征包括：加加速度（单位：g/s）、斜坡时间（单位：s）以及最大减速度（单位：g）。我们对其中测试车辆未接触泡沫靶标的测试子集（n=1665）进行了进一步分析，额外提取了以下变量：减速时长（单位：s）、稳态减速度（单位：g）以及持续时长（单位：s）。同时还纳入了与脉冲无关的其他特征：测试车速（20km/h与40km/h）、整备质量（单位：Kg）以及车辆生产年份。借助机器学习方法，我们将脉冲划分为多个聚类簇。针对连续变量采用单因素方差分析（One-way ANOVA），针对分类特征采用卡方检验（χ²），以评估各聚类簇间的差异（p≤0.05）。基于全部提取的2278次AEB车辆测试数据，我们最终选定了3个聚类簇。这3个簇在加加速度、斜坡时间以及最大减速度上均存在显著差异（p<0.001）。随着车辆生产年份愈发贴近当下，AEB测试中车辆接触泡沫靶标的比例逐渐降低：2014年接触率为66%，2019年则降至1.7%。在其中一个聚类簇中，加加速度与最大减速度随车辆生产年份的增加而升高。针对未接触泡沫靶标的1665次测试子集，我们进一步选定了4类脉冲。在这两组聚类簇中，斜坡时间与加加速度均呈现中等程度的负相关（相关系数r=-0.7），其余特征间的相关性则较低。上述结果表明，AEB技术在避障性能上逐年提升。对AEB脉冲聚类簇的识别具有重要意义：既可用于阐释实现AEB的不同技术路径，也能在实验室中复现具有代表性的AEB脉冲，同时有助于明确此类脉冲对乘员运动的影响。