基于车路协同的行人车辆碰撞风险识别与决策方法

为提高行人与车辆碰撞风险识别和预警效果,研究车路协同(CVIS)环境中,多行驶状态下行人与车辆碰撞风险识别与决策过程。基于CVIS平台获取车辆直道行驶和换道行驶状态信息,考虑驾驶人反应状态、车辆运动状态对碰撞风险的影响。引入风险区域预判风险,将当前相对运动状态与决策阈值进行比较,分级识别碰撞风险。采用提醒避碰判别法实时提醒驾驶人当前驾驶状态,使其采取相应的阶梯式双重避碰措施实现避碰决策。仿真结果表明,随着车辆行车速度的增加,进行预警的起始距离和起始时间均呈增加趋势,符合实际情形,该方法考虑了影响人车碰撞的多种因素,能够对碰撞风险进行分级识别和预警。     

半刚性护栏受冲击载荷时力学行为数值模拟

对一个给定的半刚性护栏的设计结构进行抗冲击能力评价,同时对车辆质心与护栏悬挂点之间的距离参数进行优化.采用Abaqus 6.5软件对弹塑性大变形动力学接触问题进行有限元数值计算,给出了不同质心高度和不同质量车辆与护栏系统碰撞时车辆的最大外向位移,比较了车辆质心不同高度对应的冲击载荷下护栏系统吸收能量的特性.同时计算和讨论了车辆与地面的摩擦系数对护栏系统行为的影响.数值计算结果表明,给定的护栏系统设计能够有效拦阻和引导质量为1 500 kg、时速80 km/h的失控汽车; 在拦阻质量为10 000 kg、40 km/h时速的失控车辆时也比较成功,但不能使之回到正常行驶路线上.车身质心与护栏悬挂点相对距离为0.6 m时,护栏系统能在与车体碰撞时发挥最佳吸能效果.     

不同约束下乘员在车辆正碰中损伤概率预测

为减小乘员在车辆碰撞事故中的损伤概率,进而为降低事故严重程度提供理论依据,以车辆碰撞前后速度变化ΔV为自变量,根据头部和胸部的损伤公式,分析全重叠正面碰撞过程中乘员在安全带和安全气囊组合的4种约束条件下的损伤,将损伤数据与事故损伤判定标准——简明创伤分级标准(AIS)关联,预测乘员损伤等级为AIS3+的概率,并结合具体实例进行说明。结果表明:随着车辆碰撞前后速度变化ΔV增加,乘员损伤等级AIS3+的概率增大; 4种约束条件下,当ΔV小于20 km/h,AIS3+概率均低于10%;当ΔV处于20～60 km/h时,安全带和安全气囊同时约束对乘员的保护效果最好,其对应的AIS3+概率最低;当ΔV超过60 km/h时,约束系统对乘员的保护作用有限,4种约束条件对应的AIS3+概率均超过85%。故在碰撞之前,驾驶员应通过降低ΔV以及确保乘员受到安全带和安全气囊的共同保护,来减小乘员损伤概率。     

强降雨环境下公路车辆制动安全性分析

为分析强降雨环境下公路车辆的制动安全可靠性，基于2种停车视距模型引入可靠性理论，将降雨强度、车速及驾驶员思索时间视为随机变量，运用蒙特卡罗模拟方法建立强降雨环境下公路车辆制动安全可靠性分析模型，以研究不同降雨强度、不同车速下公路车辆的停车视距。研究结果表明：降雨强度一定时，2种计算模型的停车视距差值随着车速的增大而增大，车速一定时，2种计算模型的停车视距差值随着降雨强度的增大基本不变；若随机变量服从正态分布，停车视距也近似服从正态分布，且模型Ⅱ的分布均值大于模型Ⅰ,模型Ⅱ的分布标准差近似于模型Ⅰ;车辆的制动失效概率随着车速及降雨强度的增大而增大，车速及降雨强度一定时，基于模型Ⅱ计算的车辆制动失效概率略高于模型Ⅰ;车辆的临界车速随着降雨强度的增大而降低，随着制动失效概率的增大而增大，车辆制动失效概率及降雨强度一定时，基于模型Ⅰ计算的临界车速略高于模型Ⅱ;较传统确定性方法相比，基于可靠性方法可以获得更为合理的雨天行车安全临界车速，当降雨强度为4mm/min,失效概率为0.000 1时，临界车速已低于45km/h。     

基于隶属函数的高速公路安全距离模型研究

为了改善高速公路防追尾安全距离模型,对模型中的驾驶员反应时间进行研究,分析不同车速时驾驶员反应时间对安全距离的影响。驾驶员反应时间受驾龄、应变能力、性别、心理生理状况、车速等多种因素的影响。基于模糊数学中隶属函数的有关理论,利用Matlab软件,在驾驶员反应时间大小允许的范围内,综合考虑各种主要因素,确定驾驶员反应时间。通过具体实例说明该方法的应用,结果证明,应用模糊数学,可以计算出不同的驾驶员,在不同的车速时的反应时间。     

汽车-摩托车碰撞事故车速及碰撞位置预估方法

为给汽车-摩托车碰撞事故再现提供初始碰撞车速与碰撞位置的预估值,基于Pc-Crash软件所获得的仿真试验数据及支持向量回归方法,得到碰撞车速、车辆制动距离与汽车-骑车人静止位置间距离、汽车-摩托车静止位置间距离、摩托车-骑车人静止位置间距离的回归关系模型。用案例对所得模型进行演示及验证。结果表明,相关模型中决定系数大于0.993,而剩余标准差小于0.003,回归关系显著;用支持向量回归模型所得预估碰撞车速、车辆制动距离与借助Pc-Crash软件再现得到的结果很接近,相对误差均小于2%。     

基于行人横穿场景的AEB触发宽度优化研究

为研究自动紧急制动(AEB)系统控制策略中触发宽度对行人横穿场景结果的影响,利用自动驾驶仿真软件PreScan建立道路及车辆模型,在Matlab模型控制平台Simulink中设计AEB纵向控制算法,模拟行人横穿危险场景,不断调整触发宽度,观察碰撞结果。结果表明:当汽车速度处于30～50 km/h时,系统触发宽度为1.75 m,能够起到很好的避撞效果;当汽车速度处于50～80 km/h时,触发宽度需随行人速度增加而增加;触发宽度越宽,汽车接收信息越多,AEB误作用概率增大,故将最大触发宽度设置为3.5 m;当汽车速度处于60～80 km/h时,需同时优化触发宽度值和全力制动提前时间的长短,才能避免碰撞。     

人-车-路耦合环境中车辆稳态安全仿真

为揭示驾驶员、道路、车辆综合作用下车辆运行状态的失稳机理,基于多Agent建模与安全仿真技术,构建包含驾驶员、道路、车辆、协调中心、人机接口等5方面的多Agent车辆稳态安全仿真框架;建立车辆多体动力学、道路三维空间、驾驶员预瞄控制与跟随仿真模型,从车辆稳定性和驾驶员操作负荷2方面分析车辆稳态安全性。以小轿车为代表车型,针对某二级公路开展车辆稳态安全仿真试验。仿真结果表明,当汽车以60和70 km/h行驶时,前后轮总体围绕平均轴重波动,稳态运行;以80 km/h行驶时,汽车冲出车道,轮胎最小垂直反力仍然大于0;3种速度下侧向加速度均大于0.3 g(g为重力加速度),且速度越高,侧向加速度越大,表明汽车冲出车道由侧滑引起。     

男性行人与SUV正碰后的运动形态及损伤研究

为降低人车正面碰撞事故中男性行人的损伤程度,利用放缩法建立我国50百分位男性身高168.5cm,体质量为50.5、65.5、75.5、80.5 kg等4种体型的男性行人模型;在MADYMO仿真分析环境中建立上述不同体型的男性行人与运动型多功能车(SUV)在不同碰撞速度、不同最大制动减速度下的正面碰撞模型,开展仿真试验,研究碰撞后男性行人运动形态和头部损伤情况。结果表明:碰撞车速决定碰撞后男性行人运动形态,且显著影响男性行人头部损伤来源;碰撞车速为30～80 km/h,男性行人头部损伤主要是与地面碰撞所致;碰撞车速大于90 km/h,男性行人头部损伤主要是与SUV碰撞所致;男性行人体型越肥胖,与SUV碰撞所致的头部伤害指标值(HIC)越小;最大制动减速度越大,男性行人与SUV碰撞所致的头部HIC值越小,头部与SUV碰撞时刻越晚。     

基于改进驾驶员模型的车道保持控制模型

为解决传统车道保持系统控制精度较差、驾驶员模型对大曲率道路适应性不足的问题,对传统预瞄加速度驾驶员模型非零频率点进行泰勒展开以改进模型;利用驾驶模拟器试验数据和快速傅里叶方法(FFT)研究双移线道路下的主要转向频率,并搭建Simulink/Car Sim联合仿真模型以验证改进前后驾驶员模型的双移线路径跟踪精度。仿真试验表明:改进后模型的横向位移误差比改进前明显减小,车速为60、70和80 km/h时的最大横向位移误差比改进前分别降低了0.2、0.15和0.11 m;与常规道路人工势场车道保持系统的对比,改进驾驶员模型能够更好地控制车辆跟随期望路径行驶,体现出良好的车道保持能力。     

Effects of lead time of verbal collision warning messages on driving behavior in connected vehicle settings

IntroductionUnder the connected vehicle environment, vehicles will be able to exchange traffic information with roadway infrastructure and other vehicles. With such information, collision warning systems (CWSs) will be able to warn drivers with potentially hazardous situations within or out of sight and reduce collision accidents. The lead time of warning messages is a crucial factor in determining the effectiveness of CWSs in the prevention of traffic accidents. Accordingly, it is necessary to understand the effects of lead time on driving behaviors and explore the optimal lead time in various collision scenarios.MethodsThe present driving simulator experiment studied the effects of controlled lead time at 16 levels (predetermined time headway from the subject vehicle to the collision location when the warning message broadcasted to a driver) on driving behaviors in various collision scenarios.ResultsMaximum effectiveness of warning messages was achieved when the controlled lead time was within the range of 5 s to 8 s. Specifically, the controlled lead time ranging from 4 s to 8 s led to the optimal safety benefit; and the controlled lead time ranging from 5 s to 8 s led to more gradual braking and shorter reaction time. Furthermore, a trapezoidal distribution of warning effectiveness was found by building a statistic model using curve estimation considering lead time, lifetime driving experience, and driving speed.ConclusionsThe results indicated that the controlled lead time significantly affected driver performance.Practical applicationsThe findings have implications for the design of collision warning systems.     

Analysis of effects of driver’s evasive action time on rear-end collision risk using a driving simulator

Introduction: Driver’s evasive action is closely associated with collision risk in a critical traffic event. To quantify collision risk, surrogate safety measures (SSMs) have been estimated using vehicle trajectories. However, vehicle trajectories cannot clearly capture presence and time of driver’s evasive action. Thus, this study determines the driver’s evasive action based on his/her use of accelerator and brake pedals, and analyzes the effects of the driver’s evasive action time (i.e., duration of evasive action) on rear-end collision risk. Method: Fifty drivers’ car-following behavior on a freeway was observed using a driving simulator. An SSM called “Deceleration Rate to Avoid Crash (DRAC)” and the evasive action time were determined for each driver using the data from the driving simulator. Each driver tested two traffic scenarios – Cars and Trucks scenarios where conflicting vehicles were cars and trucks, respectively. The factors related to DRAC were identified and their effects on DRAC were analyzed using the Generalized Linear Models and random effects models. Results: DRAC decreased with the evasive action time and DRAC was closely related to drivers’ gender and driving experience at the road sections where evasive action to avoid collision was required. DRAC was also significantly different between Cars and Trucks scenarios. The effect of the evasive action time on DRAC varied among different drivers, particularly in the Trucks scenario. Conclusions: Longer evasive action time can significantly reduce crash risk. Driver characteristics are more closely related to effective evasive action in complex driving conditions. Practical Applications: Based on the findings of this study, driver warning information can be developed to alert drivers to take specific evasive action that reduces collision risk in a critical traffic event. The information is likely to reduce the variability of the driver’s evasive action and the speed variations among different drivers.     

雾霾时车辆尾气排放对驾驶员能见度的影响

针对北京道路车辆尾气排放对雾霾时驾驶员道路能见度的影响问题,建立了雾霾时车辆尾气排放与驾驶员道路能见度关系模型。该模型以车辆为单元建立车辆尾气排放元胞,考虑了车辆尾气排放后的污染物物理和化学变化,并以车道中的尾气排放位置截面及截面单元、交叉口处的尾气排放位置区间为模型单元。为验证该模型,于2016年10月-2017年2月和2017年10月-2018年2月,在北京地区部分路网检测了驾驶员道路能见度等数据,并统计了各时段驾驶员道路能见度的分布情况。对建立的关系模型进行仿真,结果表明,该关系模型的仿真结果与实际检测数据的误差占比平均值小于4. 98%,验证了该关系模型用于描述北京市道路车辆尾气排放对雾霾时驾驶员道路能见度影响的有效性。     

Estimated benefit of automated emergency braking systems for vehicle–pedestrian crashes in the United States

Abstract

Objective: The objective of this research study is to estimate the benefit to pedestrians if all U.S. cars, light trucks, and vans were equipped with an automated braking system that had pedestrian detection capabilities.

Methods: A theoretical automatic emergency braking (AEB) model was applied to real-world vehicle–pedestrian collisions from the Pedestrian Crash Data Study (PCDS). A series of potential AEB systems were modeled across the spectrum of expected system designs. Both road surface conditions and pedestrian visibility were accounted for in the model. The impact speeds of a vehicle without AEB were compared to the estimated impact speeds of vehicles with a modeled pedestrian detecting AEB system. These impacts speeds were used in conjunction with an injury and fatality model to determine risk of Maximum Abbreviated Injury Scale of 3 or higher (MAIS 3+) injury and fatality.

Results: AEB systems with pedestrian detection capability, across the spectrum of expected design parameters, reduced fatality risk when compared to human drivers. The most beneficial system (time-to-collision [TTC]?=?1.5?s, latency = 0?s) decreased fatality risk in the target population between 84 and 87% and injury risk (MAIS score 3+) between 83 and 87%.

Conclusions: Though not all crashes could be avoided, AEB significantly mitigated risk to pedestrians. The longer the TTC of braking and the shorter the latency value, the higher benefits showed by the AEB system. All AEB models used in this study were estimated to reduce fatalities and injuries and were more effective when combined with driver braking.     

Forward collision warning based on a driver model to increase drivers’ acceptance

Abstract

Objective: Systems that can warn the driver of a possible collision with a vulnerable road user (VRU) have significant safety benefits. However, incorrect warning times can have adverse effects on the driver. If the warning is too late, drivers might not be able to react; if the warning is too early, drivers can become annoyed and might turn off the system. Currently, there are no methods to determine the right timing for a warning to achieve high effectiveness and acceptance by the driver. This study aims to validate a driver model as the basis for selecting appropriate warning times. The timing of the forward collision warnings (FCWs) selected for the current study was based on the comfort boundary (CB) model developed during a previous project, which describes the moment a driver would brake. Drivers’ acceptance toward these warnings was analyzed. The present study was conducted as part of the European research project PROSPECT (“Proactive Safety for Pedestrians and Cyclists”).

Methods: Two warnings were selected: One inside the CB and one outside the CB. The scenario tested was a cyclist crossing scenario with time to arrival (TTA) of 4?s (it takes the cyclist 4?s to reach the intersection). The timing of the warning inside the CB was at a time to collision (TTC) of 2.6?s (asymptotic value of the model at TTA = 4?s) and the warning outside the CB was at TTC = 1.7?s (below the lower 95% value at TTA = 4?s). Thirty-one participants took part in the test track study (between-subjects design where warning time was the independent variable). Participants were informed that they could brake any moment after the warning was issued. After the experiment, participants completed an acceptance survey.

Results: Participants reacted faster to the warning outside the CB compared to the warning inside the CB. This confirms that the CB model represents the criticality felt by the driver. Participants also rated the warning inside the CB as more disturbing, and they had a higher acceptance of the system with the warning outside the CB. The above results confirm the possibility of developing wellsaccepted warnings based on driver models.

Conclusions: Similar to other studies’ results, drivers prefer warning times that compare with their driving behavior. It is important to consider that the study tested only one scenario. In addition, in this study, participants were aware of the appearance of the cyclist and the warning. A further investigation should be conducted to determine the acceptance of distracted drivers.     

Autonomous emergency braking systems adapted to snowy road conditions improve drivers' perceived safety and trust

Objective: This study investigated drivers' evaluation of a conventional autonomous emergency braking (AEB) system on high and reduced tire–road friction and compared these results to those of an AEB system adaptive to the reduced tire–road friction by earlier braking. Current automated systems such as the AEB do not adapt the vehicle control strategy to the road friction; for example, on snowy roads. Because winter precipitation is associated with a 19% increase in traffic crashes and a 13% increase in injuries compared to dry conditions, the potential of conventional AEB to prevent collisions could be significantly improved by including friction in the control algorithm. Whereas adaption is not legally required for a conventional AEB system, higher automated functions will have to adapt to the current tire–road friction because human drivers will not be required to monitor the driving environment at all times. For automated driving functions to be used, high levels of perceived safety and trust of occupants have to be reached with new systems. The application case of an AEB is used to investigate drivers' evaluation depending on the road condition in order to gain knowledge for the design of future driving functions.

Methods: In a driving simulator, the conventional, nonadaptive AEB was evaluated on dry roads with high friction (μ = 1) and on snowy roads with reduced friction (μ = 0.3). In addition, an AEB system adapted to road friction was designed for this study and compared with the conventional AEB on snowy roads with reduced friction. Ninety-six drivers (48 males, 48 females) assigned to 5 age groups (20–29, 30–39, 40–49, 50–59, and 60–75 years) drove with AEB in the simulator. The drivers observed and evaluated the AEB's braking actions in response to an imminent rear-end collision at an intersection.

Results: The results show that drivers' safety and trust in the conventional AEB were significantly lower on snowy roads, and the nonadaptive autonomous braking strategy was considered less appropriate on snowy roads compared to dry roads. As expected, the adaptive AEB braking strategy was considered more appropriate for snowy roads than the nonadaptive strategy. In conditions of reduced friction, drivers' subjective safety and trust were significantly improved when driving with the adaptive AEB compared to the conventional AEB. Women felt less safe than men when AEB was braking. Differences between age groups were not of statistical significance.

Conclusions: Drivers notice the adaptation of the autonomous braking strategy on snowy roads with reduced friction. On snowy roads, they feel safer and trust the adaptive system more than the nonadaptive automation.     

AEB effectiveness evaluation based on car-to-cyclist accident reconstructions using video of drive recorder

Objective: Though autonomous emergency braking (AEB) systems for car-to-cyclist collisions have been under development, an estimate of the benefit of AEB systems based on an analysis of accident data is needed for further enhancing their development. Compared to the data available from in-depth accident data files, data provided by drive recorders can be used to reconstruct car-to-cyclist collisions with greater accuracy because the position of cyclists can be observed from the videos. In this study, using data from drive recorders, the performance and limitations of AEB systems were investigated.

Method: Data of drive recorders involving taxi-to-cyclist collisions were collected. Using the images collected from the drive recorders of those taxis, 40 cases of 90° car-to-cyclist intersection collisions were reconstructed using PC-Crash. Then, the collisions were reconstructed again utilizing car models with AEB systems installed while changing the sensor’s field of view (FOV) and the delay time of initiating vehicle deceleration.

Results: The angle of FOV has a significant influence on avoiding car-to-cyclist collisions. Using a 50° FOV with a braking delay time of 0.5?s resulted in avoiding 6 collisions, and using a 90° FOV resulted in avoiding an additional 14 collisions. Even when installing an ideal AEB system providing 360° FOV and no delay time for braking, 8 collisions were not avoided, though the impact velocities were reduced for all of these remaining collisions. These collisions were caused by the cyclists’ sudden appearance in front of cars, and the time-to-collision (TTC) when the cyclists appeared was less than 0.9?s.

Conclusion: The AEB systems were effective for mitigating collisions that occurred due to driver perception delay. Because cyclists have a traveling velocity, a wide-angle FOV is effective for reduction of car-to-cyclist intersection collisions. The reduction of delay time in braking can reduce the number of collisions that are close to the braking performance limit. The collisions that remained even with an ideal AEB system in the PC-Crash simulation indicate that such collisions could still occur for autonomous cars if the traffic environment does not change.     

Fatal crash between a car operating with automated control systems and a tractor-semitrailer truck

Abstract

Objective: The objective of this investigation was to evaluate the interaction between an SAE level 2 automated vehicle and the driver, including the limitations imposed by the vehicle on the driver.

Methods: A case study of the first fatal crash involving a vehicle operating with an automated control system was performed using scene evidence, vehicle damage, and recorded data available from the vehicle, and information from both drivers, including experience, phone records, computer systems, and medical information, was reviewed.

Results: System performance data downloaded from the car indicated that the driver was operating it using the Traffic-Aware Cruise Control and Autosteer lane-keeping systems, which are automated vehicle control systems within Tesla’s Autopilot suite. As the car crested the hill, a tractor trailer began its left turn onto a crossing roadway. Although reconstruction of the crash determined that there was sufficient sight distance for both drivers to see each other and take action, neither responded to the circumstances leading to the collision. Further, based on the speeds of the vehicles and simulations of the truck’s path, the car driver had at least 10.4?s to detect the truck and take evasive action. Neither the car driver nor the Autopilot system changed the vehicle’s velocity.

?At the time of the crash, the system performance data indicated that the last driver interaction with the system was 1?min 51?s prior when the cruise control speed was set to 74?mph. The driver was operating the vehicle using the Autopilot system for 37 of the 41?min in the last trip. During this period, the vehicle detected the driver’s hands on the steering wheel for a total of 25?s; each time his hands were detected on the wheel was preceded by a visual alert or auditory warning.

Conclusions: The National Transportation Safety Board (NTSB) determined that the probable cause of the Williston, Florida, crash was the truck driver’s failure to yield the right of way to the car, combined with the car driver’s inattention due to overreliance on vehicle automation, which resulted in the car driver’s lack of reaction to the presence of the truck. Contributing to the car driver’s overreliance on the vehicle automation was the car’s operational design, which permitted the driver’s prolonged disengagement from the driving task and his use of the automation in ways inconsistent with guidance and warnings from the manufacturer.     

Driver Behavior During Overtaking Maneuvers from the 100-Car Naturalistic Driving Study

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.     

Analysis of Driver Evasive Maneuvering Prior to Intersection Crashes Using Event Data Recorders

Objective: Intersection crashes account for over 4,500 fatalities in the United States each year. Intersection Advanced Driver Assistance Systems (I-ADAS) are emerging vehicle-based active safety systems that have the potential to help drivers safely navigate across intersections and prevent intersection crashes and injuries. The performance of an I-ADAS is expected to be highly dependent upon driver evasive maneuvering prior to an intersection crash. Little has been published, however, on the detailed evasive kinematics followed by drivers prior to real-world intersection crashes. The objective of this study was to characterize the frequency, timing, and kinematics of driver evasive maneuvers prior to intersection crashes.

Methods: Event data recorders (EDRs) downloaded from vehicles involved in intersection crashes were investigated as part of NASS-CDS years 2001 to 2013. A total of 135 EDRs with precrash vehicle speed and braking application were downloaded to investigate evasive braking. A smaller subset of 59 EDRs that collected vehicle yaw rate was additionally analyzed to investigate evasive steering. Each vehicle was assigned to one of 3 precrash movement classifiers (traveling through the intersection, completely stopped, or rolling stop) based on the vehicle's calculated acceleration and observed velocity profile. To ensure that any significant steering input observed was an attempted evasive maneuver, the analysis excluded vehicles at intersections that were turning, driving on a curved road, or performing a lane change. Braking application at the last EDR-recorded time point was assumed to indicate evasive braking. A vehicle yaw rate greater than 4° per second was assumed to indicate an evasive steering maneuver.

Results: Drivers executed crash avoidance maneuvers in four-fifths of intersection crashes. A more detailed analysis of evasive braking frequency by precrash maneuver revealed that drivers performing complete or rolling stops (61.3%) braked less often than drivers traveling through the intersection without yielding (79.0%). After accounting for uncertainty in the timing of braking and steering data, the median evasive braking time was found to be between 0.5 to 1.5 s prior to impact, and the median initial evasive steering time was found to occur between 0.5 and 0.9 s prior to impact. The median average evasive braking deceleration for all cases was found to be 0.58 g. The median of the maximum evasive vehicle yaw rates was found to be 8.2° per second. Evasive steering direction was found to be most frequently in the direction of travel of the approaching vehicle.

Conclusions: The majority of drivers involved in intersection crashes were alert enough to perform an evasive action. Most drivers used a combination of steering and braking to avoid a crash. The average driver attempted to steer and brake at approximately the same time prior to the crash.