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Collision Warning Timing, Driver Distraction, and Driver Response to Imminent Rear-End Collisions in a High-Fidelity Driving Simulator

机译:高保真驾驶模拟器中的碰撞警告正时,驾驶员分心和驾驶员对即将发生的后端碰撞的响应

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摘要

Rear-end collisions account for almost 30% of automotive crashes. Rear-end collision avoidance systems (RECASs) may offer a promising approach to help drivers avoid these crashes. Two experiments performed using a high-fidelity motion-based driving simulator examined driver responses to evaluate the efficacy of a RECAS. The first experiment showed that early warnings helped distracted drivers react more quickly - and thereby avoid more collisions - than did late warnings or no warnings. Compared with the no-warning condition, an early RECAS warning reduced the number of collisions by 80.7%. Assuming collision severity is proportional to kinetic energy, the early warning reduced collision severity by 96.5%. In contrast, the late warning reduced collisions by 50.0 % and the corresponding severity by 87.5%. The second experiment showed that RECAS benefits even undistracted drivers. Analysis of the braking process showed that warnings provide a potential safety benefit by reducing the time required for drivers to release the accelerator. Warnings do not, however, speed application of the brake, increase maximum deceleration, or affect mean deceleration. These results provide the basis for a computational model of driver performance that was used to extrapolate the findings and identify the most promising parameter settings. Potential applications of these results include methods for evaluating collision warning systems, algorithm design guidance, and driver performance model input.
机译:追尾事故几乎占汽车事故的30%。后端避撞系统(RECAS)可能提供一种有前途的方法来帮助驾驶员避免这些撞车事故。使用基于高保真运动的驾驶模拟器进行的两个实验检查了驾驶员的反应,以评估RECAS的功效。第一个实验表明,与晚警告或无警告相比,预警有助于分心的驾驶员反应更快,从而避免了更多的碰撞。与不发出警告的情况相比,早期的RECAS警告将碰撞次数减少了80.7%。假设碰撞的严重程度与动能成正比,则预警将碰撞的严重程度降低了96.5%。相比之下,后期预警减少了50.0%的碰撞,相应的严重程度降低了87.5%。第二个实验表明RECAS甚至可以让分散注意力的驾驶员受益。对制动过程的分析表明,警告可以通过减少驾驶员释放加速器所需的时间来提供潜在的安全益处。但是,警告不会影响制动器的速度,增加最大减速度或影响平均减速度。这些结果为驾驶员性能计算模型提供了基础,该模型用于推断发现并确定最有希望的参数设置。这些结果的潜在应用包括评估碰撞预警系统的方法,算法设计指南和驾驶员性能模型输入。

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