增强城市自动紧急制动系统:基于距离估计误差容限和路面-轮胎摩擦系数的仿真分析

Yifan Wang, Jannes Iatropoulos, Silvia Thal, Roman Henze
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摘要

自动紧急制动系统对防止碰撞至关重要,但其有效性取决于对车辆与其他道路使用者之间距离的准确估计,以及对路况的了解。距离估计错误会导致过早或延迟制动,而不同的路况会改变轮胎摩擦系数,从而影响制动距离。先进传感器(如激光雷达)的集成大大提高了距离估计能力。摄像头和深度神经网络也被用来估计路况。然而,受复杂场景和雨雾等恶劣天气条件的影响,AEB 系统在城市环境中面临着显著的挑战。因此,研究这些估计的误差容限对于提高 AEB 系统的性能至关重要。为此,我们在 IPG CarMaker 仿真环境中开发了测试车辆的数字孪生模型,其中包括真实的驾驶动力学和传感器模型。我们的模拟测试车辆配备了距离估计算法和自动紧急制动(AEB)系统,旨在最终部署到现实世界中。我们在各种模拟测试场景中对车辆进行测试。这种方法有助于精确测量和调整车距及路面-轮胎摩擦系数。测试协议以欧洲新车评估计划(EU NCAP)的 AEB 汽车对行人标准为起点。此外,我们的模拟还包括真实的城市场景、复杂的交通状况和不同的天气情况,包括雨、雾和不同的路面,如干燥、潮湿、积雪和结冰。最后,我们确定了各种条件下的误差容限。模拟过程和结果表明,主要挑战包括创建关键场景、环境和传感器建模以及构建测试车辆的数字双胞胎。我们还提供了从这些结果中得出的建议和见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Urban AEB Systems: Simulation-Based Analysis of Error Tolerance in Distance Estimation and Road-Tire Friction Coefficients
AEB systems are critical in preventing collisions, yet their effectiveness hinges on accurately estimating the distance between the vehicle and other road users, as well as understanding road conditions. Errors in distance estimation can result in premature or delayed braking and varying road conditions alter road-tire friction coefficients, affecting braking distances. The integration of advanced sensors like LiDARs has significantly enhanced distance estimation. Cameras and deep neural networks are also employed to estimate the road conditions. However, AEB systems face notable challenges in urban environments, influenced by complex scenarios and adverse weather conditions such as rain and fog. Therefore, investigating the error tolerance of these estimations is essential for the performance of AEB systems. To this end, we develop a digital twin of our test vehicle in the IPG CarMaker simulation environment, which includes realistic driving dynamics and sensor models. Our simulated test vehicle is equipped with a distance estimation algorithm and AEB system designed for eventual deployment in its real-world counterpart. We test the vehicle in various simulated test scenarios. This approach facilitates accurate measurement and adjustment of distance and road-tire friction coefficients. The testing protocol begins with the European New Car Assessment Programme (EU NCAP) AEB Car-to-Pedestrian standard. Additionally, our simulation encompasses realistic urban scenarios, featuring complex traffic conditions and diverse weather scenarios, including rain, fog, and varying road surfaces like dry, wet, snow-covered, and icy. Finally, we have determined the error tolerances for various conditions. The simulation process and results reveal that the major challenges involve creating critical scenarios, modeling environments and sensors, and constructing digital twins of test vehicles. Recommendations and insights derived from these findings are also provided.
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