Autonomous Emergency Braking (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. Advancements in sensor technology and deep learning have improved vehicle perception and real-world understanding. 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. Determining the actual distances between other road users and the ego vehicle poses a significant challenge. Likewise, precisely capturing and adjusting real-world values of road-tire friction coefficients is highly challenging. To address these issues, we develop a digital twin of our test vehicle in the IPG CarMaker simulation environment, which includes realistic driving dynamics and sensor models. This approach facilitates accurate measurement and adjustment of distance and road-tire friction coefficients. Our simulated test vehicle is equipped with a distance estimation algorithm and AEB system designed for eventual deployment in its real-world counterpart. 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. The study concludes with an analysis of the identified error tolerances, an in-depth discussion on the challenges encountered during the simulation process, and reflections on the implications for future research and practical applications.
Session:
Poster
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