Advanced Longitudinal Control and Collision Avoidance for High-Risk Edge Cases in Autonomous Driving

Advanced Longitudinal Control and Collision Avoidance for High-Risk Edge Cases in Autonomous Driving Dianwei Chen a, Yaobang Gong a and Xianfeng Terry Yang a, a Department of Civil and Environmental Engineering, University of Maryland, College Park, Maryland, 20740, United StatesA R T I C L E I N F OKeywords: Advanced Driver-Assistance Systems Collision avoidance Reinforcement learning Edge Cases Trajectory calibration model Automatic Emergency Braking A B S T R A C T Advanced Driver-Assistance Systems (ADAS) and Advanced Driving Systems (ADS) are key to improving road safety, yet most existing implementations focus primarily on the vehicle ahead, neglecting the behavior of following vehicles. This shortfall often leads to chain-reaction collisions in high-speed, densely spaced traffic--particularly when a middle vehicle suddenly brakes and trailing vehicles cannot respond in time. To address this critical gap, we propose a novel longitudinal control and collision avoidance algorithm that integrates adaptive cruising with emergency braking. Leveraging deep reinforcement learning, our method simultaneously accounts for both leading and following vehicles. Through a data preprocessing framework that calibrates real-world sensor data, we enhance the robustness and reliability of the training process, ensuring the learned policy can handle diverse driving conditions. In simulated high-risk scenarios (e.g., emergency braking in dense traffic), the algorithm effectively prevents potential pile-up collisions, even in situations involving heavy-duty vehicles. Furthermore, in typical highway scenarios where three vehicles decelerate, the proposed DRL approach achieves a 99% success rate--far surpassing the standard Federal Highway Administration speed concepts guide, which reaches only 36.77% success under the same conditions.1. Introduction Advanced Driver-Assistance Systems (ADASs) and Automated Driving Systems (ADSs) are pivotal technologies in modern vehicles, sharing the overarching goal of improving road safety and paving the way toward fully autonomous driving. ADASs primarily function as semi-automated features that monitor the vehicle's environment, intervening when drivers do not respond adequately (Galvani, 2019; Kukkala et al., 2018). Collectively, these advancements have significantly enhanced road safety and occupant comfort, with many vehicles now including one or more ADAS features as standard equipment. In parallel, efforts in autonomous driving--encompassing levels of automation from partial (Level 3) to fully autonomous (Level 4 or 5)--have led to the development of ADSs (Leiman, 2021). These systems aim to replace or minimize human input in vehicle operation, leveraging advanced sensing, computing, and control technologies to handle dynamic road conditions (Okuda et al., 2014).