Download PDFOpen PDF in browserRear-end Vehicle Collision Avoidance using Reinforced Learning10 pages•Published: March 21, 2024AbstractA rear-end collision happens when a driver collides with the vehicle directly ahead of them from the rear. Such accidents are common at traffic stops like red lights and stop signs or in heavy traffic conditions. While most rear-end accidents occur at low speeds, they can also happen at higher speeds on highways, interstates, and similar fast-moving roadways. Typically, these accidents involve two vehicles, but they can sometimes lead to a domino effect involving multiple vehicles. [1]. This study delves into Mitigating rear-end vehicle collisions using reinforcement learning (RL). The RL algorithm in focus is intended to be integrated into the ego vehicle's system, see Figure 1, aiming primarily to avert colliding with the rear car when both vehicles are progressing forward. Through the utilization of reinforcement learning algorithms, the RCA system can learn from its interactions with the environment, adapt to changing scenarios, and make intelligent decisions to prevent or mitigate collisions effectively. This research investigates the application of the Deep Deterministic Policy Gradient (DDPG) algorithm in the context of rear collision avoidance. The research methodology involves developing a simulated environment that accurately represents lane driving scenarios using longitudinal car dynamics for the ego and rear cars, including the two vehicles’ speeds and positions. The outcomes of this research study are expected to contribute to the development of advanced rear collision avoidance systems that can adapt and improve their performance based on real-time data and experiences.Keyphrases: deep deterministic policy gradient, rear end collision, reinforcement learning In: Ajay Bandi, Mohammad Hossain and Ying Jin (editors). Proceedings of 39th International Conference on Computers and Their Applications, vol 98, pages 36-45.
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