David Riesman: Außenleitung

Abels, Heinz; König, Alexandra

doi:10.1007/978-3-531-92024-5_7

Cited by 1 publication

(3 citation statements)

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“…Rear-end collisions can result in whiplash, head, neck, and back injuries, which can have long-lasting effects on the individuals involved. Rear collision avoidance systems can help by triggering automatic braking or alerting the driver to act, reducing the severity of the impact and potentially minimizing deaths and injuries; around 1,700 people die, and another 500,000 are injured in rear-end collisions in the United States each year [1].…”

Section: Mitigating Deaths and Injuriesmentioning

confidence: 99%

“…This technology can detect objects or vehicles approaching from behind and provide warnings or take automatic actions to prevent a collision. Around 1.7 million rear-end collisions, about 29 percent of all car crashes, occur in the United States annually [1].…”

Section: Introductionmentioning

confidence: 99%

“…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).…”

mentioning

confidence: 99%

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Rear-end Vehicle Collision Avoidance using Reinforced Learning

Sebi,

Kintali,

Cheok

EPiC Series in Computing

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A 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.

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Section: Mitigating Deaths and Injuriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%