Autonomous Emergency Steering Using Deep Reinforcement Learning For Advanced Driver Assistance System
| Autor: | Kinsuk Sarkar, Satoru Araki, Abinav Kaushik, Ishaan Sood, Bharat Kumar Padi, Gakuyo Fujimoto, Misako Yoshimura, Amit More, Tsuchiya Masamitsu, Yuji Yasui, Tijmen Tieleman, Abdul Muneer, Matthew Dennison, Anil Hebber |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science 020208 electrical & electronic engineering Work (physics) 02 engineering and technology Collision 020901 industrial engineering & automation Control theory 0202 electrical engineering electronic engineering information engineering Trajectory Reinforcement learning Collision avoidance system Advanced driver Collision avoidance Simulation |
| Zdroj: | SICE |
| Popis: | Although automobile technology has continuously evolved in recent years, there are still many traffic accidents all over the world. In order to reduce the number of traffic accidents, we focused on developing an emergency collision avoidance system. In this work, our goal is to propose a smart controller which can calculate the trajectory in order to avoid vehicle collision in the emergency situation while preventing the potential secondary collision caused by the change of trajectory in order to avoid the first collision. We consider two emergency driving scenarios as a first step towards this goal and propose a solution using deep reinforcement learning algorithm. We present detailed results of training and validation in the simulated environment. Further, we also validate the proposed solution in an actual vehicle and show that the trained controller successfully avoided collisions in case of an emergency situation without any human interaction. |
| Databáze: | OpenAIRE |
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