Driverless Car: Autonomous Driving Using Deep Reinforcement Learning in Urban Environment
| Autor: | Sabir Hossain, Doukhi Oualid, Deok Jin Lee, Abdur Razzaq Fayjie |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
010401 analytical chemistry Real-time computing 02 engineering and technology 01 natural sciences 0104 chemical sciences Lidar Laser sensor Obstacle avoidance 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing Image sensor Urban environment Front (military) |
| Zdroj: | UR |
| DOI: | 10.1109/urai.2018.8441797 |
| Popis: | Deep Reinforcement Learning has led us to newer possibilities in solving complex control and navigation related tasks. The paper presents Deep Reinforcement Learning autonomous navigation and obstacle avoidance of self-driving cars, applied with Deep Q Network to a simulated car an urban environment. The approach uses two types of sensor data as input: camera sensor and laser sensor in front of the car. It also designs a cost-efficient high-speed car prototype capable of running the same algorithm in real-time. The design uses a camera and a Hokuyo Lidar sensor in the car front. It uses embedded GPU (Nvidia-TX2) for running deep-learning algorithms based on sensor inputs. |
| Databáze: | OpenAIRE |
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