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Conference Paper
자율주행을 위한 TORCS 기반 End-to-End 학습
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- Issue Date
- 2017-11
- Citation
- 대한전자공학회 종합 학술 대회 (추계) 2017, pp.740-743
- Publisher
- 대한전자공학회
- Language
- Korean
- Type
- Conference Paper
- Abstract
- In this paper, we trained a convolutional neural network (CNN) to map a single front-facing camera image from The Open Racing Car Simulator (TORCS) directly to steering commands. To this end, we developed the driving experience data acquisition system, which saves the front-facing camera images and the corresponding steering commands during racing at 10Hz. The collected data is then used to train a deep convolutional neural network. At the end of this paper, we analyze how the collected data affects the driving quality of the end-to-end driving system.
- KSP Keywords
- Camera Image, Car simulator, Convolution neural network(CNN), Data Acquisition(DAQ), Deep convolutional neural networks, Driving experience, Driving system, End to End(E2E), Front-facing camera, Racing car, data acquisition system