0000000000948752

AUTHOR

Knut Eivind Sandsmark

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Road Detection for Reinforcement Learning Based Autonomous Car

2020

Human mistakes in traffic often have terrible consequences. The long-awaited introduction of self-driving vehicles may solve many of the problems with traffic, but much research is still needed before cars are fully autonomous.In this paper, we propose a new Road Detection algorithm using online supervised learning based on a Neural Network architecture. This algorithm is designed to support a Reinforcement Learning algorithm (for example, the standard Proximal Policy Optimization or PPO) by detecting when the car is in an adverse condition. Specifically, the PPO gets a penalty whenever the virtual automobile gets stuck or drives off the road with any of its four wheels.Initial experiments …

Artificial neural networkComputer sciencebusiness.industrySupervised learningNeural network architectureReinforcement learningArtificial intelligenceReinforcement learning algorithmbusinessProceedings of the 2020 The 3rd International Conference on Information Science and System
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