Deep Learning and Control Algorithms of Direct Perception for Autonomous Driving

26 Oct 2019Der-Hau LeeKuan-Lin ChenKuan-Han LiouChang-Lun LiuJinn-Liang Liu

Based on the direct perception paradigm of autonomous driving, we investigate and modify the CNNs (convolutional neural networks) AlexNet and GoogLeNet that map an input image to few perception indicators (heading angle, distances to preceding cars, and distance to road centerline) for estimating driving affordances in highway traffic. We also design a controller with these indicators and the short-range sensor information of TORCS (the open racing car simulator) for driving simulated cars to avoid collisions... (read more)

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