Neural Networks for Obstacle Avoidance
نویسندگان
چکیده
Learning a set of rules to perform reliable obstacle avoidance has been proven to be a difficult task. In this paper, a neural network learned from human driving data is introduced to model obstacle avoidance through dense areas of obstacles. The learned neural network is then tested on different scenarios and compared using cross-validation to determine the optimal network structure (number of nodes and layers). The results are also compared with another obstacle avoidance approach, which acts as a basis for comparison.
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