Measuring driving performance for an All-Terrain Vehicle in a paved road in the woods

This paper provides a technique to rate human driving skills. This technique is initiated using a combination of deep learning in computer vision and Inverse-Reinforcement Learning (IRL). The main idea is to transfer knowledge through human-machine-human. The use of machine learning aims at teaching an agent the best driving attitudes from a human expert driver, then this agent is used to teach humans once again. To achieve that goal, deep learning semantic segmentation (S.S.) efficient network (ENet) was used to detect the road. IRL was used to get the reward function from an expert driver's behavior while driving the All-Terrain Vehicle (ATV) in the woods.

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