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Vision-based Autonomous Driving for Unstructured Environments Using Imitation Learning by Ahn, Joonwoo; Kim, Minsoo; Park, Jaeheung is a scholarly article available to read on EtoBox.

What is Vision-based Autonomous Driving for Unstructured Environments Using Imitation Learning about?

Unstructured environments are difficult for autonomous driving. This is because various unknown obstacles are lied in drivable space without lanes, and its width and curvature change widely. In such complex environments, searching for a path in real-time is difficult. Also, inaccurate localization data reduce the path tracking accuracy, increasing the risk of collision. Instead of searching and tracking the path, an alternative approach has been proposed that reactively avoids obstacles in real-time. Some methods are available for tracking global path while avoiding obstacles using the candidate paths and the artificial potential field. However, these methods require heuristics to find specific parameters for handling various complex environments. In addition, it is difficult to track the global path accurately in practice because of inaccurate localization data. If the drivable space is not accurately recognized (i.e., noisy state), the vehicle may not smoothly drive or may collide with obstacles. In this study, a method in which the vehicle drives toward drivable space only using a vision-based occupancy grid map is proposed. The proposed method uses imitation learning, where a d

Author

Ahn, Joonwoo; Kim, Minsoo; Park, Jaeheung

Published

2022

Language

EN