This paper aims to investigate direct imitation learning from human drivers for the task of lane keeping assistance in highway and country roads using grayscale images from a single front view camera. The employed method utilizes convolutional neural networks (CNN) to act as a policy that is driving a vehicle. The policy is successfully learned via imitation learning using real-world data collected from human drivers and is evaluated in closed-loop simulated environments, demonstrating good driving behaviour and a robustness for domain changes. Evaluation is based on two proposed performance metrics measuring how well the vehicle is positioned in a lane and the smoothness of the driven trajectory.
Imitation learning for vision-based lane keeping assistance
01.10.2017
241850 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
LANE KEEPING ASSISTANCE DEVICE AND LANE KEEPING ASSISTANCE METHOD
Europäisches Patentamt | 2018
|MACHINE VISION-BASED LANE KEEPING ASSISTANCE SYSTEM AND METHOD
Europäisches Patentamt | 2022
|