Imitation Learning for Vision-based Lane Keeping Assistance
Paper in proceedings, 2018

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.


Christopher Innocenti


Henrik Linden


Ghazaleh Panahandeh


Lennart Svensson

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Nasser Mohammadiha


IEEE International Conference on Intelligent Transportation Systems-ITSC

2153-0009 (ISSN)

20th IEEE International Conference on Intelligent Transportation Systems (ITSC)
Yokohama, Japan,

Subject Categories

Infrastructure Engineering


Computer Vision and Robotics (Autonomous Systems)



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