Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving

Supervised deep learning methods using image data as input have shown promising results in the context of vehicle control. However, these supervised methods have two main disadvantages: 1) They require a copious amount of labeled training data, which is difficult and expensive to collect. 2) Such models do not perform well, when situations that are not in the distribution of the training set are encountered. This includes deviations from the designated driving behavior. We therefore provide a framework to mitigate these problems from merely an unlabeled sequence of images. Visual Odometry is first used to determine the vehicle trajectory. Model Predictive Control (MPC) then uses this trajectory to implicitly infer the steering labels. Meanwhile, synthesized images at deviated trajectories are included in the training distribution for enhanced robustness of the neural network model. Experimental results demonstrate that the performance of our network is at par with methods requiring additional data collection or supervision. Code and supplementary information is available here: https://github.com/idilsulo/nn-driving

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Imitation learning based decision-making for autonomous vehicle control at traffic roundabouts

Wang

Jiang

Lin

et al. 2022

Multimed Tools Appl

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The essential of developing an advanced driving assistance system is to learn human-like decisions to enhance driving safety. When controlling a vehicle, joining roundabouts smoothly and timely is a challenging task even for human drivers. In this paper, we propose a novel imitation learning based decision making framework to provide recommendations to join roundabouts. Our proposed approach takes observations from a monocular camera mounted on vehicle as input and use deep policy networks to provide decisions when is the best timing to enter a roundabout. The domain expert guided learning framework can not only improve the decision-making but also speed up the convergence of the deep policy networks. We evaluate the proposed framework by comparing with state-of-the-art supervised learning methods, including conventional supervised learning methods, such as SVM and kNN, and deep learning based methods. The experimental results demonstrate that the imitation learning-based decision making framework, which ourperforms supervised learning methods, can be applied in driving assistance system to facilitate better decision-making when approaching roundabouts.

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Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving

Cited by 15 publications

References 16 publications

Lateral Ego-Vehicle Control Without Supervision Using Point Clouds

Lateral Ego-Vehicle Control Without Supervision Using Point Clouds

Learning vision based autonomous lateral vehicle control without supervision

Imitation learning based decision-making for autonomous vehicle control at traffic roundabouts

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