Unconstrained Road Marking Recognition with Generative Adversarial Networks

Recent road marking recognition has achieved great success in the past few years along with the rapid development of deep learning. Although considerable advances have been made, they are often over-dependent on unrepresentative datasets and constrained conditions. In this paper, to overcome these drawbacks, we propose an alternative method that achieves higher accuracy and generates high-quality samples as data augmentation. With the following two major contributions: 1) The proposed deblurring network can successfully recover a clean road marking from a blurred one by adopting generative adversarial networks (GAN). 2) The proposed data augmentation method, based on mutual information, can preserve and learn semantic context from the given dataset. We construct and train a class-conditional GAN to increase the size of training set, which makes it suitable to recognize target. The experimental results have shown that our proposed framework generates deblurred clean samples from blurry ones, and outperforms other methods even with unconstrained road marking datasets.Comment: Accepted at IEEE Intelligent Vehicles Symposium (IV), 201

Imitating Driver Behavior with Generative Adversarial Networks

The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts a method for overcoming the problem of cascading errors inherent in prior approaches, resulting in realistic behavior that is robust to trajectory perturbations. We extend Generative Adversarial Imitation Learning to the training of recurrent policies, and we demonstrate that our model outperforms rule-based controllers and maximum likelihood models in realistic highway simulations. Our model both reproduces emergent behavior of human drivers, such as lane change rate, while maintaining realistic control over long time horizons.Comment: 8 pages, 6 figure

Using Deep Neural Networks to Classify Symbolic Road Markings for Autonomous Vehicles

To make autonomous cars as safe as feasible for all road users, it is essential to interpret as many sources of trustworthy information as possible. There has been substantial research into interpreting objects such as traffic lights and pedestrian information, however, less attention has been paid to the Symbolic Road Markings (SRMs). SRMs are essential information that needs to be interpreted by autonomous vehicles, hence, this case study presents a comprehensive model primarily focused on classifying painted symbolic road markings by using a region of interest (ROI) detector and a deep convolutional neural network (DCNN). This two-stage model has been trained and tested using an extensive public dataset. The two-stage model investigated in this research includes SRM classification by using Hough lines where features were extracted and the CNN model was trained and tested. An ROI detector is presented that crops and segments the road lane to eliminate non-essential features of the image. The investigated model is robust, achieving up to 92.96 percent accuracy with 26.07 and 40.1 frames per second (FPS) using ROI scaled and raw images, respectively