A real-time road-direction point detection model is developed based on convolutional neural network architecture which can adapt to complex environment. Firstly, the concept of road-direction point is defined for either single road or crossroad. For single road, the predicted road-direction point can serve as a guiding point for a self-driving vehicle to go ahead. In the situation of crossroad, multiple road-direction points can also be detected which will help this vehicle to make a choice from possible directions. Meanwhile, different types of road surface can be classified by this model for both paved roads and unpaved roads. This information will be beneficial for a self-driving vehicle to speed up or slow down according to various road conditions. Finally, the performance of this model is evaluated on different platforms including Jetson TX1. The processing speed can reach 12 FPS on this portable embedded system so that it provides an effective and economic solution of road-direction estimation in the applications of autonomous navigation.

This paper proposes a vanishing point-based road detection method. Firstly, a vanishing point is detected using a texture-based method proposed in a recent study. After that, a histogram is generated for detecting two road borders. The road area is defined as the region between the two road borders and below the vanishing point. The experimental results demonstrate that our method performs well in general road images.

This letter presents a novel approach for traffic light detection in a video frame captured by an in-vehicle camera. The algorithm consists of rotated principal component analysis (RPCA), modified amplitude thresholding with respect to the histograms of the PC planes and final filtering with a neural network. The proposed algorithm achieves an average detection rate of 96% and is very robust to variations in the image quality.

Road traffic incidents analysis has shown that a third of them occurs without any conflict which indicates problems with road following. In this paper a driving safety assistance system is introduced, whose aim is to prevent the driver drifting off or running off the road. The road following system is based on a frontal on-board monocular camera. In order to get a high degree of reliability and robustness, an original combination of three different algorithms is performed. Low level results from the first two algorithms are used to compute a reliability indicator and to update a high level model through the third algorithm using Kalman filtering. Searching areas of the road sides for the next image are also updated. Experimental results show the reliability and the robustness of this original association of three different algorithms. Various road situations are addressed, including roads with high curvature. A multi-lanes extension is also presented.