Street Mark Detection Using Raspberry PI for Self-Driving System

Self driving is an autonomous vehicle that can follow the road with less human intervention. The development of self driving utilizes various methods such as radar, lidar, GPS, camera, or combination of them. In this research, street mark detection system was designed using webcam and raspberry-pi mini computer for processing the image. The image was processed by HSV color filtering method. The processing rate of this algorithm was 137.98 ms correspondinig to 7.2 FPS. The self-driving prototype was found to be working optimally for “hue” threshold of 0-179, “saturation” threshold of 0-30, and “value” threshold of 200-255. Street mark detection has been obtained from the coordinates of street mark object which had range 4-167 on x axis and 4-139 on y axis. As a result, we have successfully built the street mark detection by COG method more effectively and smoothly in detection in comparison with Hough transform method

Towards an enhanced driver situation awareness system

This paper outlines our current research agenda to achieve enhanced driver situation awareness. A novel approach that incorporates information gathered from sensors mounted on the neighboring vehicles, in the road infrastructure as well as onboard sensory information is proposed. A solution to the fundamental issue of registering data into a common reference frame when the relative locations of the sensors themselves are changing is outlined. A description of the vehicle test bed, experimental results from information gathered from various onboard sensors, and preliminary results from the sensor registration algorithm are presented. ©2007 IEEE

Pedestrian lane detection in unstructured scenes for assistive navigation

Automatic detection of the pedestrian lane in a scene is an important task in assistive and autonomous navigation. This paper presents a vision-based algorithm for pedestrian lane detection in unstructured scenes, where lanes vary significantly in color, texture, and shape and are not indicated by any painted markers. In the proposed method, a lane appearance model is constructed adaptively from a sample image region, which is identified automatically from the image vanishing point. This paper also introduces a fast and robust vanishing point estimation method based on the color tensor and dominant orientations of color edge pixels. The proposed pedestrian lane detection method is evaluated on a new benchmark dataset that contains images from various indoor and outdoor scenes with different types of unmarked lanes. Experimental results are presented which demonstrate its efficiency and robustness in comparison with several existing methods

Visualizing Road Appearance Properties in Driving Video

With the increasing videos taken from driving recorders on thousands of cars, it is a challenging task to retrieve these videos and search for important information. The goal of this work is to mine certain critical road properties in a large scale driving video data set for traffic accident analysis, sensing algorithm development, and testing benchmark. Our aim is to condense video data to compact road profiles, which contain visual features of the road environment. By visualizing road edge and lane marks in the feature space with the reduced dimension, we will further explore the road edge models influenced by road and off-road materials, weather, lighting condition, etc