text: Traffic congestion is a widespread problem. Dynamic traffic routing systems and congestion pricing are getting importance in recent research. Lane prediction and vehicle density estimation is an important component of such systems. We introduce a novel problem of vehicle self-positioning which involves predicting the number of lanes on the road and vehicle's position in those lanes using videos captured by a dashboard camera. We propose an integrated closed-loop approach where we use the presence of vehicles to aid the task of self-positioning and vice-versa. To incorporate multiple factors and high-level semantic knowledge into the solution, we formulate this problem as a Bayesian framework. In the framework, the number of lanes, the vehicle's position in those lanes and the presence of other vehicles are considered as parameters. We also propose a bounding box selection scheme to reduce the number of false detections and increase the computational efficiency. We show that the number of box proposals decreases by a factor of 6 using the selection approach. It also results in large reduction in the number of false detections. The entire approach is tested on real-world videos and is found to give acceptable results.
  @cite_16: Assigning the ego-vehicle to a lane is not only beneficial for navigation but will be an essential element in future Advanced Driver Assistance Systems. This paper describes an approach for ego-lane index estimation using only a monocular camera and no additional sensing equipment like, e.g., the typically employed GPS and Inertial Measurement Unit. Key aspect of the approach are SPatial RAY (SPRAY) features which represent the spatial layout of the road in the visual scene. The proposed method perceives a variety of local visual properties of the scene by means of base classifiers operating on patches extracted from camera images. The spatial arrangement of these local visual properties are captured using SPRAY features. With a boosting classifier trained on these features the ego-lane index is obtained. The system is evaluated on low traffic density and complementary to an object-based approach suitable for heavy traffic. In the conducted experiments, the proposed approach reaches recognition rates of 93 to 97 on individual highway images without applying any kind of temporal filtering.
summary: A similar problem to self-positioning has been handled in @cite_16 using spatial RAY features. It tries to predict the position of the vehicle on the road from an input video stream. However, they predict at most 3 lanes at a time either on the left or right hand side, whereas we have upto 6 lanes in our database with upto 3 lanes being on each side. In addition, there experiments are spread over only 2 days with similar road conditions. Our experiments are more rigorous, spreading over 5 days and the data includes 6 different road conditions. They have low traffic density whereas we have moderate-to-high traffic density. Thus direct comparison between these two approaches may not be possible.