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.
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.
@cite_16
2019933614
Driver-assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively considered. It is therefore important to take a critical look at key aspects of these systems, one of which is lane-position tracking. It is for these driver-assistance objectives that motivate the development of the novel "video-based lane estimation and tracking" (VioLET) system. The system is designed using steerable filters for robust and accurate lane-marking detection. Steerable filters provide an efficient method for detecting circular-reflector markings, solid-line markings, and segmented-line markings under varying lighting and road conditions. They help in providing robustness to complex shadowing, lighting changes from overpasses and tunnels, and road-surface variations. They are efficient for lane-marking extraction because by computing only three separable convolutions, we can extract a wide variety of lane markings. Curvature detection is made more robust by incorporating both visual cues (lane markings and lane texture) and vehicle-state information. The experiment design and evaluation of the VioLET system is shown using multiple quantitative metrics over a wide variety of test conditions on a large test path using a unique instrumented vehicle. A justification for the choice of metrics based on a previous study with human-factors applications as well as extensive ground-truth testing from different times of day, road conditions, weather, and driving scenarios is also presented. In order to design the VioLET system, an up-to-date and comprehensive analysis of the current state of the art in lane-detection research was first performed. In doing so, a comparison of a wide variety of methods, pointing out the similarities and differences between methods as well as when and where various methods are most useful, is presented 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. In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions. We tackle rainy and low illumination conditions, which have not been extensively studied until now due to clear challenges. For example, images taken under rainy days are subject to low illumination, while wet roads cause light reflection and distort the appearance of lane and road markings. At night, color distortion occurs under limited illumination. As a result, no benchmark dataset exists and only a few developed algorithms work under poor weather conditions. To address this shortcoming, we build up a lane and road marking benchmark which consists of about 20,000 images with 17 lane and road marking classes under four different scenarios: no rain, rain, heavy rain, and night. We train and evaluate several versions of the proposed multi-task network and validate the importance of each task. The resulting approach, VPGNet, can detect and classify lanes and road markings, and predict a vanishing point with a single forward pass. Experimental results show that our approach achieves high accuracy and robustness under various conditions in real-time (20 fps). The benchmark and the VPGNet model will be publicly available. This paper describes a system for detecting and estimating the properties of multiple travel lanes in an urban road network from calibrated video imagery and laser range data acquired by a moving vehicle. The system operates in real-time in several stages on multiple processors, fusing detected road markings, obstacles, and curbs into a stable non-parametric estimate of nearby travel lanes. The system incorporates elements of a provided piecewise-linear road network as a weak prior. Our method is notable in several respects: it detects and estimates multiple travel lanes; it fuses asynchronous, heterogeneous sensor streams; it handles high-curvature roads; and it makes no assumption about the position or orientation of the vehicle with respect to the road. We analyze the system's performance in the context of the 2007 DARPA Urban Challenge. With five cameras and thirteen lidars, our method was incorporated into a closed-loop controller to successfully guide an autonomous vehicle through a 90 km urban course at speeds up to 40 km h amidst moving traffic.