Search

Driving environment perception technology is a key issue for driving assistance systems and autonomous vehicles. Driving environment information collection and information fusion and representation are two important contents of this technology. On-board sensors such as camera, radar, lidar, etc., are popularly used to collect relevant information. However, when using these on-board sensors, uncertainty arises from ignorance and errors, where ignorance is usually caused by vehicle occlusion, and errors is caused by imprecise pose estimation and noisy measurements. Increasing sensor number would decrease such uncertainty, but complexity and cost is also increased in this way. In this paper, we present a V2I (vehicle-infrastructure) based urban driving environment collection method achieved only by two sensors, i.e., a Road Side Unit camera (RSU camera) and vehicle GPS. RSU camera is used to extract lane marking information, including position and type, and vehicle position information. Vehicle GPS provide position information of vehicle. A credibility map is proposed as the compact representation of urban environment, and accept those information from RSU camera and vehicle GPS. In order to hand uncertainty caused by sensors, Dempster-Shafer theory is employed to estimate the credibility of each cell, fuse the credibility root in the information provided by RSU camera and vehicle GPS, and map onto the credibility map. The performance of this method has been validated with real-world urban traffic data. [ABSTRACT FROM AUTHOR]

Edge information help highlight the contour as well as cast shadow of objects. As the low complexity for edge extraction, the edge-based methods are widely used in vehicle detection. Traditional edge-based vehicle detection methods are easily interfered by noise and background, which resulting in inaccurate false detection. In this paper, a vehicle detection method based on multiscale edge fusion is proposed. First, multiscale images are obtained from the decomposition of the DoG pyramid. Second, multiscale edges are extracted by the DoG operator in multiscale images. Third, different scale edge map are fused according to the proposed multiscale edge fusion strategy. Then, an accurately located, low redundant and strongly anti-noise edge map is obtained. Finally, morphological operation and connectivity analysis are applied on the edge fusion map. Experiments with traffic images in different weather conditions verify the practicability of the proposed method. Comparison with related method in detection rate and detection accuracy verifies the superiority of the proposed method. [ABSTRACT FROM AUTHOR]