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The future of intelligent vehicles will rely on robust information to allow the proper feedback to the vehicle itself, to issue several kinds of active safety, but before all, to generate information for the driver by calling his or her attention to potential instantaneous or mid-term risks associated with the driving. Before true vehicle autonomy, safety and driver assistance are a priority. Sophisticated...
Various image processing techniques and geometric models have been applied in vision based lane detection subsystems of intelligent vehicles and Advanced Driver Assistance Systems (ADAS). However, challenging conditions such as strong shadows, occlusions, eroded markings, high curvatures are ongoing issues in this topic. In this paper, a novel lane extraction method based on symmetrical local threshold...
Bus lane enforcement system aims to monitor illegal utilization of bus lane by non-permitted vehicles (violator). However, Road-side system leads to considerable infrastructure cost while bus mounted system has limited surveillance coverage. In this paper, we consider an interesting problem in bus mounted system: how to improve the surveillance coverage of bus mounted system without additional infrastructure...
This paper describes a novel method for detecting vehicles on a highway using two visual features: color and texture. Our method consists of a segmentation process computed on the L*u*v* color space and a texture feature extraction procedure based on Dual-Tree Complex Wavelet Transform. We also apply a denoising process using morphological operations to build a background model and make possible the...
Accurate lane detection in real-time is a critical task in autonomous vehicle guidance and lane departure warning for driver assistance. Existing vision-based approaches rely mostly on some analysis of the spatial gradient of the image. However, if the road structure is not regular and well delimited, edges may not be easy to extract and other features must be employed. This paper evaluates the use...
Road detection is a crucial part of autonomous driving system. Most of the methods proposed nowadays only achieve reliable results in relatively clean environments. In this paper, we combine edge detection with road area extraction to solve this problem. Our method works well even on noisy campus road whose boundaries are blurred with sidewalks and surface is often covered with unbalanced sunlight...
Application of a vision system and a remote control car to an elective fourth year design project in electrical engineering is discussed. The design project involves learning and application of various sensors, actuators, control theories. The end product of the design project is an intelligent vehicle that is able to follow an emulated highway automatically. A camera with embedded color tracking...
For realizability and real-time processing consideration, a novel vehicle classification method is proposed for heavy traffic flow multi-lanes roads, which can classify vehicles into cars, trucks and buses. In order to monitor two lanes, our system uses three cameras which are mounted overhead of the road and look down the road at an angle of about 60 degrees. Two of them focus on the two lanes respectively...
A novel vision-based road detection method was proposed in this paper to realize visual guiding navigation for ground mobile vehicles in outdoor environments. The road region was first segmented from the jumbled backgrounds by using an adaptive threshold segmentation algorithm named OTSU. Subsequently, the Canny edges extracted in grey images would be filtered in the road region so that the road boundary...
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