Increased Accuracy Stereo Approach for 3D Lane Detection

Nedevschi, Sergiu; Oniga, Florin; Danescu, Radu; Graf, T.; Schmidt, Rolf

doi:10.1109/ivs.2006.1689603

Cited by 27 publications

(15 citation statements)

References 9 publications

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“…For instance, several works address sub-pixel accuracy lane markings models [3][4][5] using calibration information. Others use image alignment between the stereo pair to detect volumetric objects on the road plane [6,7], or to enhance lane marking detection [8].…”

Section: Introductionmentioning

confidence: 99%

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Nieto

Laborda

Salgado

2010

Machine Vision and Applications

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Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance-cost ratio, mono-camera systems are arising as the main focus of this field of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectified image of the road plane. This rectified plane is used by a recursive Bayesian classifier, which classifies pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios.

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Section: Introductionmentioning

confidence: 99%

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Nieto

Laborda

Salgado

2010

Machine Vision and Applications

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“…1: first an Inverse Perspective Mapping (IPM) transformation [6], [7] is applied to the frame grabbed by the camera, then a low-level filtering is performed to highlight the Dark-LightDark [8] (DLD) patterns of the image; the resulting points are grouped together, and clusters are finally approximated by continuous piecewise-linear functions. Once the low-level processing is over, the resulting segment lists are compared to existing lane markings in a tracking stage (Fig.…”

Section: Algorithmmentioning

confidence: 99%

Robust monocular lane detection in urban environments

Felisa

Zani

2010

2010 IEEE Intelligent Vehicles Symposium

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Abstract-An effective lane detection algorithm is a basic, yet fundamental component of both autonomous navigation and advanced road safety systems; this paper presents an approach that produces reliable results exploiting a robust polyline matching technique. The proposed solution has been designed from the ground up so that only very limited hardware resources are required: just one camera is used, and the processing is fast enough to be compatible with mainstream DSP units.

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“…The integration of a vision system with active sensors is useful because the two technologies are complementary, and in some ways redundant: while the problem of lane detection is mainly addressed using vision [2] [3] [4], obstacle detection can get many benefits from an intelligent fusion with scan data. This topic will be discussed in section III.…”

Section: Introductionmentioning

confidence: 99%

The passive sensing suite of the TerraMax autonomous vehicle

Broggi

Cappalunga

Caraffi

et al. 2008

2008 IEEE Intelligent Vehicles Symposium

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Abstract-This paper presents the TerraMax autonomous vehicle, which competed in the DARPA Urban Challenge 2007. The sensing system is mainly based on passive sensors, in particular four vision subsystems are used to cover a 360• area around the vehicle, and to cope with the problems related to complex traffic scenes navigation. A trinocular system derived from the one used during the 2005 Grand Challenge performs obstacle and lane detection, twin stereo systems (one in the front and one in the back) monitor the area close to the truck, two lateral cameras detect oncoming vehicles at intersections, and a rear view system monitors the lanes next to the truck looking for overtaking vehicles. Data fusion between laserscanners and vision will be discussed, focusing on the benefits of this approach.

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Increased Accuracy Stereo Approach for 3D Lane Detection

Abstract: Abstract

Cited by 27 publications

References 9 publications

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Robust monocular lane detection in urban environments

The passive sensing suite of the TerraMax autonomous vehicle

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