Vehicle yaw, pitch, roll and 3D lane shape recovery by vision

Coulombeau, P.; Laurgeau, Claude

doi:10.1109/ivs.2002.1188020

Cited by 27 publications

(28 citation statements)

References 2 publications

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“…4 (a). In classical algorithms only one lane is detected [7], [XI, [9], [14]. We propose to detect every visible lane at the same time by classifying data into different groups and then estimate the road shape for each one of them.…”

Section: T H E Multi-lane Detectionmentioning

confidence: 99%

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A new multi-lanes detection using multi-camera for robust vehicle location

Ieng

Vrignon²,

Gruyer

et al. 2005

IEEE Proceedings. Intelligent Vehicles Symposium, 2005.

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Abstract-This paper deals with a new multi-lane markings detection and tracking system. The proposed system uses multiple cameras positioned difIerently in order to reduce different kind of perturbations, such as light sensitivity. The algorithm combines Robust Kalman Filtering and association based on belief theory to achievc multi-object tracking. Thus, the system provides the ability to track lane markings without any assumption on their number. It also proposes a new lane change management. To study this new system, the algorithm has been implemented on an embedded computer equipped With multiple cameras. We present experimental results obtained on a track. These results allow us to show important advantages of this new system and its robustness by comparing it to a classical system. Index Terms-Imaging and Vision, Robust Estimation, Lane Detection, Road Modelling, Classification, Multi-Object Tracking, Kalman Filter.The Fig. I gives an overview of the system architecture. In section IT, we will present the multi-camera data fusion system. The main idea of multi-lane marking detection and tracking will be given in section I11 and section IV respectivelly, and finally we will discuss the results of our new system in section V where we will compare these results with the results obtained from a classical one lane detection system.

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Section: T H E Multi-lane Detectionmentioning

confidence: 99%

“…For this reason the system must be as robust as possible. There are still many recent studies on such systems [8], [IO], [9], 1151, [14]. These works are mainly interested in the robustness and the accuracy of such a system.…”

Section: Introductionmentioning

confidence: 99%

A new multi-lanes detection using multi-camera for robust vehicle location

Ieng

Vrignon²,

Gruyer

et al. 2005

IEEE Proceedings. Intelligent Vehicles Symposium, 2005.

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show abstract

“…However, these features alone do not perform well in different light conditions or occlusions. Others predefine the road model and then use a Bayes filter to estimate the state [11], [12], [13], [14]. This method assumes that the lane has constant width on a flat plane and works well in these common cases.…”

Section: Introductionmentioning

confidence: 99%

A lane marking extraction approach based on Random Finite Set Statistics

Zhang

Stähle

Chen

et al. 2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-Within the past few years, lane detection technology has become of high interest in the field of intelligent vehicles; however, robustness is still an issue. The challenge is to extract the lane markings effectively from the complex urban environment. In this paper, we present a novel approach based on Random Finite Set Statistics for estimating the position of lane markings. We rely on Probability Hypothesis Density (PHD) filtering and apply this technique to lane marking extraction in urban environment. Our method is based on two phases: an image preprocessing phase to extract pixels that potentially represent lanes and a tracking phase to identify lane markings. Compared to other approaches, our method presents a recursive filtering algorithm which extracts lane markings in the presence of clutter and non-lane markings.The experimental results exhibit the high performance of the proposed approach under various scenarios.

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“…Applications such as obstacle avoidance, pedestrian detection, or traffic signal recognition, could both speed up the whole process and make use of additional information by a precise estimation of the current camera extrinsic parameters, related to the road. Most of recent works (e.g., Bertozzi et al, 2003b;Coulombeau & Laurgeau, 2002;Liang et al, 2003;Ponsa et al, 2005;Labayrade & Aubert, 2003) assume, or impose, a scene prior knowledge to simplify the problem. Although prior knowledge has been extensively used to tackle the driver assistance problem, it should be carefully used since it may lead to wrong results.…”

Section: Introductionmentioning

confidence: 99%