A novel approach to lane detection and tracking

Meuter, Mirko; Müller-Schneiders, Stefan; Mika, Adalbert; Hold, Stephanie; Nunn, Christian; Kummert, Anton

doi:10.1109/itsc.2009.5309855

Cited by 38 publications

(33 citation statements)

References 20 publications

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“…At its most basic level, lane keeping for driver assistance consists of locating lane markings, fitting the lane markings to a lane model, and tracking their locations temporally with respect to the ego-vehicle. Image descriptors reported in the literature for lane marking localization include adaptive thresholds [20], [21], steerable filters [4], [9], [22], ridges [23], edge detection, global thresholds, and top-hat filters [21]. In [24], a classifier-based lane marker detection is employed.…”

Section: A Lane Detection and Trackingmentioning

confidence: 99%

“…This is often done via a parabolic or cubic fitting of the lane markings to a parametric road model [20]. In [4], this is achieved via fitting an adaptive road template to the viewed data.…”

Section: A Lane Detection and Trackingmentioning

confidence: 99%

“…Lane tracking has often been implemented using Kalman filters, or variations such as the extended Kalman filter, which tend to work well for continuous structured roads [4], [9], [20], [22]. The state vector tracks the positions of the lane markings, heading, curvature, and the vehicle's lateral position [4].…”

Section: A Lane Detection and Trackingmentioning

confidence: 99%

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Integrated Lane and Vehicle Detection, Localization, and Tracking: A Synergistic Approach

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

162

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Abstract-In this paper, we introduce a synergistic approach to integrated lane and vehicle tracking for driver assistance. The approach presented in this paper results in a final system that improves on the performance of both lane tracking and vehicle tracking modules. Further, the presented approach introduces a novel approach to localizing and tracking other vehicles on the road with respect to lane position, which provides information on higher contextual relevance that neither the lane tracker nor vehicle tracker can provide by itself. Improvements in lane tracking and vehicle tracking have been extensively quantified. Integrated system performance has been validated on real-world highway data. Without specific hardware and software optimizations, the fully implemented system runs at near-real-time speeds of 11 frames per second.

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Section: A Lane Detection and Trackingmentioning

confidence: 99%

Section: A Lane Detection and Trackingmentioning

confidence: 99%

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Integrated Lane and Vehicle Detection, Localization, and Tracking: A Synergistic Approach

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

162

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“…Various vision based methods have been used for identification of lane markings. Image descriptors that have been successfully used for lane marking localization include steerable filters [16], [17], [2], adaptive thresholds [18], edge detection, global thresholds, and top hat filters [26].…”

Section: A Lane Detection and Trackingmentioning

confidence: 99%

“…This is often done via a parabolic or cubic fitting of the lane markings [18]. In [16], this is achieved via fitting an adaptive road template to the viewed data.…”

Section: A Lane Detection and Trackingmentioning

confidence: 99%

Improved vision-based lane tracker performance using vehicle localization

Sivaraman

Trivedi

2010

2010 IEEE Intelligent Vehicles Symposium

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Abstract-In this paper, we present improved lane tracking using vehicle localization. Lane markers are detected using a bank of steerable filters, and lanes are tracked using Kalman filtering. On-road vehicle detection has been achieved using an active learning approach, and vehicles are tracked using a Condensation particle filter. While most state-of-the art lane tracking systems are not capable of performing in high-density traffic scenes, the proposed framework exploits robust vehicle tracking to allow for improved lane tracking in high density traffic. Experimental results demonstrate that lane tracking performance, robustness, and temporal response are significantly improved in the proposed framework, while also tracking vehicles, with minimal additional hardware requirements.

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Application Challenges from a Bird's‐Eye View

Scaramuzza

López²,

Imiya³

et al. 2017

Computer Vision in Vehicle Technology

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Computer Vision in Vehicle Technology focuses on computer vision as on-board technology, bringing together fields of research where computer vision is progressively penetrating: the automotive sector, unmanned aerial and underwater vehicles. It also serves as a reference for researchers of current developments and challenges in areas of the application of computer vision, involving vehicles such as advanced driver assistance (pedestrian detection, lane departure warning, traffic sign recognition), autonomous driving and robot navigation (with visual simultaneous localization and mapping) or unmanned aerial vehicles (obstacle avoidance, landscape classification and mapping, fire risk assessment).

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A novel approach to lane detection and tracking

Cited by 38 publications

References 20 publications

Integrated Lane and Vehicle Detection, Localization, and Tracking: A Synergistic Approach

Integrated Lane and Vehicle Detection, Localization, and Tracking: A Synergistic Approach

Improved vision-based lane tracker performance using vehicle localization

Application Challenges from a Bird's‐Eye View

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