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Cited by 1,028Autonomous Vehicle Technology and SafetyVehicle Dynamics and Control SystemsAdvanced Neural Network Applications

Video-Based Lane Estimation and Tracking for Driver Assistance: Survey, System, and Evaluation

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McCall, J.C., Trivedi, M.M.·2006·IEEE Transactions on Intelligent Transportation Systems
DOI 10.1109/tits.2006.869595
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Abstract

Driver-assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively considered. It is therefore important to take a critical look at key aspects of these systems, one of which is lane-position tracking. It is for these driver-assistance objectives that motivate the development of the novel "video-based lane estimation and tracking" (VioLET) system. The system is designed using steerable filters for robust and accurate lane-marking detection. Steerable filters provide an efficient method for detecting circular-reflector markings, solid-line markings, and segmented-line markings under varying lighting and road conditions. They help in providing robustness to complex shadowing, lighting changes from overpasses and tunnels, and road-surface variations. They are efficient for lane-marking extraction because by computing only three separable convolutions, we can extract a wide variety of lane markings. Curvature detection is made more robust by incorporating both visual cues (lane markings and lane texture) and vehicle-state information. The experiment design and evaluation of the VioLET system is shown using multiple quantitative metrics over a wide variety of test conditions on a large test path using a unique instrumented vehicle. A justification for the choice of metrics based on a previous study with human-factors applications as well as extensive ground-truth testing from different times of day, road conditions, weather, and driving scenarios is also presented. In order to design the VioLET system, an up-to-date and comprehensive analysis of the current state of the art in lane-detection research was first performed. In doing so, a comparison of a wide variety of methods, pointing out the similarities and differences between methods as well as when and where various methods are most useful, is presented

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  • Fully automated driving will require a complete, semantic understanding of road structures and the ability to detect several lanes at long ranges.

    “These ADAS or ADS technologies have different criteria both in terms of task, detection distance and reliability rates, but fully automated driving will require a complete, semantic understanding of road structures and the ability to detect several lanes at long ranges [199].”

    From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [199]

    The Road Modeling section discusses lookahead distances for vehicle control and lane-departure warning, but does not say fully automated driving requires long-range detection of several lanes. The Common Assumptions and Comparative Analysis section discusses autonomous vehicle systems without specifying a need for complete semantic understanding of road structures. Searches for those requirements found no supporting passage in our copy; page numbers were unavailable. (LLM, conf=0.76)

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Authors
McCall, J. C., & Trivedi, M. M.
Affiliations
University of California San Diego (US)
ORCID
Trivedi, M.M.
Venue
IEEE Transactions on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2006-03
Volume
7
Issue
1
Pages
20-37
ISSN
1524-9050 (ISSN-L)
DOI
10.1109/tits.2006.869595
OpenAlex
W2131076267
Cited by
1,028
References
0
Citations by year
2026: 11 · 2025: 18 · 2024: 27 · 2023: 25 · 2022: 36 · 2021: 44 · 2020: 43 · 2019: 53 · 2018: 80 · 2017: 64 · 2016: 60 · 2015: 72 · 2014: 92 · 2013: 91 · 2012: 77
FWCI
95.55
Type
journal article
Language
en
Open access
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Engineering › Automotive Engineering › Vehicle Dynamics and Control Systems
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
lane detection, lane tracking, driver assistance systems, steerable filters, curvature sensing, evaluation metrics

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