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Cited by 54Autonomous Vehicle Technology and SafetyAdvanced Vision and ImagingVehicle Dynamics and Control Systems

The DOI matches its Crossref record, with a small difference in title or year.

Vision-based lane recognition under adverse weather conditions using optical flow

Gern, A., Moebus, R., Franke, U.·2003·Intelligent Vehicle Symposium, 2002. IEEE
DOI 10.1109/ivs.2002.1188025
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Abstract

Lane recognition is the basis for many driver assistance systems, including lane departure warning (LDW), the assignment of vehicles to specific lanes and fully autonomous driving. A major problem of common vision-based lane recognition systems is their susceptibility to weather. Especially when driving in adverse weather conditions such as rain or snow it is difficult to estimate the road course. The contrast between the white lane markings and the pavement is poor, sometimes the colors of the markings are negated. Furthermore the range of sight is reduced enormously causing a bad prediction of the lane parameters, particularly the curvature. We present a solution which relies not only on finding white markings. Correlating structures parallel to the road over time the horizontal optical flow is calculated. It is then integrated in the lane recognition system estimating the position of the vehicle within the lane and the curvature parameters of the road ahead. The system allows to assign obstacles detected by radar or vision reliably to specific lanes and to drive laterally controlled autonomously even under adverse weather conditions.

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Research works printed but not linked: 17

  1. 10.1109/ITSC.1997.660582 · DOI
  2. gem, 2001, Robust Lane Recognition Using Vision and DGPS Road Course Information, Proceedings of the IEEE Conference of Intelligent Vehicles
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  5. goldbeck, 1998, Lane Following Combining Vision and DGPS, Proc IEEE Conference on lnteliigent vehicles, 445
  6. 10.1109/IVS.2000.898354 · DOI
  7. aschwunden, 1993, Experimenteller Vergleich von Korrelationskriterien in der Bildanalyse
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  11. 10.1109/IVS.1995.528257 · DOI
  12. 10.1109/IVS.1995.528333 · DOI
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  14. 10.1109/ITSC.1997.660541 · DOI
  15. dickmanns, 1986, A curvature-based scheme for improved road vehicle guidance by computer vision, Proc SPIE Conference on Mobile Robots, 161
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  17. 10.1109/ROBOT.1998.680587 · DOI

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Metadata

Authors
Gern, A., Moebus, R., & Franke, U.
Affiliations
Daimler (Germany) (DE)
Venue
Intelligent Vehicle Symposium, 2002. IEEE
Publisher
IEEE
Published
2003-10-01
Volume
2
Pages
652-657
DOI
10.1109/ivs.2002.1188025
OpenAlex
W1629522576
Cited by
54
References
0
Citations by year
2025: 2 · 2024: 1 · 2023: 1 · 2021: 1 · 2020: 1 · 2019: 3 · 2018: 5 · 2017: 3 · 2016: 3 · 2014: 3 · 2013: 1 · 2012: 3
FWCI
5.82
Type
conference paper
Language
en
Open access
closed
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
  • Physical Sciences › Engineering › Automotive Engineering › Vehicle Dynamics and Control Systems
Keywords
lane detection, adverse weather, optical flow, lane departure warning, road curvature estimation, lane markings, autonomous driving, lateral control, driver assistance systems

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CoChat. (2026). Citation check: Vision-based lane recognition under adverse weather conditions using optical flow. https://app.cochat.ai/papers/10.1109/ivs.2002.1188025
The paper
Gern, A., Moebus, R., & Franke, U. (2003). Vision-based lane recognition under adverse weather conditions using optical flow. Intelligent Vehicle Symposium, 2002. IEEE. https://doi.org/10.1109/ivs.2002.1188025