NDLI: Lane recognition on poorly structured roads-the bots dot problem in California

Content Provider	IEEE Xplore Digital Library
Author	Gehrig, S.K. Gern, A. Heinrich, S. Woltermann, B.
Copyright Year	2002
Description	Author affiliation: DaimlerChrysler AG, Stuttgart, Germany (Gehrig, S.K.; Gern, A.; Heinrich, S.; Woltermann, B.)
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 and poorly structured roads. 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. In addition we are recognizing reflective lane markers and bots dots. These measurements are 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 us to perform lane departure warning and to drive laterally controlled autonomously even under adverse weather conditions.
Starting Page	67
Ending Page	71
File Size	626691
Page Count	5
File Format	PDF
ISBN	0780373898
DOI	10.1109/ITSC.2002.1041190
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-09-06
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	US Department of Transportation Rain Road vehicles Space vehicles Remotely operated vehicles Vehicle driving Mobile robots Weather forecasting Snow Position measurement
Content Type	Text
Resource Type	Article

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