Research Papers

FLane: An Adaptive Fuzzy Logic Lane Tracking System for Driver Assistance

[+] Author and Article Information
Giulio Reina1

Department of Engineering for Innovation, University of Salento, Via per Arnesano, 73100 Lecce, Italygiulio.reina@unisalento.it

Annalisa Milella

Institute of Intelligent Systems for Automation, National Research Council, Via G. Amendola 122/D, 70126 Bari, Italymilella@ba.issia.cnr.it


Corresponding author.

J. Dyn. Sys., Meas., Control 133(2), 021002 (Feb 11, 2011) (11 pages) doi:10.1115/1.4003091 History: Received July 09, 2008; Revised July 23, 2010; Published February 11, 2011; Online February 11, 2011

In the last few years, driver assistance systems are increasingly being investigated in the automotive field to provide a higher degree of safety and comfort. Lane position determination plays a critical role toward the development of autonomous and computer-aided driving. This paper presents an accurate and robust method for detecting road markings with applications to autonomous vehicles and driver support. Much like other lane detection systems, ours is based on computer vision and Hough transform. The proposed approach, however, is unique in that it uses fuzzy reasoning to combine adaptively geometrical and intensity information of the scene in order to handle varying driving and environmental conditions. Since our system uses fuzzy logic operations for lane detection and tracking, we call it “FLane.” This paper also presents a method for building the initial lane model in real time, during vehicle motion, and without any a priori information. Details of the main components of the FLane system are presented along with experimental results obtained in the field under different lighting and road conditions.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 1

Driver assistance systems that require lane position: (a) lane-departure warning, (b) driver-attention monitoring, and (c) vehicle-control

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Figure 2

Sample images of road markings and conditions: (a) simple road with solid lane marking, (b) disturbances due to curb and manhole cover, (c) transversal solid lines due to side road enters, (d) nonuniform pavement texture, (e) freeway overpass causing lighting change and reducing road-marking contrast, and (f) low lighting and shadowing at night time

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Figure 3

Model of the lane marking in the image plane. Note that the parameters ρ1, ρ2, and ρ are expressed in pixels.

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Figure 4

Model of the lane marking in the real world. Note that the distances d1 and d2 are expressed in millimeters.

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Figure 5

Membership function of the intensity indicator. If the degree of membership is greater than a threshold T (T=0.7 in our case), then the pixel is accepted, and it is set to 1 (white), otherwise it is disregarded, and it is set to 0 (black).

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Figure 9

Lane detection for a sample image where extraneous transversal white road markings are present: (a) output of the FED module and (b) indication of the lane marker candidates

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Figure 10

DMB: input and output membership functions

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Figure 11

Conceptual scheme of the CHM

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Figure 15

Example of false positive due the presence of a manhole cover: (a) grey lines (red lines in the online version of the paper): erroneous lane marker estimated by the FLane system and black lines: lane candidates; and (b) output of the FED module

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Figure 16

Example of false negative due to poor image segmentation: (a) black lines: lane candidates; and (b) output of the FED module.

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Figure 6

Results of a sample image binarization using the fuzzy edge detection module: (a) selected ROI, (b) fuzzy thresholding using the Intensity Indicator, (c) Canny edge detection, and (d) Boolean AND of the two previous operations. Note that the negatives of the binary images are shown for visualization’s sake.

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Figure 7

Membership functions of the FLR module

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Figure 8

Fuzzy lane selection applied to a sample image. Five lines were selected forming ten lane marker candidates.

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Figure 12

Result of the DMB module applied to a sample sequence: (a) first frame of the sequence, (b) fifth frame of the sequence, (c) representation of the CHM, and (d) selected lane marker model. Images (a) and (b) report indication of the confidence assigned to the selected lane marker.

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Figure 13

Example of robust marker model building: (a)-(l) consecutive frames used for model building; (m) marker model obtained as output of the DMB module. Although the absence of the main lane marker in (d) or the presence of multiple lines in (e), (f) and results in misidentifications, the DMB module retains a correct model.

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Figure 14

The test bed used for experimental validation of the FLane system




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