Data fusion based lane departure warning framework using fuzzy logic

Abstract

Background: Lane detection is a difficult issue because of different lane circumstances. It plays an important part in advanced driver assistance systems, which give information about the centre of a host vehicle such as lane structure and lane position. Lane departure warning (LDW) is used to warn the driver about an unplanned lane exit from the original lane. The objective of this study was to develop a data-fusion LDW framework to improve the rate of detection of lane departure during daylight and at night. Methods: Vision-based LDW is a comprehensive framework based on vision-based lane detection with additional lateral offset ratio computations based on the detected X12 and X22 coordinates. The computed lateral offset ratio is used to detect lane departure based on predefined LDW identification criteria for vision-based LDW. Data fusion-based LDW was developed using a multi-input-single-output fuzzy logic controller. Data fusion involved lateral offset ratio and yaw acceleration response from the vision-based LDW and model-based vehicle dynamics frameworks. Real-life datasets were generated for simulation under the MATLAB Simulink platform. Results: Experimental results showed that fusion-based LDW achieved an average lane departure detection rate of 99.96% and 98.95% with false positive rates (FPR) of 0.04% and 1.05% using road footage clips #5–#27 in daytime and night-time, respectively. The average FPR using data fusion-based LDW reduced by 18.83% and 15.22% compared to vision-based LDW in daytime and night-time, respectively. Conclusions: The data fusion-based LDW is a novel way of reducing false lane departure detection by fusing two types of modalities to determine the correct lane departure information. The limitation is the constant warning threshold value used in the current implementation of LDW in the vision-based LDW framework. An adaptive mechanism of warning threshold taking various road structures into account could be developed to improve lane departure detection.