Vision-based lane departure warning framework

Abstract

Collisions arising from lane departures have contributed to traffic accidents causing millions of injuries and tens of thousands of casualties per year worldwide. Many related studies had shown that single vehicle lane departure crashes accounted largely in road traffic deaths that results from drifting out of the roadway. Hence, automotive safety has becoming a concern for the road users as most of the road casualties occurred due to driver's fallacious judgement of vehicle path. This paper proposes a vision-based lane departure warning framework for lane departure detection under daytime and night-time driving environments. The traffic flow and conditions of the road surface for both urban roads and highways in the city of Malacca are analysed in terms of lane detection rate and false positive rate. The proposed vision-based lane departure warning framework includes lane detection followed by a computation of a lateral offset ratio. The lane detection is composed of two stages: pre-processing and detection. In the pre-processing, a colour space conversion, region of interest extraction, and lane marking segmentation are carried out. In the subsequent detection stage, Hough transform is used to detect lanes. Lastly, the lateral offset ratio is computed to yield a lane departure warning based on the detected X -coordinates of the bottom end-points of each lane boundary in the image plane. For lane detection and lane departure detection performance evaluation, real-life datasets for both urban roads and highways in daytime and night-time driving environments, traffic flows, and road surface conditions are considered. The experimental results show that the proposed framework yields satisfactory results. On average, detection rates of 94.71% for lane detection rate and 81.18% for lane departure detection rate were achieved using the proposed frameworks. In addition, benchmark lane marking segmentation methods and Caltech lanes dataset were also considered for comparison evaluation in lane detection. Challenges to lane detection and lane departure detection such as worn lane markings, low illumination, arrow signs, and occluded lane markings are highlighted as the contributors to the false positive rates.

Sharpening and Detection of Road Studs for Intelligent Vehicles at Nighttime

“…However, the detection of road lanes is missed in the dark time because the street lights on the road are required for the visibility of road lanes in the dark. A vision-based approach was proposed by [18] for lane detection against daytime and nighttime, respectively. For lane detection, the flow of traffic and situations of the road surface against urban and highways are assessed.…”

A real‐time lane detection network using two‐directional separation attention

“…LaLi+VPR achieves an average accuracy of 93.88%, but it is still 5.095% lower than TSA‐LNet. Vision‐based lane detection (VBLD) (Em et al., 2019) is another traditional method based on Hough transform. VBLD involves four stages, including color space transformation, region of interest extraction, lane segmentation, and lane detection by adopting Hough transform.…”

Lane Departure Warning Estimation Using Yaw Acceleration

“…The recommendation for future work is to reduce the dependency of vehicle state information like yaw acceleration response for the application of LDWS. A vehicle state information alone is unable to overcome the limitations of environmental factors, such as rainy conditions, drastic change of illuminations conditions like night-times, nonstructural road, and clutters/shadow noise on road surface [27]. Alternatively, vision data can be fused with a local sensor like the yaw rate sensor for enhancing the LDWS reliability, particularly in urban traffic situations with a variety of road signs printed on the road background.…”