A Diverse Low Cost High Performance Platform for Advanced Driver Assistance System (ADAS) Applications

Viswanath, Prashanth; Chitnis, Kedar; Swami, Pramod; Mody, Mihir; Shivalingappa, Sujith; Nagori, Soyeb; Mathew, Manu; Desappan, Kumar; Jagannathan, Shyam; Poddar, Deepak; Jain, Arvind; Garud, Hrushikesh; Appia, Vikram; Mangla, Mayank; Dabral, Shashank

doi:10.1109/cvprw.2016.107

Cited by 13 publications

(2 citation statements)

References 7 publications

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“…In response to this problem, Lane Keeping Assistance (LKA) (Yoshida et al, 2015); Lane Departure Warning (LDW) (Haloi and Jayagopi, 2015; Narote et al, 2018; Viswanath et al, 2016); Lane Following (LF) (Jung and Kelber, 2004, Jung and Kelber, 2005b); Lateral Control (LC); Intelligent Cruise Control (ICC); Collision Warning (CW) (Meng and Spence, 2015), and Autonomous Vehicle Guidance (Vacek et al, 2007) have been called for by vehicle manufacturers to enhance vehicle safety.…”

Section: Introductionmentioning

confidence: 99%

Vision-based lane departure warning framework

Ping

Hossen

Imaduddin

et al. 2019

Heliyon

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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.

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Section: Introductionmentioning

confidence: 99%

Vision-based lane departure warning framework

Ping

Hossen

Imaduddin

et al. 2019

Heliyon

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“…In response to such stern problems, Lane Keeping Assistance (LKA) [14]; Lane Departure Warning (LDW) [15]- [17]; Lane Following (LF) [18], [19]; Lateral Control (LC); Intelligent Cruise Control (ICC); Collision Warning (CW) [20]; and Autonomous Vehicle Guidance [21] are called for by vehicle manufacturer industries to enhance the vehicle safety.…”

Section: Introductionmentioning

confidence: 99%

Experimental of Vision-based Lane Markings Segmentation Methods in Lane Detection Application

Ping¹,

Hossen²,

Imaduddin³

et al. 2019

JESTR

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Lane departure collisions have contributed into the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Hence, a vision-based lane detection framework (VBLD) is proposed to detect lane markings on the road for unindented lane departure. The proposed VBLD framework is composed of colour space conversion, region of interest, lane marking segmentation, Hough transformation and peak detection, reverse Hough transformation, and draw detected lines on original image. Besides, finite impulse response saturation autothreshold (FIRSA) lane marking segmentation method is also proposed for lane edges extraction. For performance evaluation on the proposed VBLD framework and proposed FIRSA lane markings segmentation method, real-life datasets of road footages are collected using an instrumented vehicle. The outputs of lane detection frames from Clip #1, #2, #3, and #4 involving variety of road conditions are evaluated using detection rate, false positive rate, and false negative rate assessment metrics where the number of frames are manually counted using visual inspection. Experimental results have shown the evidences of the proposed VBLD framework using proposed FIRSA lane markings segmentation method obtained satisfactory lane detection results compared to benchmark lane marking segmentation methods.

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