Review of Lane Detection and Tracking Algorithms in Advanced Driver Assistance System

Kumar, Ammu M.; Simon, Philomina

doi:10.5121/ijcsit.2015.7406

Cited by 44 publications

(14 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…True-negative (TN) means that the algorithm does not decide a lane mark while there is not a ground truth lane in reality. False-negative (FN) is the case where the algorithm does not detect any lane marking but ground truth data exist for this decision [22]. In this work, correct lane detection decision is given when the distance between detected lane marking and ground truth data points within a defined interval.…”

Section: Lane Detection Resultsmentioning

confidence: 99%

Real-time illumination and shadow invariant lane detection on mobile platform

Küçükmanisa

Tarım

Urhan

2017

J Real-Time Image Proc

View full text Add to dashboard Cite

In this work, a novel lane detection method using a single input image is presented. The proposed method adopts a color and shadow invariant preprocessing stage including a feature region detection method called as maximally stable extremal regions. Next, candidate lane regions are examined according to their structural properties such as width-height ratio and orientation. This stage is followed by a template matching-based approach to decide final candidates for lane markings. At the final stage of the proposed method, outliers are eliminated using the random sample consensus approach. The proposed method is computationally lightweight, and thus, it is possible to execute it in real-time on consumer-grade mobile devices. Experimental results show that the proposed method is able to provide shadow, illumination and road defects invariant performance compared to the existing methods.

show abstract

Section: Lane Detection Resultsmentioning

confidence: 99%

Real-time illumination and shadow invariant lane detection on mobile platform

Küçükmanisa

Tarım

Urhan

2017

J Real-Time Image Proc

View full text Add to dashboard Cite

show abstract

“…Among the traditional lane line detection methods, some works of literature [13][14][15] have made a comprehensive summary of it summarized that the traditional lane line detection methods [16] are usually divided into three steps: image pre-processing, local feature extraction, and lane line fitting. Among them, local feature extraction is to capture local lane line information by using edge [17], texture [18], and color [19] features on the region of interest (ROI) [20] in the image, which is a key step of traditional lane line detection methods.…”

Section: Related Workmentioning

confidence: 99%

Lane Line Detection Based on Object Feature Distillation

Haris

Głowacz

2021

Electronics

View full text Add to dashboard Cite

In order to meet the real-time requirements of the autonomous driving system, the existing method directly up-samples the encoder’s output feature map to pixel-wise prediction, thus neglecting the importance of the decoder for the prediction of detail features. In order to solve this problem, this paper proposes a general lane detection framework based on object feature distillation. Firstly, a decoder with strong feature prediction ability is added to the network using direct up-sampling method. Then, in the network training stage, the prediction results generated by the decoder are regarded as soft targets through knowledge distillation technology, so that the directly up-samples branch can learn more detailed lane information and have a strong feature prediction ability for the decoder. Finally, in the stage of network inference, we only need to use the direct up-sampling branch instead of the forward calculation of the decoder, so compared with the existing model, it can improve the lane detection performance without additional cost. In order to verify the effectiveness of this framework, it is applied to many mainstream lane segmentation methods such as SCNN, DeepLabv1, ResNet, etc. Experimental results show that, under the condition of no additional complexity, the proposed method can obtain higher F1Measure on CuLane dataset.

show abstract

“…The lane markers along the roads must be properly detected and understood. However these lane markers tend to become unclear to the road users who are driving vehicles along the road when the weather changes [1,2] especially for advanced driver assistance systems [3][4][5] Road Safety Research (MIROS) in 2016 has shown that 156 drivers out of 44614 drivers had made illegal overtaking along roads with double solid road marking [6]. Foggy, snowy and rainy weather conditions cause difficulties on the road users to clearly detect the lane and correctly perceive the types of the lane markers and have turned out to be among the major factors that cause road crashes [7].…”

Section: Introductionmentioning

confidence: 99%

Advances in lane marking detection algorithms for all-weather conditions

Ghani

Besar

Sani

et al. 2021

IJECE

View full text Add to dashboard Cite

Driving vehicles in all-weather conditions is challenging as the lane markers tend to be unclear to the drivers for detecting the lanes. Moreover, the vehicles will move slower hence increasing the road traffic congestion which causes difficulties in detecting the lane markers especially for advanced driving assistance systems (ADAS). Therefore, this paper conducts a thorough review on vision-based lane marking detection algorithms developed for all-weather conditions. The review methodology consists of two major areas, which are a review on the general system models employed in the lane marking detection algorithms and a review on the types of weather conditions considered for the algorithms. Throughout the review process, it is observed that the lane marking detection algorithms in literature have mostly considered weather conditions such as fog, rain, haze and snow. A new contour-angle method has also been proposed for lane marker detection. Most of the research work focus on lane detection, but the classification of the types of lane markers remains a significant research gap that is worth to be addressed for ADAS and intelligent transport systems.

show abstract

Review of Lane Detection and Tracking Algorithms in Advanced Driver Assistance System

Abstract: ABSTRACT

Cited by 44 publications

References 27 publications

Real-time illumination and shadow invariant lane detection on mobile platform

Real-time illumination and shadow invariant lane detection on mobile platform

Lane Line Detection Based on Object Feature Distillation

Advances in lane marking detection algorithms for all-weather conditions

Contact Info

Product

Resources

About