Fast and robust approaches for lane detection using multi‐camera fusion in complex scenes

Xiong, Hui; Yu, Dingwen; Liu, Jinxin; Huang, Heye; Xu, Qing; Wang, Jianqiang; Li, Keqiang

doi:10.1049/iet-its.2019.0399

Cited by 5 publications

(5 citation statements)

References 42 publications

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“…A fast and robust approach for lane detection based on multi-source camera fusion system was developed by Xiong et al, (2020). A B-spline lane line was fitted based on the strength of the RANSAC algorithm for the front view image detection.…”

Section: Computer Vision-based Lane Detection and Classification Algo...mentioning

confidence: 99%

Evaluating the Impact of Lane Marking Quality on the Operation of Autonomous Vehicles

Formosa,

Quddus,

Man

et al. 2024

J. Transp. Eng., Part A: Systems

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The quality of lane markings is pivotal for safe operations and efficient trajectory generations of Connected and Autonomous Vehicles (AVs). However, most studies are devoted to enhancing invehicle detection systems and ignore the impact of faulty lane markings. An instrumented vehicle was employed to mimic the data input of an AV and real-world trials were conducted on (1) live motorways and (2) a controlled motorway facility. From the live motorway data, causal factors affecting computer vision lane detection and classification algorithms were examined and an enhanced lane classification algorithm was developed to overcome the limitations posed by poor lane markings. In the controlled motorway facility, experiments to modify the physical appearance of the lane markings were conducted to further test the performance of the developed algorithm. The detection rates of the developed lane classification algorithm were compared with the Lane Departure Warning (LDW) system already implemented in the vehicle. Findings revealed that the LDW system is accurate over 95% and 54% of time when lanes are faded by 50% and 75% respectively. Further testing on the quality of the lane markings was carried out virtually in such a way that the experiments were replicated in a simulation environment to: (1) identify lane marking conditions that can be reliably adopted for safe operations of AVs, (2) estimate the effect of adverse weather and lighting conditions on road markings detection and(3) address localisation issues for AVs. Simulation results show that poor lane markings have a significant negative impact on AV safety, especially in inclement weather and poor light conditions inducing an increase in conflicts, and delays. This can be compensated if more sophisticated sensors are employed in AVs, and the operators of road network develop lane-based digital road maps.

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Section: Computer Vision-based Lane Detection and Classification Algo...mentioning

confidence: 99%

Evaluating the Impact of Lane Marking Quality on the Operation of Autonomous Vehicles

Formosa,

Quddus,

Man

et al. 2024

J. Transp. Eng., Part A: Systems

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“…Line detection is a vast field of study as provided. CNN and RNN are used [4] to represent features and fusion of image processing methods. The LDTFE-based technique in [5] is better than the Line segment Detector method for lane detection.…”

Section: Literature Surveymentioning

confidence: 99%

A Smart System for Detection of Road Lane and Divider

Bhatlawande,

Shilaskar,

Patil

et al. 2023

IJRITCC

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Road Lane and divider detection is a core part in environmental perception for driver assistance system. The paper discusses about machine learning and computer vision approach to identify road lanes and dividers and proposes a system for detection of road lane and divider. Driving assistance systems heavily rely on the road lane and divider recognition. Voting classification was implemented using 7 different classifiers. The combination of Scale Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) provided feature extraction. Principle Component Analysis (PCA) provided dimension reduction. The performance has been examined on the basis of accuracy (95.50%), precision (78.81%), recall (65.71%), and F1 score (71.67%). The proposed solution is helpful in solving issues related to road safety and reducing road accidents.

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“…Generally, the lane detection methods can be divided into three categories: (1) traditional lane detection methods, (2) image processing method combined with deep learning, and (3) end-to-end lane line detection method. e traditional lane line detection method can be divided into three steps [7,8]. Firstly, the road image data is preprocessed to remove the noise and to obtain the lane line features; then, the lane line is detected from the preprocessed image by means of feature-based or model-based methods; finally, the detection results are fitted to convert the lane lines represented by image coordinates into world coordinates.…”

Section: Introductionmentioning

confidence: 99%

A Robust Detection Method for Multilane Lines in Complex Traffic Scenes

Song

Che²,

Jiang

et al. 2022

Mathematical Problems in Engineering

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The robustness and stability of lane detection is vital for advanced driver assistance vehicle technology and even autonomous driving technology. To meet the challenges of real-time lane detection in complex traffic scenes, a simple but robust multilane detection method is proposed in this paper. The proposed method breaks down the lane detection task into two stages, that is, lane line detection algorithm based on instance segmentation and lane modeling algorithm based on adaptive perspective transform. Firstly, the lane line detection algorithm based on instance segmentation is decomposed into two tasks, and a multitask network based on MobileNet is designed. This algorithm includes two parts: lane line semantic segmentation branch and lane line Id embedding branch. The lane line semantic segmentation branch is mainly used to obtain the segmentation results of lane pixels and reconstruct the lane line binary image. The lane line Id embedding branch mainly determines which pixels belong to the same lane line, thereby classifying different lane lines into different categories and then clustering these different categories. Secondly, the adaptive perspective transformation model is adopted. In this model, the motion information is used to accurately convert the original image into a bird’s-eye view image, and then the least-squares second-order polynomial fitting is performed on the lane line pixels. Finally, experiments on the CULane dataset show that the proposed method achieved similar or better performance compared with several state-of-the-art methods, the F1 score of the proposed method in the normal test set and most challenge test sets is better than other algorithms, which verifies the effectiveness of the proposed method, and then the field experiments results show that the proposed method has good practical application value in various complex traffic scenes.

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Fast and robust approaches for lane detection using multi‐camera fusion in complex scenes

Cited by 5 publications

References 42 publications

Evaluating the Impact of Lane Marking Quality on the Operation of Autonomous Vehicles

Evaluating the Impact of Lane Marking Quality on the Operation of Autonomous Vehicles

A Smart System for Detection of Road Lane and Divider

A Robust Detection Method for Multilane Lines in Complex Traffic Scenes

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