Recent progress in road and lane detection: a survey

Bar-Hillel, Aharon; Lerner, R.; Levi, Dan; Raz, Guy

doi:10.1007/s00138-011-0404-2

Cited by 676 publications

(372 citation statements)

References 42 publications

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“…Assuming that the proportion of noise points in all feature pointsisε,each feature subset contains s feature points.Then, to reach 95% credibility the minimum number of sample subset is [3]: (1) Randomly selected feature points assembled feature subset,assuming that feature subset contains n feature points, the curve parameter vector is , and the number of parameters is p.Each subset has a parameter vector initial value and repeats the following process:…”

Section: 3select the Best Subset Based On Lmed Squarementioning

confidence: 99%

Research on Lane Detection Technology Based on OPENCV

Xu¹,

Zhang²

2015

Proceedings of the 3rd International Conference on Mechanical Engineering and Intelligent Systems (ICMEIS 2015)

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Abstract. In view of the huge computing, poor anti-interference ability of traditional detection alogrithm, it does not meet the requirement of the vehicle system, for which this paper proposed a lane detection method based on OpenCV. Preprocessing image in the OpenCV environment, adopting LMedSquare(Least Median Square) idea to select the best subset combined with least squares method to picewise fitting the lane so that it realized automatic identification of lane. This algorithm is suitable for both straight and curve. Simulation shows that this algorithm has well real-time performance, accuracy and robustness. It can meet the requirements of the vehicle system.

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Section: 3select the Best Subset Based On Lmed Squarementioning

confidence: 99%

Research on Lane Detection Technology Based on OPENCV

Xu¹,

Zhang²

2015

Proceedings of the 3rd International Conference on Mechanical Engineering and Intelligent Systems (ICMEIS 2015)

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“…Road detection is a significant component for building an autonomous navigation system in self-driving vehicle applications [3]. With the development of computer vision, vision-based road detection models are widely investigated and can provide effective and economic solutions for road perception in an autonomous navigation system compared with other sensors like lidar.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Road-Direction Point Detection in Complex Environment

Cai¹,

Liu

Liu³

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARYA real-time road-direction point detection model is developed based on convolutional neural network architecture which can adapt to complex environment. Firstly, the concept of road-direction point is defined for either single road or crossroad. For single road, the predicted road-direction point can serve as a guiding point for a self-driving vehicle to go ahead. In the situation of crossroad, multiple road-direction points can also be detected which will help this vehicle to make a choice from possible directions. Meanwhile, different types of road surface can be classified by this model for both paved roads and unpaved roads. This information will be beneficial for a self-driving vehicle to speed up or slow down according to various road conditions. Finally, the performance of this model is evaluated on different platforms including Jetson TX1. The processing speed can reach 12 FPS on this portable embedded system so that it provides an effective and economic solution of road-direction estimation in the applications of autonomous navigation.

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“…A recent lane and road boundary detection survey (Hillel et al, 2012) explored a large body of research on lane detection, including methods using gradientbased feature detection (Samadzadegan et al, 2006;Nieto et al, 2008;Sawano and Okada, 2006), steerable filters (McCall and Trivedi, 2006), box filters (Huang et al, 2009;Wu et al, 2008), and learningbased lane pattern recognition (Cheng et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Map-based Lane and Obstacle-free Area Detection

2014

Proceedings of the 9th International Conference on Computer Vision Theory and Applications

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Abstract:With the emergence of intelligent Advanced Driving Assistance Systems (i-ADAS), the need for effective detection of vehicular surroundings is considered a necessity. The effectiveness of such systems directly depends on their performance in various environments such as rural and urban roads, and highways. Most of the current lane detection techniques are not suitable for urban roads with complex lane shapes and frequent occlusions. We propose a map-based lane detection approach which can robustly detect the lanes in urban and rural environments, and highways. We also present an algorithm for detecting obstacle-free areas in detected lanes based on the stereo depth maps of driving scenes. Experiments show that our approach reliably detects lanes and obstacle free areas within them, even in case of partially occluded or worn-off lane markers.

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Recent progress in road and lane detection: a survey

Cited by 676 publications

References 42 publications

Research on Lane Detection Technology Based on OPENCV

Research on Lane Detection Technology Based on OPENCV

Real-Time Road-Direction Point Detection in Complex Environment

Map-based Lane and Obstacle-free Area Detection

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