A novel algorithm of lane detection addressing varied scenarios of curved and dashed lanemarks

Suddamalla, Upendra; Kundu, Saikat; Farkade, Sameer; Das, Apurba

doi:10.1109/ipta.2015.7367103

Cited by 27 publications

(15 citation statements)

References 7 publications

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“…The particular suggested technique provides efficient results in several illumination situations such as in bad weather conditions as well as at night time. Jianwei Niu et al 2015 [15] Lane Detection with Two-stage Feature Extraction is presented in this particular paper to detect lanes. A modified Hough Transform is applied to extract small line segments of the lane contour and then DBSCAN(Density Based Spatial Clustering of Applications with Noise) clustering algorithm is used to divide the segments into clusters.…”

Section: Related Workmentioning

confidence: 99%

A Review on the Artificial Bee Colony & Clahe Based Hybrid Hough Transform For Efficient Lane Detection

Multani

Marwaha²

2017

IJECS

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This paper represents that lane coloration has become popular in real time vehicular ad-hoc system. Lane detection is normally helpful to localize path limits, determine undesired lane variations, and to enable approximation of the upcoming geometry of the road. There are different types of methods that are used for detecting lines, curves and ellipses i.e. hough transform, clustering and curve fitting. The overall objective of this paper is to providing better performance in lane coloration algorithm. The methods developed till now are working effectively as well as giving good results.

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Section: Related Workmentioning

confidence: 99%

A Review on the Artificial Bee Colony & Clahe Based Hybrid Hough Transform For Efficient Lane Detection

Multani

Marwaha²

2017

IJECS

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“…Ali et al [4] inspired from Zhou et al [8] proposed to take input from lane boundary for correcting minute position differences in ground truth. Here we propose to use structure and shape details of the lane marking which can be derived from spatial information of the lane marking to handle the noises [9].…”

Section: Lbc: Lane Boundary Correspondencementioning

confidence: 99%

M2BMT: An Automated Ground Truth Generation Algorithm for Lane Detection

Das

Srinivas

Suddamalla

2016

International Conference on Pattern Recognition Systems (ICPRS-16)

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Safety is one of the primary requirements of automotive manufactures and buyers and regulatory bodies are supporting this by mandating the safety features in vehicles. To achieve safety, multiple sensors such as vision cameras, radars, LIDAR and ultrasound devices are installed in the vehicle at various locations and sensor data is processed continuously using advanced algorithms. Validation of these algorithms is a critical requirement to ensure quality of the system. The present paper proposes an enhancement of the ground truth determination for automated lane detection system. The approach of time-slicing [1] has been built up on the binary framework. But the classical binarization algorithms have some limitations to address the particular domain of lane detection in an unconstrained environment and varied scenarios such as curvy and dashed lane marks. The current paper proposes a novel binarization algorithm based on min-between-max thresholding (MBMT). The adaptive binarization addresses the issue of outlier rejection in an efficient way and handles the effect of shadow, illumination variation and other factors in time-sliced images automatically. In addition the current work identifies limitation of the classical timeslice (TS) based approach even with time MBMT (TMBMT) for ground truth determination and addresses the same through second level of adaptation by spatial MBMT (SMBMT). Finally a complete mathematical model is presented to determine ground truth through the said method of hybrid or Modified MBMT or M 2 BM T . Technology disclosed in this paper is subject matter of pending patent application.

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“…Several lane detection algorithms are proposed based on low level features such as edge and curve fitting [2], shape and structure of lane [3], histogram based [4] and lane models [2]. Performance of the lane detection algorithms vary based on the image quality and illumination levels.…”

Section: A D V a N C E S I N I M A G E A N D V I D E O P R O C E S S mentioning

confidence: 99%

“…This algorithm can use output from any state-of-art lane detection algorithm, provided the algorithm gives accurate position and start, end points of the lane in given scene. In the current work, we have used the mask based approach for lane detection as explained in [3]. This algorithm uses edge and lane mask as the key features and is capable of detecting lanes in both straight road and curvy road as shown in Fig.2.…”

Section: Lane Detectionmentioning

confidence: 99%

Lane Characterization Under Challenging Scenarios for Autonomous Driving System

Suddamalla

Singal

Mohan

2016

AIVP

Self Cite

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Advanced Driver Assistance systems (ADAS) have seen increasing popularity due to their importance in automotive driver safety and autonomous driving. These systems analyze data from several sensors mounted on the vehicle to detect lanes, obstacles and traffic conditions to ensure safe driving. Lane markings on road with different color and structure provide information on safe drive zone and other traffic restrictions on road. Typical ADAS solutions depend on vision based sensors for lane detection.Here we propose an efficient algorithm for detecting type and color of the lane marks as this information plays critical role in taking the decision for safety features such as lane change and lane keep assist. Our algorithm is pluggable to any state-of-art lane detection algorithm and provides lane type and color for straight, curvy roads. The proposed method is tested on various challenging scenarios and results are promising.

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A novel algorithm of lane detection addressing varied scenarios of curved and dashed lanemarks

Cited by 27 publications

References 7 publications

A Review on the Artificial Bee Colony & Clahe Based Hybrid Hough Transform For Efficient Lane Detection

A Review on the Artificial Bee Colony & Clahe Based Hybrid Hough Transform For Efficient Lane Detection

M2BMT: An Automated Ground Truth Generation Algorithm for Lane Detection

Lane Characterization Under Challenging Scenarios for Autonomous Driving System

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