Off-Road Lane Detection Using Superpixel Clustering and RANSAC Curve Fitting

Agrawal, Sanskar; Deo, Indu Kant; Haldar, Siddhant; Kiran, G Rahul Kranti; Lodhi, Vaibhav; Chakravarty, Debashish

doi:10.1109/icarcv.2018.8581155

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Therefore, we do the grey processing (rgb2grey greyscale conversion) before utilising other image operations, such as filtering, threshold segmentation, and edge detection. Similar to [12, 38], for the grey processing phase of our lane detection task, we adopt a commonly used psychology conversion formula (1), which is obtained by using a linear weight combination of red, green and blue channels in RGB colour space.

({, \begin{aligned} V_{grey} = 0.3 R + 0.59 G + 0.114 B \\ R = G = B = V_{grey} \end{aligned})

…”

Section: Lane Detectionmentioning

confidence: 99%

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

Xiong

Liu

et al. 2020

IET intell. transp. syst

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({, \begin{aligned} V_{grey} = 0.3 R + 0.59 G + 0.114 B \\ R = G = B = V_{grey} \end{aligned})

…”

Section: Lane Detectionmentioning

confidence: 99%

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

Xiong

Liu

et al. 2020

IET intell. transp. syst

View full text Add to dashboard Cite

“…However, due to the time-varying, complexity, nonlinearity, and uncertainty, it is normally hard to obtain accurate mathematical lane models [15]. For the purpose of increasing the calculation speed, RANSAC (RANdom SAmple Consensus) algorithm was used in many types of research to eject most of the outliers in the feature matching step [16].…”

Section: Related Workmentioning

confidence: 99%

A Design of Cooperative Overtaking Based on Complex Lane Detection and Collision Risk Estimation

et al. 2019

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Cooperative overtaking is believed to have the capability of improving road safety and traffic efficiency by means of the real-time information exchange between traffic participants, including road infrastructures, nearby vehicles and others. In this paper, we focused on the critical issues of modeling, computation, and analysis of cooperative overtaking and made it playing a key role in the road overtaking area. In detail, for the purpose of extending the awareness of the surrounding environment, the lane markings in front of ego vehicle were detected and modeled with Bezier curve using an onboard camera. While the nearby vehicle positions were obtained through the vehicle-to-vehicle communication scheme making assure of the accuracy of localization. Then, Gaussian-based conflict potential field was proposed to guarantee the overtaking safety, which can quantitatively estimate the oncoming collision danger. To support the proposed method, many experiments were conducted on the human-in-the-loop simulation platform. The results demonstrated that our proposed method achieves better performance, especially in some unpredictable nature road circumstances.

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Design and Implementation of Autonomous Ground Vehicle for Constrained Environments

Kiran¹,

Deo²,

Agrawal³

et al. 2019

2019 Third IEEE International Conference on Robotic Computing (IRC)

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Off-Road Lane Detection Using Superpixel Clustering and RANSAC Curve Fitting

Cited by 3 publications

References 10 publications

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

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

A Design of Cooperative Overtaking Based on Complex Lane Detection and Collision Risk Estimation

Design and Implementation of Autonomous Ground Vehicle for Constrained Environments

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