Lane Detection and Estimation using Perspective Image

Batista, Marcos Paulo; Shinzato, Patrick Y.; Wolf, Denis F.; Gomes, Diego

doi:10.1109/sbr.lars.robocontrol.2014.43

Cited by 15 publications

(5 citation statements)

References 5 publications

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“…(2016) present the lane-keeping design and implementation of an automated and electric go-cart. Focusing on AV visual abilities, Batista et al. (2015) come up with a novel method to detect and estimate lanes, which relies on the road image captured by a monocular camera.…”

Section: Resultsmentioning

confidence: 99%

Mind the gap: Developments in autonomous driving research and the sustainability challenge

Mora

Guo

Panori³

2020

Journal of Cleaner Production

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Scientific knowledge on autonomous-driving technology is expanding at a faster-than-ever pace. As a result, the likelihood of incurring information overload is particularly notable for researchers, who can struggle to overcome the gap between information processing requirements and information processing capacity. We address this issue by adopting a multi-granulation approach to latent knowledge discovery and synthesis in large-scale research domains. The proposed methodology combines citation-based community detection methods and topic modelling techniques to give a concise but comprehensive overview of how the autonomous vehicle (AV) research field is conceptually structured. Thirteen core thematic areas are extracted and presented by mining the large data-rich environments resulting from 50 years of AV research. The analysis demonstrates that this research field is strongly oriented towards examining the technological developments needed to enable the widespread rollout of AVs, whereas it largely overlooks the wide-ranging sustainability implications of this sociotechnical transition. On account of these findings, we call for a broader engagement of AV researchers with the sustainability concept and we invite them to increase their commitment to conducting systematic investigations into the sustainability of AV deployment. Sustainability research is urgently required to produce an evidence-based understanding of what new sociotechnical arrangements are needed to ensure that the systemic technological change introduced by AV-based transport systems can fulfill societal functions while meeting the urgent need for more sustainable transport solutions.

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Section: Resultsmentioning

confidence: 99%

Mind the gap: Developments in autonomous driving research and the sustainability challenge

Mora

Guo

Panori³

2020

Journal of Cleaner Production

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“…Detection and tracking of lane marking is essential for driving safety and intelligent vehicle [5], [6]. Offline road understanding and Lane detection algorithms are generally composed of multiple modules: image preprocessing, feature extraction and model-fitting [7], [8], [9], [10]. Low-level feature extraction (feature level processing) in every single frame is usually not practical in real-time scenarios due to complexity issues.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Road Detection through Supervised Classification

Alkhorshid

Aryafar

Bauer

et al. 2016

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Autonomous driving is a rapidly evolving technology. Autonomous vehicles are capable of sensing their environment and navigating without human input through sensory information such as radar, lidar, GNSS, vehicle odometry, and computer vision. This sensory input provides a rich dataset that can be used in combination with machine learning models to tackle multiple problems in supervised settings. In this paper we focus on road detection through gray-scale images as the sole sensory input. Our contributions are twofold: first, we introduce an annotated dataset of urban roads for machine learning tasks; second, we introduce a road detection framework on this dataset through supervised classification and hand-crafted feature vectors.

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“…The lane-mark edges might merge with the edges of shadows or dirt or cracks. For threshold-based approaches in [5]- [8], [15], [18]- [20] are sensitive to changes in illumination.…”

Section: Introductionmentioning

confidence: 99%

Multi-lane Detection Based on RMFP For Self-Driving in urban traffic scenes

Li¹,

Liu²,

Zheng³

et al. 2016

Proceedings of the 2016 4th International Conference on Sensors, Mechatronics and Automation (ICSMA 2016)

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The lane detection is important for the autonomous vehicle vision navigation used in the intelligent transportation system (ITS). Several approaches for lane detection were suggested in the past. However, there is still one issue about robustness. This paper presents a robust and real-time multi-lane detection method based on Road Marking Feature Points (RMFP) for autonomous vehicle navigation in urban environment. The key idea is to apply methods from extracting RMFP and the target tracking domain to identify lanes information in this article. Then we extract the RMFP from the gray-scale image and the IPM image. Besides we also use the lane line color and structure features to sift RMFP that meets lane line. At last, we adopt the clustering method to generate lane lines, and we track these lines by frame association and Kalman Filter. The experimental results show that our proposed method is robust and real-time detect the lane line of various kinds of complicated road. And based on the lane line visual navigation of unmanned experiment validate the reliability of our method.

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Lane Detection and Estimation using Perspective Image

Cited by 15 publications

References 5 publications

Mind the gap: Developments in autonomous driving research and the sustainability challenge

Mind the gap: Developments in autonomous driving research and the sustainability challenge

Road Detection through Supervised Classification

Multi-lane Detection Based on RMFP For Self-Driving in urban traffic scenes

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