Map relative localization based on road lane matching with Iterative Closest Point algorithm

Evlampev, A.; Shapovalov, Igor; Gafurov, Salimzhan

doi:10.1145/3430199.3430229

Cited by 1 publication

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the lane geometry analysis stage determines whether the item is a lane marking. A map relative localization method based on road lane matching [49] is developed. When GNSS data is neither exact nor unavailable, the technique provides lanelevel location accuracy for autonomous vehicle driving.…”

Section: Ii) Deep Learning + Geometric Modellingmentioning

confidence: 99%

“…The generation of high-definition maps for autonomous driving using auto-assisted multi-category lane recognition [15]. The HD map is defined as a map that consists of the precise coordinates of road lanes in the Universal Transverse Mercator (UTM) coordinate system, as described in [49]. Other elements such as road signs and traffic lights are included, but only road lanes are used in this publication.…”

Section: ) Hd Mapmentioning

confidence: 99%

See 1 more Smart Citation

Lane Detection in Autonomous Vehicles: A Systematic Review

et al. 2023

View full text Add to dashboard Cite

One of the essential systems in autonomous vehicles for ensuring a secure circumstance for drivers and passengers is the Advanced Driver Assistance System (ADAS). Adaptive Cruise Control, Automatic Braking/Steer Away, Lane-Keeping System, Blind Spot Assist, Lane Departure Warning System, and Lane Detection are examples of ADAS. Lane detection displays information specific to the geometrical features of lane line structures to the vehicle's intelligent system to show the position of lane markings. This article reviews the methods employed for lane detection in an autonomous vehicle. A systematic literature review (SLR) has been carried out to analyze the most delicate approach to detecting the road lane for the benefit of the automation industry. One hundred and two publications from well-known databases were chosen for this review. The trend was discovered after thoroughly examining the selected articles on the method implemented for detecting the road lane from 2018 until 2021. The selected literature used various methods, with the input dataset being one of two types: self-collected or acquired from an online public dataset. In the meantime, the methodologies include geometric modeling and traditional methods, while AI includes deep learning and machine learning. The use of deep learning has been increasingly researched throughout the last four years. Some studies used stand-alone deep learning implementations for lane detection problems. Meanwhile, some research focuses on merging deep learning with other machine learning techniques and classical methodologies. Recent advancements imply that attention mechanism has become a popular combined strategy with deep learning methods. The use of deep algorithms in conjunction with other techniques showed promising outcomes. This research aims to provide a complete overview of the literature on lane detection methods, highlighting which approaches are currently being researched and the performance of existing state-of-the-art techniques. Also, the paper covered the equipment used to collect the dataset for the training process and the dataset used for network training, validation, and testing. This review yields a valuable foundation on lane detection techniques, challenges, and opportunities and supports new research works in this automation field. For further study, it is suggested to put more effort into accuracy improvement, increased speed performance, and more challenging work on various extreme conditions in detecting the road lane.

show abstract

Section: Ii) Deep Learning + Geometric Modellingmentioning

confidence: 99%

Section: ) Hd Mapmentioning

confidence: 99%