An Information Driven Approach For Ego-Lane Detection Using Lidar And OpenStreetMap

Kasmi, Abderrahim; Laconte, Johann; Aufrère, Romuald; Théodose, Ruddy; Denis, Dieumet; Chapuis, Rol

doi:10.1109/icarcv50220.2020.9305388

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…Following that, a practical multi-layer evaluation function was constructed to implement the online adjustment of lane lines using the straight-line fitted by the Hough Transform. Kasmi et al [44] is another paper that proposed the traditional technique. Initially selecting the best Region of In terest, the author used the conventional method for detecting the road lane.…”

Section: ) Geometric Modelling/traditional Methodsmentioning

confidence: 99%

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Lane Detection in Autonomous Vehicles: A Systematic Review

et al. 2023

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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.

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Section: ) Geometric Modelling/traditional Methodsmentioning

confidence: 99%

“…After that, the authors provide OSM data and proprioceptive sensor fusion architecture. In the meantime, a similar approach derived from OSM was used to identify ego-lane marking in LiDAR point clouds [44]. Nodes, Ways, and Relations [44] are the three crucial components of OSM data.…”

Section: ) Openstreetmapmentioning

confidence: 99%

Lane Detection in Autonomous Vehicles: A Systematic Review

et al. 2023

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“…This idea was extended in [ 63 ]. Following, Kasmi et al [ 64 ] proposed a top-down approach that first computes focused ROI using coarse maps such as OSM, then uses these ROI to perform coherent line detection.…”

Section: Ego-lane Level Localization (Ell)mentioning

confidence: 99%

A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios

Laconte

Kasmi

Aufrère

et al. 2021

Sensors

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In the context of autonomous vehicles on highways, one of the first and most important tasks is to localize the vehicle on the road. For this purpose, the vehicle needs to be able to take into account the information from several sensors and fuse them with data coming from road maps. The localization problem on highways can be distilled into three main components. The first one consists of inferring on which road the vehicle is currently traveling. Indeed, Global Navigation Satellite Systems are not precise enough to deduce this information by themselves, and thus a filtering step is needed. The second component consists of estimating the vehicle’s position in its lane. Finally, the third and last one aims at assessing on which lane the vehicle is currently driving. These two last components are mandatory for safe driving as actions such as overtaking a vehicle require precise information about the current localization of the vehicle. In this survey, we introduce a taxonomy of the localization methods for autonomous vehicles in highway scenarios. We present each main component of the localization process, and discuss the advantages and drawbacks of the associated state-of-the-art methods.

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“…Laser scanners, also known as lidar, are active sensors that serve many applications such as blockage identification, pedestrian and vehicle identification [137], host vehicle lane detection [138], and describing the precise location of a vehicle [139]. Using a laser, the lidar emits high-frequency pulses.…”

Section: ) Lidarmentioning

confidence: 99%

A Review on Driving Control Issues for Smart Electric Vehicles

Haque

Rahman²,

Islam

et al. 2021

IEEE Access

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Smart electric vehicles (EVs) are attractive because of their clean, zero-emission, low impact on the environment whilst providing a safer and smoother riding experience. To provide the latter, driving control requires appropriate systems and algorithms to optimize smart vehicle performance, maximize vehicle stability and protection, minimize accident probability, heighten driving comfort, and optimize transportation costs. Despite advancements in these areas, the realization of optimal smart EVs still requires considerable effort. This paper reviews driving control systems and algorithms for smart EVs, including the advanced driving assistant system, implementation of sensors, vehicle dynamics, and control algorithms. The major contribution of this review is to identify promising work to assist researchers with the most advanced trends in this area for prospective regulations. INDEX TERMSSmart Electric Vehicles (EVs), Driving Control Systems (DCS), Advanced Driving Assistance System (ADAS), Algorithms.

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An Information Driven Approach For Ego-Lane Detection Using Lidar And OpenStreetMap

Cited by 6 publications

References 18 publications

Lane Detection in Autonomous Vehicles: A Systematic Review

Lane Detection in Autonomous Vehicles: A Systematic Review

A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios

A Review on Driving Control Issues for Smart Electric Vehicles

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