Robust U-Net-based Road Lane Markings Detection for Autonomous Driving

Tran, Le-Anh; Le, My-Ha

doi:10.1109/icsse.2019.8823532

Cited by 50 publications

(31 citation statements)

References 15 publications

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“…Le-Anh Tran and My-Ha Le carried out their proposal of a robust road lane markings detection system with simulator CARLA that showed how the system resulted inefficient in straight parts. At the same time, it did not present beneficial results in curves [27].…”

Section: Autonomous Vehicles Detection Capability At Intersection Andmentioning

confidence: 78%

Autonomous Vehicles: An Analysis Both on Their Distinctiveness and the Potential Impact on Urban Transport Systems

et al. 2021

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Autonomous driving is a technological innovation that involves the use of Artificial Intelligence (AI) in the automotive area, representing the future of transport and whose applications will influence the concept of driving and many other features of modern society. Indeed, the introduction of Autonomous Vehicles (AVs) on the market, along with the development of related technologies, will have a potential impact not only on the automotive industry but also on urban transport systems. New mobility-related businesses will emerge, whereas existing ones will have to adapt to changes. There are various aspects that affect urban transport systems: in this work, we highlight how road markings, intersections, and pavement design upgradings have a key role for AVs operation. This work aims to describe how contemporary society will be influenced by Autonomous Vehicles’ spread in regard to urban transport systems. A comprehensive analysis of the expected developments within urban transport systems is hereby presented, and some crucial issues concerning benefits and drawbacks are also discussed. From our studies, it emerges that the detection performed by vehicles is mainly affected by road markings characteristics, especially at intersections. Indeed, the need for a new cross-sections type arise, since vehicles wandering phenomena will be reduced due to AVs position-keeping systems.

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Section: Autonomous Vehicles Detection Capability At Intersection Andmentioning

confidence: 78%

Autonomous Vehicles: An Analysis Both on Their Distinctiveness and the Potential Impact on Urban Transport Systems

et al. 2021

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“…where ϕ(t)(θ e (t)) is heading error, e(t) is cross-track error, v f (t) is the speed of the vehicle, k and k s are gain parameters respectively. k and k s in parameter types are expressed in (4). Since it is difficult to implement perfect tracking performance with only these two parameters, k 1 and k 2 should be added before θ e (t) and θ d (t), respectively.…”

Section: ) Stanley Controllermentioning

confidence: 99%

“…Hence, There are several prominent researches about path tracking on autonomous driving [2], [3]. Recently, end-to-end deep learning tracking algorithms [4]- [6] or methods using reinforcement learning [7] have been studied, but these methods are difficult to response flexible changes in the surrounding environment.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Tracker Based Optimal Path Tracking System of Autonomous Driving for Complex Road Environments

Seo

Lee²,

Shin³

et al. 2021

IEEE Access

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Path tracking system plays a key technology in autonomous driving. The system should be driven accurately along the lane and be careful not to cause any inconvenience to passengers. To address such tasks, this research proposes hybrid tracker based optimal path tracking system. By applying a deep learning based lane detection algorithm and a designated fast lane fitting algorithm, this research developed a lane processing algorithm that shows a match rate with actual lanes with minimal computational cost. In addition, three modified path tracking algorithms were designed using the GPS based path or the vision based path. In the driving system, a match rate for the correct ideal path does not necessarily represent driving stability. This research proposes hybrid tracker based optimal path tracking system by applying the concept of an observer that selects the optimal tracker appropriately in complex road environments. The driving stability has been studied in complex road environments such as straight road with multiple 3way junctions, roundabouts, intersections, and tunnels. Consequently, the proposed system experimentally showed the high performance with consistent driving comfort by maintaining the vehicle within the lanes accurately even in the presence of high complexity of road conditions. Code will be available in https://github.com/DGIST-ARTIV.

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“…Due to the significant application on saving time for driving and reducing the sudden death for the road accident, autonomous driving has become the most crucial research arena for recent years [3]. Consequently, the advanced driver assistance system (ADAS) has become one of the most popular research content for the researchers [4] based on apprehension, safety, and awareness of the traffic atmosphere around the vehicles.…”

Section: Introductionmentioning

confidence: 99%

Lane marking detection using simple encode decode deep learning technique: SegNet

Mamun

Ping

Hossen

2021

IJECE

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<span>In recent times, many innocent people are suffering from sudden death for the sake of unwanted road accidents, which also riveting a lot of financial properties. The researchers have deployed advanced driver assistance systems (ADAS) in which a large number of automated features have been incorporated in the modern vehicles to overcome human mortality as well as financial loss, and lane markings detection is one of them. Many computer vision techniques and intricate image processing approaches have been used for detecting the lane markings by utilizing the handcrafted with highly specialized features. However, the systems have become more challenging due to the computational complexity, overfitting, less accuracy, and incapability to cope up with the intricate environmental conditions. Therefore, this research paper proposed a simple encode-decode deep learning model to detect lane markings under the distinct environmental condition with lower computational complexity. The model is based on SegNet architecture for improving the performance of the existing researches, which is trained by the lane marking dataset containing different complex environment conditions like rain, cloud, low light, curve roads. The model has successfully achieved 96.38% accuracy, 0.0311 false positive, 0.0201 false negative, 0.960 F1 score with a loss of only 1.45%, less overfitting and 428 ms per step that outstripped some of the existing researches. It is expected that this research will bring a significant contribution to the field lane marking detection.</span>

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Robust U-Net-based Road Lane Markings Detection for Autonomous Driving

Cited by 50 publications

References 15 publications

Autonomous Vehicles: An Analysis Both on Their Distinctiveness and the Potential Impact on Urban Transport Systems

Autonomous Vehicles: An Analysis Both on Their Distinctiveness and the Potential Impact on Urban Transport Systems

Hybrid Tracker Based Optimal Path Tracking System of Autonomous Driving for Complex Road Environments

Lane marking detection using simple encode decode deep learning technique: SegNet

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