Dynamic integration and online evaluation of vision‐based lane detection algorithms

Xing, Yang; Lv, Chen; Wang, Huaji; Cao, Dongpu; Velenis, Efstathios

doi:10.1049/iet-its.2018.5256

Cited by 28 publications

(14 citation statements)

References 32 publications

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“…A Hough transform and a shape-preserving spline fitted the lane smoothly [78]. In previous study [79], the authors combined a Hough transform and a least-squares line-fitting model, while authors in previous study [80] In order to improve the performance of lane extraction in complex conditions, CNN based on deep learning was implemented. In previous study [84], the authors proposed a multi-task deep convolutional method to detect lane markings and geometry.…”

Section: Vision-based Methodsmentioning

confidence: 99%

Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

Zheng

Yang

et al. 2019

Sustainability

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Autonomous driving is experiencing rapid development. A lane-level map is essential for autonomous driving, and a lane-level road network is a fundamental part of a lane-level map. A large amount of research has been performed on lane-level road network generation based on various on-board systems. However, there is a lack of analysis and summaries with regards to previous work. This paper presents an overview of lane-level road network generation techniques for the lane-level maps of autonomous vehicles with on-board systems, including the representation and generation of lane-level road networks. First, sensors for lane-level road network data collection are discussed. Then, an overview of the lane-level road geometry extraction methods and mathematical modeling of a lane-level road network is presented. The methodologies, advantages, limitations, and summaries of the two parts are analyzed individually. Next, the classic logic formats of a lane-level road network are discussed. Finally, the survey summarizes the results of the review.

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Section: Vision-based Methodsmentioning

confidence: 99%

Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

Zheng

Yang

et al. 2019

Sustainability

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“…The previous work on lane change detection can be categorized into three different approaches: the feature-based, model-based, and inverse perspective transform techniques. In feature-based methods, the road markings [ 17 , 18 ] or the lane lines [ 19 ] are taken as low level features to locate the lane position in the image. On the other hand, pre-defined curves (e.g., straight line or parabola functions [ 20 ], etc.)…”

Section: Related Workmentioning

confidence: 99%

A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

Lin

Dai

et al. 2020

Sensors

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One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are adopted for visual sensing and environment perception. The purpose is to avoid potential traffic accidents due to forward collision and vehicle overtaking, and assist the drivers or self-driving cars to perform safe lane change operations. The proposed techniques consist of lane change detection, forward collision warning, and overtaking vehicle identification. A new cumulative density function (CDF)-based symmetry verification method is proposed for the detection of front vehicles. The motion cue obtained from optical flow is used for overtaking detection. It is further combined with a convolutional neural network to remove repetitive patterns for more accurate overtaking vehicle identification. Our approach is able to adapt to a variety of highway and urban scenarios under different illumination conditions. The experiments and performance evaluation carried out on real scene images have demonstrated the effectiveness of the proposed techniques.

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“…The nearby straight line model provides the robustness of the model, and the parabola provides the flexibility of the model. Similar to [46], some researchers employ a clothoid model [47] or a hyperbola model [48] as the far-range model.…”

Section: A Lane Modelingmentioning

confidence: 99%

Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios

et al. 2020

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As one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios. However, ego-lane detection in the missing feature scenarios is still an unsolved problem. To address this problem, previous methods have been devoted to proposing more complicated feature extraction algorithms, but they are very time-consuming and cannot deal with extreme scenarios. Different from others, this paper exploits prior knowledge contained in digital maps, which has a strong capability to enhance the performance of detection algorithms. Specifically, we employ the road shape extracted from OpenStreetMap as lane model, which is highly consistent with the real lane shape and irrelevant to lane features. In this way, only a few lane features are needed to eliminate the position error between the road shape and the real lane, and a search-based optimization algorithm is proposed. Experiments show that the proposed method can be applied to various scenarios and can run in real-time at a frequency of 20 Hz. At the same time, we evaluated the proposed method on the public KITTI Lane dataset where it achieves state-of-the-art performance. Moreover, our code will be open source after publication. INDEX TERMS Ego-lane detection, missing feature, OpenStreetMap, parameter estimation.

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Dynamic integration and online evaluation of vision‐based lane detection algorithms

Cited by 28 publications

References 32 publications

Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios

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