A new multi-agent approach for lane detection and tracking

Revilloud, Marc; Gruyer, Dominique; Rahal, M-C

doi:10.1109/icra.2016.7487482

Cited by 23 publications

(17 citation statements)

References 15 publications

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“…The control/command data exchange between the infrastructure supervision room and the car was done through a Direct Short-Range Communication (DSRC) antenna. All the information was displayed in the car on an Human Machine Interface (HMI) also integrating a real-time misalignment feedback graphic interface (based on a lane detection system developed according the methodology background of [44]), as shown in Figure 6: All the vehicle measurements and car CAN data could be recorded via a multichannel data logger with an adjustable acquisition frequency up to 100 kHz.…”

Section: Serial Vehicle Implemented With Dynamic Ipt System and Additmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Wireless Power Transfer Charging Infrastructure for Future EVs: From Experimental Track to Real Circulated Roads Demonstrations

Laporte

Coquery

Deniau³

et al. 2019

WEVJ

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In a context of growing electrification of road transport, Wireless Power Transfer (WPT) appears as an appealing alternative technology as it enables Electric Vehicles (EVs) to charge while driving and without any mechanical contact (with overhead cables or rails in the ground). Although the WPT technology background dates from the end of 20th century, recent advances in semiconductor technologies have enabled the first real demonstrations. Within the FABRIC European project, the French research Institute VEDECOM and its partners implemented a whole prototype wireless power transfer charging infrastructure. The first demonstrations of Inductive WPT in different real driving conditions (up to 20 kW, from 0 to 100 km/h, with one or two serial vehicles) were provided. This paper describes the prototype equipment and its instrumentation and provides the system characterization results. The future of the Inductive WPT technology is further discussed considering its different technical and economic challenges. In parallel, how this technology could be part of future generation road infrastructures is discussed. Future research and demonstration steps are presented in the conclusion.

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Section: Serial Vehicle Implemented With Dynamic Ipt System and Additmentioning

confidence: 99%

“…Road integration was handled with a civil engineering approach given accessibility constraints to power electronic components. After the demonstrations illustrated in [36][37][38][39][40][41][42][43][44][45][46][47][48], the TRL for the dynamic IPT technology could be valued between 6 and 7.…”

Section: Demonstrations and Experimental Characterizations In Real Comentioning

confidence: 99%

Dynamic Wireless Power Transfer Charging Infrastructure for Future EVs: From Experimental Track to Real Circulated Roads Demonstrations

Laporte

Coquery

Deniau³

et al. 2019

WEVJ

View full text Add to dashboard Cite

show abstract

“…Instead of using the general image processing and machine learning methods, some other researchers used evolution algorithms or heuristic algorithms to automatically search lane boundaries. For example, Revilloud et al proposed a novel lane detection method using a confidence map and a multi-agent model inspired by human driver behaviors [42]. Similarly, an ant colonies evolution algorithm for the optimal lane marking search was proposed in [43].…”

Section: Machine Learning-based Lane Detection Algorithmsmentioning

confidence: 99%

Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision

Xing

Chen

Wang

et al. 2018

IEEE/CAA J. Autom. Sinica

150

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Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.

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“…Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. the vehicle navigate in the correct way but also prompt the driving system to focus on the specific tasks in the street scene, such as lane detection [1], vehicle detection [2], pedestrian detection [3] and anomaly detection [4]. If the road region can be ensured, other detection tasks will also benefit from it.…”

Section: Introductionmentioning

confidence: 99%

Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection

Wang

Yuan

2018

IEEE Trans. Intell. Transport. Syst.

214

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Road detection from the perspective of moving vehicles is a challenging issue in autonomous driving. Recently, many deep learning methods spring up for this task because they can extract high-level local features to find road regions from raw RGB data, such as Convolutional Neural Networks (CNN) and Fully Convolutional Networks (FCN). However, how to detect the boundary of road accurately is still an intractable problem. In this paper, we propose a siamesed fully convolutional networks (named as "s-FCN-loc"), which is able to consider RGB-channel images, semantic contours and location priors simultaneously to segment road region elaborately. To be specific, the s-FCNloc has two streams to process the original RGB images and contour maps respectively. At the same time, the location prior is directly appended to the siamesed FCN to promote the final detection performance. Our contributions are threefold: (1) An s-FCN-loc is proposed that learns more discriminative features of road boundaries than the original FCN to detect more accurate road regions; (2) Location prior is viewed as a type of feature map and directly appended to the final feature map in s-FCNloc to promote the detection performance effectively, which is easier than other traditional methods, namely different priors for different inputs (image patches); (3) The convergent speed of training s-FCN-loc model is 30% faster than the original FCN, because of the guidance of highly structured contours. The proposed approach is evaluated on KITTI Road Detection Benchmark and One-Class Road Detection Dataset, and achieves a competitive result with state of the arts.

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A new multi-agent approach for lane detection and tracking

Cited by 23 publications

References 15 publications

Dynamic Wireless Power Transfer Charging Infrastructure for Future EVs: From Experimental Track to Real Circulated Roads Demonstrations

Dynamic Wireless Power Transfer Charging Infrastructure for Future EVs: From Experimental Track to Real Circulated Roads Demonstrations

Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision

Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection

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