Localization tools for benchmarking ADAS control systems

Häfner, Michael; Zhao, Kangjia; Hsia, Andy C.; Rachlin, Z.

doi:10.1109/smc.2016.7844642

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Further contributions such as Wang in [10] propose the use of a B-Snake algorithm to improve lane detection models. Others, such as Hafner in [24] focus on the limitations of lane detection deployment for ITS and provide interesting mathematical solutions to overcome some computational and decision barriers. Lee has also presented a unique lane centering and control algorithm based on future predictions for autonomous driving maneuvers [25].…”

Section: Related Workmentioning

confidence: 99%

The Use of Lane-Centering to Ensure the Visible Light Communication Connectivity for a Platoon of Autonomous Vehicles

Abualhoul

Munoz

Nashashibi

2018

2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)

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The new emerging Visible Light Communication (VLC) technology has been subjected to intensive investigation, evaluation, and lately, deployed in the context of convoy-based applications for Intelligent Transportations Systems (ITS). The technology limitations were defined and supported by different solution proposals to enhance the crucial alignment and mobility limitations. In this paper, we propose to incorporate VLC technology and the Lane-Centering (LC) technique to ensure the VLC-connectivity by keeping the autonomous vehicle aligned to the lane center using vision-based lane detection in a convoy-based formation. As soon as the road lanes are detectable, the evaluated system showed stable behavior independently of the inter-vehicle distances and without the need to exchange information between vehicles. Such a combination can ensure the optical communication connectivity with a lateral error of less than 30 cm. The evaluation of the proposed system is verified using VLC prototype and an empirical result of an LC active application over 60 km on the Madrid M40 highway.

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Section: Related Workmentioning

confidence: 99%