Accuracy Evaluation of Camera-based Vehicle Localization

Cordes, Kai; Nolte, N.; Meine, Nikolaus; Broszio, Hellward

doi:10.1109/iccve45908.2019.8965230

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…Compared with multi-camera infrastructure approaches that have access to camera extrinstics, our method is at worst on par, and in several cases improved. To begin with, the localisation accuracy is improved compared to [28], where the localisation error varies between -2m and 4m, despite higher resolution images and knowing the camera extrinsics. In addition, our localisation accuracy is on par to [29], that reports a mean error of 33cm (compared to ours of 48cm).…”

Section: Discussionmentioning

confidence: 99%

Experimental Study of Multi-Camera Infrastructure Perception for V2X-Assisted Automated Driving in Highway Merging

Shan,

Penlington,

Gunner

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Accurate and reliable perception of the surrounding environment, e.g., detection and classification of nearby objects, is the primary and most important function of automated/autonomous vehicles. However, onboard perception systems face challenges in complex road segments due to various environmental effects, such as occlusions, or high sensor noise. A potential enhancement is to equip such environments with cost-effective infrastructures that perceive the environment and provide additional perception support to autonomous vehicles through vehicle-to-everything (V2X) communication technologies. This paper develops an experimental study of vehicle detection and tracking on a bird's eye view (BEV) map using raw video collected from several low-cost roadside monocular cameras with overlapping views installed near a motorway junction to support the merging of autonomous vehicles. The paper explains how to produce vehicle tracks from the camera infrastructure and reports the real-world evaluation of the proposed solution on a physical test bed in the UK's West Midland region.

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Section: Discussionmentioning

confidence: 99%

Experimental Study of Multi-Camera Infrastructure Perception for V2X-Assisted Automated Driving in Highway Merging

Shan,

Penlington,

Gunner

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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“…It is necessary that the system considers non-connected, i.e., non-communicating, road users, since it cannot be assumed that every road user is connected to the network. For the external observation of road users, a camera system is installed at the road infrastructure next to the lane merge location [11]. All road users (connected and non-connected) are localized and identified by the camera system.…”

Section: Lane Merge Coordinationmentioning

confidence: 99%

On the Needs and Requirements Arising from Connected and Automated Driving

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Brahmi

Abbas

et al. 2020

JSAN

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Future 5G systems have set a goal to support mission-critical Vehicle-to-Everything (V2X) communications and they contribute to an important step towards connected and automated driving. To achieve this goal, the communication technologies should be designed based on a solid understanding of the new V2X applications and the related requirements and challenges. In this regard, we provide a description of the main V2X application categories and their representative use cases selected based on an analysis of the future needs of cooperative and automated driving. We also present a methodology on how to derive the network related requirements from the automotive specific requirements. The methodology can be used to analyze the key requirements of both existing and future V2X use cases.

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