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Long-range road geometry estimation using moving vehicles and road-side observations

Lars Hammarstrand (Institutionen för signaler och system, Signalbehandling) ; Maryam Fatemi (Institutionen för signaler och system, Signalbehandling) ; Angel Garcia-Fernandez ; Lennart Svensson (Institutionen för signaler och system, Signalbehandling)
IEEE transactions on intelligent transportation systems (1524-9050). Vol. 17 (2016), 8, p. 2144-2158.
[Artikel, refereegranskad vetenskaplig]

This paper presents an algorithm for estimating the shape of the road ahead of a host vehicle equipped with the following onboard sensors: a camera, a radar and vehicle internal sensors. The aim is to accurately describe the road geometry up to 200 m ahead in highway scenarios. This purpose is accomplished by deriving a precise clothoid-based road model for which we design a Bayesian fusion framework. Using this framework the road geometry is estimated using sensor observations on the shape of the lane markings, the heading of leading vehicles and the position of road side radar reflectors. The evaluation on sensor data shows that the proposed algorithm is capable of capturing the shape of the road well, even in challenging mountainous highways.

Nyckelord: Road geometry, Bayesian Estimation, Advanced driver assistance systems

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Denna post skapades 2016-02-25. Senast ändrad 2016-09-28.
CPL Pubid: 232441


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Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling



Chalmers infrastruktur

ReVeRe (Research Vehicle Resource)

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