Lane-precise localization of intelligent vehicles using the surrounding object constellation

Klunker, Florian; Orf, Stefan; Klemm, Sebastian; Zöllner, J. Marius

doi:10.1109/itsc.2016.7795605

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…With the objective to pursue lane-level localization, the authors in [16] propose to exploit the objects present in the surrounding of the vehicle and to describe the probabilistic dependencies between the object measurements, by means of a factor graph model. A similar proposal comes from the authors of [17], where Histogram of Oriented Gradients are used to align the images acquired from a front facing camera to the road lane markings, to improve the vehicle localization.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Ego-Lane Estimation with Sensor Failure Modeling

Ballardini,

Cattaneo,

Izquierdo

et al. 2020

Preprint

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We present a probabilistic ego-lane estimation algorithm for highway-like scenarios that is designed to increase the accuracy of the ego-lane estimate, which can be obtained relying only on a noisy line detector and tracker. The contribution relies on a Hidden Markov Model (HMM) with a transient failure model. The proposed algorithm exploits the OpenStreetMap (or other cartographic service) road property "lane number" as the expected number of lanes and leverages consecutive, possibly incomplete, observations. The algorithm effectiveness is proven by employing different line detectors and showing we could achieve much more usable, i.e., stable and reliable, ego-lane estimates over more than 100Km of highway scenarios, recorded both in Italy and Spain. Moreover, as we could not find a suitable dataset for a quantitative comparison with other approaches, we collected datasets and manually annotated the Ground Truth about the vehicle ego-lane. Such datasets are made publicly available for usage from the scientific community.

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Section: Related Workmentioning

confidence: 99%

Vehicle Ego-Lane Estimation with Sensor Failure Modeling

Ballardini,

Cattaneo,

Izquierdo

et al. 2020

Preprint

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“…Nevertheless, computing lane-level accurate and consistent estimates of the vehicle pose, i.e., position and heading, is still an open issue and remains a key feature to reach fully autonomous driving. In the past few years, the availability of high-definition maps [1], [3], which gather lane-level information such as lane markings, has led to new map-aided localization algorithms [8]. At the same time, the development of vehicle-to-vehicle (V2V) wireless communication devices allows a vehicle to have more sources of information through the exchange of messages.…”

Section: Introductionmentioning

confidence: 99%

“…Using lane markings information is especially useful to reduce cross-track and heading errors. However, in terms of along-track localization, lane markings can be invariant, for example on straight roads, therefore making the localization more challenging along the vehicle longitudinal direction [8]. Through out this paper, we assume that the vehicle has the capability to properly estimate its cross-track location and heading with respect to a given lane.…”

Section: Introductionmentioning

confidence: 99%

Along-track localization for cooperative autonomous vehicles

Héry

2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Localization is a key problem for autonomous vehicle navigation. The use of high-definition maps and perception algorithms allows now to have lane-level accurate pose estimation in terms of cross-track and heading error. In this paper, we focus on the along-track localization of cooperative vehicles. We introduce a one-dimensional formulation of the localization problem by considering curvilinear coordinates. The covariance intersection filter is derived in one dimension leading to a minimum variable filter which allows multiple vehicles to cooperate while keeping consistent localization estimates. We show that the along-track localization error is directly dependent on the relative orientation between the trajectories followed by the cooperating vehicles. Experiments with two autonomous electric vehicles were conducted to evaluate the proposed approach.

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