In this paper we present a probabilistic lane-localization algorithm for highway-like scenarios designed to increase the accuracy of the vehicle localization estimate. The contribution relies on a Hidden Markov Model (HMM) with a transient failure model. The idea behind the proposed approach is to exploit the availability of OpenStreetMap road properties in order to reduce the localization uncertainties that would result from relying only on a noisy line detector, by leveraging consecutive, possibly incomplete, observations. The algorithm effectiveness is proven by employing a line detection algorithm and showing we could achieve a much more usable, i.e., stable and reliable, lane-localization 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 of our results 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.

Ballardini, A., Cattaneo, D., Izquierdo, R., Parra, I., Sotelo, M., Sorrenti, D. (2018). Ego-lane estimation by modeling lanes and sensor failures. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) (pp.1-7). Institute of Electrical and Electronics Engineers Inc. [10.1109/ITSC.2017.8317834].

Ego-lane estimation by modeling lanes and sensor failures

Ballardini, AL
;
Cattaneo, D;Sorrenti, DG
2018

Abstract

In this paper we present a probabilistic lane-localization algorithm for highway-like scenarios designed to increase the accuracy of the vehicle localization estimate. The contribution relies on a Hidden Markov Model (HMM) with a transient failure model. The idea behind the proposed approach is to exploit the availability of OpenStreetMap road properties in order to reduce the localization uncertainties that would result from relying only on a noisy line detector, by leveraging consecutive, possibly incomplete, observations. The algorithm effectiveness is proven by employing a line detection algorithm and showing we could achieve a much more usable, i.e., stable and reliable, lane-localization 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 of our results 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.
slide + paper
Roads, Estimation, Hidden Markov models, Detectors, Reliability, Probabilistic logic
English
20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017
2017
2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)
9781538615256
2018
2018-
1
7
reserved
Ballardini, A., Cattaneo, D., Izquierdo, R., Parra, I., Sotelo, M., Sorrenti, D. (2018). Ego-lane estimation by modeling lanes and sensor failures. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) (pp.1-7). Institute of Electrical and Electronics Engineers Inc. [10.1109/ITSC.2017.8317834].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/192520
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