Predicting Road Surface Anomalies by Visual Tracking of a Preceding Vehicle

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00387137" target="_blank" >RIV/68407700:21230/25:00387137 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/IV64158.2025.11097610" target="_blank" >https://doi.org/10.1109/IV64158.2025.11097610</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IV64158.2025.11097610" target="_blank" >10.1109/IV64158.2025.11097610</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting Road Surface Anomalies by Visual Tracking of a Preceding Vehicle

Popis výsledku v původním jazyce

A novel approach to detect road surface anomalies by visual tracking of a preceding vehicle is proposed. The method is versatile, predicting any kind of road anomalies, such as potholes, bumps, debris, etc., unlike direct observation methods that rely on training visual detectors of those cases. The method operates in low visibility conditions or in dense traffic where the anomaly is occluded by a preceding vehicle. Anomalies are detected predictively, i.e., before a vehicle en-counters them, which allows to pre-configure low-level vehicle systems (such as chassis) or to plan an avoidance maneuver in case of autonomous driving. A challenge is that the signal coming from camera-based tracking of a preceding vehicle may be weak and disturbed by camera ego motion due to vibrations affecting the ego vehicle. Therefore, we propose an efficient method to compensate camera pitch rotation by an iterative robust estimator. Our experiments on both controlled setup and normal traffic conditions show that road anomalies can be detected reliably at a distance even in challenging cases where the ego vehicle traverses imperfect road surfaces. The method is effective and performs in real time on standard consumer hardware.

Název v anglickém jazyce

Predicting Road Surface Anomalies by Visual Tracking of a Preceding Vehicle

Popis výsledku anglicky

A novel approach to detect road surface anomalies by visual tracking of a preceding vehicle is proposed. The method is versatile, predicting any kind of road anomalies, such as potholes, bumps, debris, etc., unlike direct observation methods that rely on training visual detectors of those cases. The method operates in low visibility conditions or in dense traffic where the anomaly is occluded by a preceding vehicle. Anomalies are detected predictively, i.e., before a vehicle en-counters them, which allows to pre-configure low-level vehicle systems (such as chassis) or to plan an avoidance maneuver in case of autonomous driving. A challenge is that the signal coming from camera-based tracking of a preceding vehicle may be weak and disturbed by camera ego motion due to vibrations affecting the ego vehicle. Therefore, we propose an efficient method to compensate camera pitch rotation by an iterative robust estimator. Our experiments on both controlled setup and normal traffic conditions show that road anomalies can be detected reliably at a distance even in challenging cases where the ego vehicle traverses imperfect road surfaces. The method is effective and performs in real time on standard consumer hardware.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IEEE IV2025 36th IEEE Intelligent Vehicles Symposium

ISBN

979-8-3315-3803-3

ISSN

1931-0587

e-ISSN

2642-7214

Počet stran výsledku

6

Strana od-do

1795-1800

Název nakladatele

IEEE Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Piscataway, New Jersey

Místo konání akce

Cluj-Napoca

Datum konání akce

22. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001556907500281