Distance and relative speed estimation of vehicles moving in the same direction

Intelligent vehicle systems often use expensive hardware to calculate distances and speeds of surrounding objects. This paper presents two algorithms for estimating the relative speed of vehicles using monocular cameras. The first algorithm relies on license plate detection with a known size, while a modified version incorporates car dimension calculations. The second algorithm utilizes pre-calibrated cameras based on marked driveways. An experiment was conducted to compare the efficiency of the algorithms. Experiments showed that the most accurate results for estimation the distance to vehicles were obtained by the preliminary marking method with an error close to 0%. However, this method has significant limitations, such as camera fixation and stabilization requirements. The second most accurate method, based on using vehicle width values, demonstrated an error range of 1.5% to 3.3%. Unlike the first method, this algorithm does not require strict camera fixation and stabilization.

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Publication Details

Published
2024-09-19
DOI
https://doi.org/10.1109/dcna63495.2024.10718489
Citations
2
Primary Topic
Vehicle Dynamics and Control Systems
Type
article
Field-Weighted Citation Impact
0.41
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article

Distance and relative speed estimation of vehicles moving in the same direction

Dmitry Gavrilov, Ivan Kholodnyak, Nikita Vodichev, Masoud Vahid Dastgerdi et al.
2 citations
Vehicle Dynamics and Control Systems
0.41
article

Distance and relative speed estimation of vehicles moving in the same direction

Dmitry Gavrilov, Ivan Kholodnyak, Nikita Vodichev, Masoud Vahid Dastgerdi, Mikhail Parshikov, Viktor Zuev, Andrew Leus, Vlad Efremov, Nikolay Gershtein, Vadim Laukhin
article en
2 citations

Abstract

Intelligent vehicle systems often use expensive hardware to calculate distances and speeds of surrounding objects. This paper presents two algorithms for estimating the relative speed of vehicles using monocular cameras. The first algorithm relies on license plate detection with a known size, while a modified version incorporates car dimension calculations. The second algorithm utilizes pre-calibrated cameras based on marked driveways. An experiment was conducted to compare the efficiency of the algorithms. Experiments showed that the most accurate results for estimation the distance to vehicles were obtained by the preliminary marking method with an error close to 0%. However, this method has significant limitations, such as camera fixation and stabilization requirements. The second most accurate method, based on using vehicle width values, demonstrated an error range of 1.5% to 3.3%. Unlike the first method, this algorithm does not require strict camera fixation and stabilization.

Moscow Institute of Physics and Technology (RU)
Openalex Percentile: Top 39%
Vehicle Dynamics and Control Systems
0.41
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Distance and relative speed estimation of vehicles moving in the same direction — Dmitry Gavrilov, Ivan Kholodnyak, et al. · (2024) | TGRS Research Map | TGRS