Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Aug 12;23(16):7136.
doi: 10.3390/s23167136.

Infrastructure-Based Vehicle Localization through Camera Calibration for I2V Communication Warning

Daniele Vignarca  1 , Michele Vignati  1 , Stefano Arrigoni  1 , Edoardo Sabbioni  1
Affiliations

Infrastructure-Based Vehicle Localization through Camera Calibration for I2V Communication Warning

Daniele Vignarca et al. Sensors (Basel). .

Abstract

In recent years, the research on object detection and tracking is becoming important for the development of advanced driving assistance systems (ADASs) and connected autonomous vehicles (CAVs) aiming to improve safety for all road users involved. Intersections, especially in urban scenarios, represent the portion of the road where the most relevant accidents take place; therefore, this work proposes an I2V warning system able to detect and track vehicles occupying the intersection and representing an obstacle for other incoming vehicles. This work presents a localization algorithm based on image detection and tracking by a single camera installed on a roadside unit (RSU). The vehicle position in the global reference frame is obtained thanks to a sequence of linear transformations utilizing intrinsic camera parameters, camera height, and pitch angle to obtain the vehicle's distance from the camera and, thus, its global latitude and longitude. The study brings an experimental analysis of both the localization accuracy, with an average error of 0.62 m, and detection reliability in terms of false positive (1.9%) and missed detection (3.6%) rates.

Keywords: I2V communication; image detection; intelligent transportation system; road user localization; roadside unit.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Communication categories for intelligent transportation system.
Figure 2
Figure 2
Schematic representation of the scenario under analysis.
Figure 3
Figure 3
Flow chart of the proposed methodology made of image acquisition, image processing, and DENM broadcasting.
Figure 4
Figure 4
Representation of the GStreamer pipeline for detection and tracking phases.
Figure 5
Figure 5
Image reference frame and bounding box representation.
Figure 6
Figure 6
Representation of the region of interest for localization algorithm.
Figure 7
Figure 7
Representation of the UTM coordinate reference frame and the quantities involved in the coordinates transformations.
Figure 8
Figure 8
Decentralized environmental notification message structure according to ETSI specification [27].
Figure 9
Figure 9
Picture of the roadside unit installed and camera view example.
Figure 10
Figure 10
Scheme of the roadside unit components: (a) power supply, (b) computation unit, (c) network, (d) camera.
Figure 11
Figure 11
Detection error for cars in the region of interest.
Figure 12
Figure 12
Latitude–longitude positioning comparison between camera estimate and RTK-corrected GPS for different velocities. (a) Car speed: 5 km/h. (b) Car speed: 10 km/h. (c) Car speed: 20 km/h.
Figure 13
Figure 13
Analysis of the longitudinal and lateral error with respect to the vehicle direction. (a) Car speed: 5 km/h. (b) Car speed: 10 km/h. (c) Car speed: 20 km/h.
Figure 14
Figure 14
Normalized localization error: car speed 20 km/h.
Figure 15
Figure 15
Total delay: composed of image acquisition, image detection, and computational time.
Figure 16
Figure 16
End-to-end infrastructure communication delay loop.
Figure 17
Figure 17
End-to-end infrastructure communication time delay.
See this image and copyright information in PMC

References

    1. Buch N., Velastin S.A., Orwell J. A review of computer vision techniques for the analysis of urban traffic. IEEE Trans. Intell. Transp. Syst. 2011;12:920–939. doi: 10.1109/TITS.2011.2119372. - DOI
    1. Tyagi B., Nigam S., Singh R. A Review of Deep Learning Techniques for Crowd Behavior Analysis. Arch. Comput. Methods Eng. 2022;29:5427–5455. doi: 10.1007/s11831-022-09772-1. - DOI
    1. Ounoughi C., Yahia S.B. Data fusion for ITS: A systematic literature review. Inf. Fusion. 2023;89:267–291. doi: 10.1016/j.inffus.2022.08.016. - DOI
    1. Nidamanuri J., Nibhanupudi C., Assfalg R., Venkataraman H. A Progressive Review: Emerging Technologies for ADAS Driven Solutions. IEEE Trans. Intell. Veh. 2022;7:326–341. doi: 10.1109/TIV.2021.3122898. - DOI
    1. Hu L., Ou J., Huang J., Chen Y., Cao D. A Review of Research on Traffic Conflicts Based on Intelligent Vehicles. IEEE Access. 2020;8:24471–24483. doi: 10.1109/ACCESS.2020.2970164. - DOI

LinkOut - more resources