2022
Conference article  Open Access

Towards multi-camera system for the evaluation of motorcycle driving test

Leone G. R., Righi M., Moroni D., Paolucci F.

Camera-base systems  Edge computing  Trajectory analysis  Computer vision  Artificial intelligence  Motorcycle driving test 

This work describes the early stage of an interactive and accelerated AI-driven framework for Practical Driving Courses and Driving Licence Exams. The core of the project is an innovative multi-parameter AI-assisted telemetry system able to compute test scores and outcome, useful for human-neutral auditability of Driving Licence Exams. The distributed Artificial Intelligence (AI) system available at the Track Testbed will be able to perform driving behaviour classifications and will suggest specific improvements based on the analysis of vehicle trajectories acquired during the driving test. Finally, the project will target the creation of a large dataset for driving test classification of key performance parameters. The system is envisioned to have a relevant impact on all the certification, driving licence operators and regulator entities.

Source: SITIS 2022 - 16th International Conference on Signal-Image Technology & Internet-Based Systems, pp. 561–568, Dijon, France, 19-21/10/2022


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BibTeX entry
@inproceedings{oai:it.cnr:prodotti:471275,
	title = {Towards multi-camera system for the evaluation of motorcycle driving test},
	author = {Leone G. R. and Righi M. and Moroni D. and Paolucci F.},
	doi = {10.1109/sitis57111.2022.00090},
	booktitle = {SITIS 2022 - 16th International Conference on Signal-Image Technology \& Internet-Based Systems, pp. 561–568, Dijon, France, 19-21/10/2022},
	year = {2022}
}