Authors:
Nesrine Triki
1
;
2
;
Mohamed Karray
2
and
Mohamed Ksantini
1
Affiliations:
1
National School of Engineers of Sfax, CEM Lab, University of Sfax, Sfax, Tunisia
;
2
ESME, ESME Research Lab, Ivry Sur Seine, France
Keyword(s):
Advanced Driver Assistance Systems (ADAS), Automated Driving Systems (ADS), Speed Limit Recognition System (SLRs), Artificial Intelligence, Belief Functions, Ensemble Learning, Embedded Systems.
Abstract:
Recent advancements in intelligent driving have led to the integration of various automated systems into vehicles, including Speed Limit Recognition systems, which play a crucial role in enhancing road safety and autonomous driving technologies. This paper presents a comprehensive approach to Speed Limit Recognition, based on three modules: detection, classification, and the fusion of machine learning and deep learning classifiers. The proposed approach achieves impressive results, with an accuracy of 99.98% using Dempster Shafer theory and 99.96% with the voting technique. The system’s performance is rigorously evaluated through simulation and hardware validation using a Raspberry Pi 4 board. Experimental results indicate high performance rates across nine classes from the German Traffic sign Recognition Benchmark dataset in an average processing time of 0.15 seconds.