A Driver Assistance System Based on YOLO Object Detection: Development and Experimental Validation in the CARLA Simulator

Daniel Terra Gomes; Annabell Del Real Tamariz

doi:10.5753/jbcs.2026.6503

Authors

Daniel Terra Gomes Universidade Estadual do Norte Fluminense Darcy Ribeiro https://orcid.org/0009-0003-0701-5889
Annabell Del Real Tamariz Universidade Estadual do Norte Fluminense Darcy Ribeiro https://orcid.org/0000-0001-9951-0657

DOI:

https://doi.org/10.5753/jbcs.2026.6503

Keywords:

Self-driving car, Object Detection, Vehicle Control, Simulation, CARLA, YOLO

Abstract

This work presents the development and validation of an integrated Advanced Driver-Assistance System (ADAS) combining YOLOv8-based computer vision with vehicle control in the CARLA simulator. The primary objective was to implement and validate the system’s capability to detect stop signs and vehicles in real time, providing visual feedback to the driver and enabling the vehicle to respond to stop signs through appropriate deceleration and stopping behavior. The system employs a modular three-layer architecture: perception (YOLOv8), planning (finite state machine), and control (Proportional-Integral-Derivative (PID) longitudinal, Pure Pursuit lateral). Evaluation across six simulations under three weather conditions (clear noon, heavy rain sunset, heavy rain noon) demonstrated real-time processing at 17.13 FPS average, 59.4 ms detection time per frame, and 100% detection accuracy for stop signs and vehicles on the test route. Stop sign detection confidence remained above 0.73 across all conditions (coefficient of variation: 0.58%), and ANOVA revealed a significant effect of weather on detection time (p = 0.025) but no impact on detection confidence (p = 0.651), confirming perceptual reliability under adverse conditions. All routes were completed without collisions. Despite limitations inherent to simulated validation, the results empirically confirm that YOLO-based ADAS can provide reliable real-time visual feedback and proper behavioral responses for stop signs and vehicles under three distinct weather conditions, establishing methodological foundations for modular, distributed-processing architectures in autonomous vehicle applications.

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