Deadline‐Adherent Edge AI for Intelligent Vehicles: Real‐Time Obstacle and Traffic Light Detection Using Quantized YOLOv8n on Jetson Orin Nano

ABSTRACT Reliable and time‐bounded perception systems are necessary for autonomous vehicles' (AVs') safe navigation, especially at intersections. In this work, a post‐training quantized you only look once (YOLOv8n) model for real‐time obstacle and traffic light recognition is implemented on the NVIDIA Jetson Orin Nano. The system, which is integrated with the robot operating system 2 (ROS 2) framework, analyzes stereo input from a ZED 2i camera using soft real‐time scheduling theory, taking worst‐case execution time (WCET), jitter, and slack into account. Despite the workload surpassing the traditional schedulability constraints under earliest deadline first (EDF) and rate monotonic scheduling (RMS), empirical evaluation across 423 daytime urban frames reveals that 98.11% of inferences fulfil a 150 ms soft deadline. The results show minimal thermal drift, low jitter, consistent slack margins, and bounded deadline violations (