Enhanced Drift-Aware Computer Vision Architecture for Autonomous Driving

TL;DR

This paper introduces a hybrid computer vision system for autonomous driving that combines YOLOv8 and CNN to enhance object detection accuracy under challenging, drifted environmental conditions, thereby improving safety.

Contribution

A novel hybrid architecture trained on synthetic data that significantly improves detection robustness in adverse conditions for autonomous vehicles.

Findings

Detection accuracy improved by over 90% in drifted environments

Hybrid model outperforms single-model approaches in robustness

Synthetic data training enhances real-world performance

Abstract

The use of computer vision in automotive is a trending research in which safety and security are a primary concern. In particular, for autonomous driving, preventing road accidents requires highly accurate object detection under diverse conditions. To address this issue, recently the International Organization for Standardization (ISO) released the 8800 norm, providing structured frameworks for managing associated AI relevant risks. However, challenging scenarios such as adverse weather or low lighting often introduce data drift, leading to degraded model performance and potential safety violations. In this work, we present a novel hybrid computer vision architecture trained with thousands of synthetic image data from the road environment to improve robustness in unseen drifted environments. Our dual mode framework utilized YOLO version 8 for swift detection and incorporated a five-layer CNN for verification. The system functioned in sequence and improved the detection accuracy by more than 90\% when tested with drift-augmented road images. The focus was to demonstrate how such a hybrid model can provide better road safety when working together in a hybrid structure.