# ECE-VDTDA: A robust and computationally efficient collision avoidance system for driver assistance in foggy weather

## TL;DR

This paper introduces ECE-VDTDA, a system that improves vehicle detection and collision avoidance in foggy weather using optimized algorithms and tracking methods.

## Contribution

The novel ECE-VDTDA system combines an optimized SimYOLO-V5s_WIOU algorithm with advanced tracking methods for improved performance in foggy conditions.

## Key findings

- The SimYOLO-V5s_WIOU algorithm achieved a 17.45% increase in mAP50 on the foggy driving dataset.
- It improved multiclass mAP50, mAP50-95, F1 score, precision, and recall by 0.32%, 1.05%, 1.58%, 2%, and 0.54% on the foggy cityscapes dataset.
- The system enables accurate vehicle tracking and timely collision warnings in foggy weather.

## Abstract

Advanced Driver Assistance Systems (ADAS) and Collision Avoidance Systems (CAS) are the primary modules of modern human-centric and autonomous driving applications, such as forward and rear-end collision warnings. To enhance the performance of ADAS and CAS systems in foggy weather, an Efficient and Cost-Effective Vehicle Detection and Tracking with Driver Assistance (ECE-VDTDA) system is proposed. The proposed ECE-VDTDA system comprises vehicle detection, tracking, and driver assistance modules. An optimized SimYOLO-V5s_WIOU vehicle detection algorithm is proposed, based on the SimSPPF module, the baseline You Only Look Once (YOLO) algorithm (YOLO-V5s), and the Wise Intersection Over Union (WIOU) localization loss function. State-of-the-art Deep-SORT, Strong-SORT, and optimized Deep-SORT algorithms are utilized for vehicle tracking. The vehicle detection and tracking performance of the ECE-VDTDA system is rigorously evaluated on DAWN, foggy driving, foggy cityscapes, BDD100K, web-collected, and self-collected foggy weather datasets. Optimized SimYOLO-V5s_WIOU algorithm outperformed on the foggy driving dataset with a 17.45% increase in mAP50, and foggy cityscapes dataset with a 0.32%, 1.05%, 1.58%, 2%, 0.54% increase in the multiclass mAP50, mAP50-95, F1 score, precision, and recall scores, respectively, compared to the baseline YOLO-V5s. Furthermore, the SimYOLO-V5s_WIOU algorithm also outperformed the state-of-the-art methods and enables Deep-SORT, Strong-SORT, and optimized Deep-SORT vehicle tracking algorithms to track vehicles with high confidence. The driver assistance module of the ECE-VDTDA system helps prevent imminent road collisions in foggy weather by estimating distance, speed, and time-to-collision and by issuing timely collision warnings. The experimental results demonstrate the robustness and computational efficiency of the proposed ECE-VDTDA system.