V-CAS: A Realtime Vehicle Anti Collision System Using Vision Transformer on Multi-Camera Streams

TL;DR

V-CAS is a real-time, multi-camera vision transformer-based system that improves vehicle collision avoidance by accurately perceiving the environment and proactively applying adaptive braking, demonstrating high accuracy and early alert capabilities.

Contribution

This work introduces V-CAS, a novel multi-camera, vision transformer-based system for real-time vehicle collision avoidance with enhanced perception and proactive safety features.

Findings

Achieved over 98% detection accuracy on the CCD dataset.

Provided an average proactive alert time of 1.13 seconds.

Enhanced collision avoidance performance over traditional single-camera systems.

Abstract

This paper introduces a real-time Vehicle Collision Avoidance System (V-CAS) designed to enhance vehicle safety through adaptive braking based on environmental perception. V-CAS leverages the advanced vision-based transformer model RT-DETR, DeepSORT tracking, speed estimation, brake light detection, and an adaptive braking mechanism. It computes a composite collision risk score based on vehicles' relative accelerations, distances, and detected braking actions, using brake light signals and trajectory data from multiple camera streams to improve scene perception. Implemented on the Jetson Orin Nano, V-CAS enables real-time collision risk assessment and proactive mitigation through adaptive braking. A comprehensive training process was conducted on various datasets for comparative analysis, followed by fine-tuning the selected object detection model using transfer learning. The system's effectiveness was rigorously evaluated on the Car Crash Dataset (CCD) from YouTube and through real-time experiments, achieving over 98% accuracy with an average proactive alert time of 1.13 seconds. Results indicate significant improvements in object detection and tracking, enhancing collision avoidance compared to traditional single-camera methods. This research demonstrates the potential of low-cost, multi-camera embedded vision transformer systems to advance automotive safety through enhanced environmental perception and proactive collision avoidance mechanisms.