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.

翻译：本文提出了一种实时车辆防碰撞系统（V-CAS），旨在通过基于环境感知的自适应制动来提升车辆安全性。V-CAS集成了先进的基于视觉的Transformer模型RT-DETR、DeepSORT跟踪、速度估计、刹车灯检测以及自适应制动机制。该系统通过利用来自多摄像头流的刹车灯信号和轨迹数据，基于车辆的相对加速度、距离以及检测到的制动行为，计算综合碰撞风险评分，从而提升场景感知能力。V-CAS在Jetson Orin Nano平台上实现，能够进行实时碰撞风险评估并通过自适应制动主动缓解风险。研究在多个数据集上进行了全面的训练过程以进行对比分析，随后通过迁移学习对选定的目标检测模型进行了微调。该系统在来自YouTube的车辆碰撞数据集（CCD）上以及通过实时实验进行了严格评估，实现了超过98%的准确率，平均主动预警时间为1.13秒。结果表明，相较于传统的单摄像头方法，该系统在目标检测与跟踪方面有显著改进，从而提升了防碰撞性能。本研究证明了低成本、多摄像头嵌入式视觉Transformer系统通过增强的环境感知和主动防碰撞机制，在推动汽车安全进步方面的潜力。