多摄像头视觉变压器系统实时预判碰撞并自动刹车
V-CAS: A Realtime Vehicle Anti Collision System Using Vision Transformer on Multi-Camera Streams
- 融合多视角视觉与Transformer模型，动态评估风险
- 98%准确率，预警提前1.13秒，优于单摄像头方案
- 适合车载安全系统开发，嵌入式部署成本低
本文提出一种基于多摄像头视觉流的实时车辆防撞系统（V-CAS），通过环境感知实现自适应制动。系统采用RT-DETR视觉变压器模型、DeepSORT跟踪算法、速度估计、刹车灯检测及自适应制动机制，综合计算相对加速度、距离与刹车行为的风险评分。利用多路摄像头的轨迹数据和刹车信号提升场景理解能力。在Jetson Orin Nano上部署，实现实时风险评估与主动干预。基于多个数据集进行训练与对比分析，并通过迁移学习微调检测模型。在YouTube上的汽车碰撞数据集（CCD）及真实场景实验中，系统准确率超98%，平均主动预警时间达1.13秒。结果表明，该系统显著提升了目标检测与追踪性能，相比传统单摄像头方法更有效实现防撞。研究展示了低成本、多摄像头嵌入式视觉变压器系统在增强环境感知与主动避撞方面的潜力。
原文摘要 · Abstract (English)
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.
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