边缘AI路侧节点实时识别五类交通违规，助力智能网联车协同安全
Edge-AI Perception Node for Cooperative Road-Safety Enforcement and Connected-Vehicle Integration
- 基于YOLOv8 Nano与DeepSORT，实现高精度多目标检测与追踪
- 28-30帧/秒下功耗仅9.6瓦，违规识别准确率97.7%，OCR精度84.9%
- 支持无区域校准的自动违章分析，可向车联网发布安全事件
新兴经济体如印度的快速机动化导致严重执法不均，2023年记录超1100万起交通违规，而每4000辆车仅配一名警力。传统监控与人工开罚单难以应对，亟需自主、协作且节能的边缘智能感知基础设施。本文提出一种实时路侧感知节点，用于多类交通违规分析与安全事件分发，集成YOLOv8 Nano进行高精度多对象检测，DeepSORT实现时序一致的车辆追踪，并结合规则驱动的OCR后处理模块，可识别符合MoRTH AIS 159和ISO 7591标准的破损或多种语言车牌。系统部署于NVIDIA Jetson Nano（128核Maxwell GPU），经TensorRT FP16量化优化，在9.6瓦功耗下保持28至30帧/秒推理速度，对信号闯红灯、人行横道违停、逆向行驶、非法掉头及超速共五类违规的检测准确率达97.7%，OCR精度达84.9%，无需手动设置感兴趣区域。相比YOLOv4 Tiny、PP YOLOE S与Nano DetPlus，平均精度提升10.7%，单位功耗准确率提高1.4倍。除执法外，该节点通过V2X协议向联网车辆与智能交通系统后端发布标准化安全事件（CAM与DENM），证明路侧边缘AI分析可增强协同感知与主动道路安全管理，融入IEEE智能网联车生态。
原文摘要 · Abstract (English)
Rapid motorization in emerging economies such as India has created severe enforcement asymmetries, with over 11 million recorded violations in 2023 against a human policing density of roughly one officer per 4000 vehicles. Traditional surveillance and manual ticketing cannot scale to this magnitude, motivating the need for an autonomous, cooperative, and energy efficient edge AI perception infrastructure. This paper presents a real time roadside perception node for multi class traffic violation analytics and safety event dissemination within a connected and intelligent vehicle ecosystem. The node integrates YOLOv8 Nano for high accuracy multi object detection, DeepSORT for temporally consistent vehicle tracking, and a rule guided OCR post processing engine capable of recognizing degraded or multilingual license plates compliant with MoRTH AIS 159 and ISO 7591 visual contrast standards. Deployed on an NVIDIA Jetson Nano with a 128 core Maxwell GPU and optimized via TensorRT FP16 quantization, the system sustains 28 to 30 frames per second inference at 9.6 W, achieving 97.7 percent violation detection accuracy and 84.9 percent OCR precision across five violation classes, namely signal jumping, zebra crossing breach, wrong way driving, illegal U turn, and speeding, without manual region of interest calibration. Comparative benchmarking against YOLOv4 Tiny, PP YOLOE S, and Nano DetPlus demonstrates a 10.7 percent mean average precision gain and a 1.4 times accuracy per watt improvement. Beyond enforcement, the node publishes standardized safety events of CAM and DENM type to connected vehicles and intelligent transportation system backends via V2X protocols, demonstrating that roadside edge AI analytics can augment cooperative perception and proactive road safety management within the IEEE Intelligent Vehicles ecosystem.
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