Real-time　detection　of　the　pavement　environment　is　an　important　part　of　autonomous　driving　technology.　This　paper　presents　a　real-time　vehicle　detection　system　based　on　embedded　devices.　Based　on　the　existing　yolov3-tiny　neural　network　structure,　this　paper　proposes　a　new　neural　network　structure　-　the　YOLO　v3-live.　Tailoring　the　network　layer　structure　of　YOLOv3-tiny,　and　quantify　the　network　parameters　in　the　network.　Reducing　the　complexity　of　computing　in　embedded　devices,　making　the　proposed　neural　network　structure　more　suitable　for　embedded　devices.　The　new　structure　is　tested,　before　quantization　the　YOLO　v3-live＇s　detection　precision　can　achieve　87.79%　mAP　on　the　test　set,　after　quantization　of　network　parameters　can　achieve　69.79%　mAP　and　detection　speed　can　achieve　28　FPS.　©　2019　IEEE.