Abstract: The invention discloses a method for identifying a road risk based on a networked vehicle-mounted ADAS, including a construction of a road risk classification system and regional road risk identification, and the following steps: step S1: collecting networked ADAS sensing data, extracting time-to-collision, and braking deceleration; step S2: establishing two-dimensional comprehensive risk indicators, performing clustering, and building a road risk classification system based on clustering results; step S3: establishing a scoring system; step S4: selecting regional roads, divide the regional roads into different sections, and obtaining the two-dimensional comprehensive risk indicators corresponding to each section; step S5: matching the two-dimensional comprehensive risk indicators section with the road risk grade classification system to obtain the frequency of different road risk grades for each road section; step S6: combining with the scoring system.

Abstract: The disclosure provides a lane-level navigation method and system incorporating ADAS fine-sensory data, which comprises the following steps: collecting the real-time road images in front of the vehicle, and pretreating the real-time road images which are collected; extracting lane feature points and determining lane information from the preprocessed images; extracting feature points and contour edge lines of road signs in the lane area where the vehicle is located in the preprocessed images, and combining with lane signs to generate lane sign feature point information; identifying and outputting the corresponding lane number and lane function, and transmitting recognized information to the vehicle navigation center control platform, or transmitting to a mobile terminal navigation APP; generating automatic navigation instructions to provide a driver with precise lane-level movements. By effectively fusing ADAS data, the method of the present invention has the advantages of low cost, good compatibility.