Method and system for extracting abnormal scenes from driving data of intelligent vehicles based on artificial intelligence

A method for extracting abnormal scenes from driving data of intelligent vehicles based on artificial intelligence (AI), comprising the steps of:

• retrieving basic information of various intelligent vehicles from a plurality of data transmission terminals, and analyzing and processing the basic information to obtain abnormal risk assessment values of the various intelligent vehicles, followed by matching to obtain a driving data extraction ratio of each intelligent vehicle based on the abnormal risk assessment values of the various intelligent vehicles,
• acquiring driving data packets of various intelligent vehicles based on the driving data extraction ratio of each intelligent vehicle, simultaneously acquiring time points marked with driving abnormalities, statistically recording comprehensive scene data of the time points marked with driving abnormalities from the driving data packets, performing classified data processing, and importing the processed data into an integrated AI cloud computing center, and
• receiving the processed comprehensive scene data of time points marked with driving abnormalities by the integrated AI cloud computing center, and performing clustering processing to generate and store abnormal scene data of each intelligent vehicle, wherein
• the performing classified data processing, and importing the processed data into an integrated AI cloud computing center specifically comprises the steps of:
• importing image data, point cloud data and radar data of the time points marked with driving abnormalities into an image feature extraction module, a point cloud geometric feature extraction module and a radar feature extraction module local to the AI cloud computing center, and performing corresponding data processing to obtain image feature data, point cloud geometric feature data and radar feature data of the time points marked with driving abnormalities;
• comparing and analyzing control system data of the time points marked with driving abnormalities with a pre-stored control system data check set in a local database to obtain a control system state characteristic value of the time points marked with driving abnormalities, wherein the control system state characteristic value of the time points marked with driving abnormalities is used for confirming whether a control system of time points marked with driving abnormalities is normal or not, it is determined that the control system of at a certain time point marked with driving abnormality is abnormal if the control system state characteristic value at the certain time point marked with driving abnormality is greater than or equal to a preset control system state abnormal threshold value;
• and it is determined that the control system state characteristic value at a certain time point marked with driving abnormality is normal if the control system state characteristic value at the certain time point marked with driving abnormality is less than the control system state abnormal threshold value;
• statistically recording the time points marked with driving abnormalities when the control system is abnormal as time points marked with each abnormal control system, and storing image feature data, point cloud geometric feature data and radar feature data of the time points marked with each abnormal control system in a database under a label of the abnormal control system;
• the control system data check set comprising an engine response time threshold, a braking deceleration threshold, a braking distance threshold, a suspension compression threshold, and a suspension rebound speed threshold;
• and
• collectively recording the image feature data, the point cloud geometric feature data, and the radar feature data of the time points marked with driving abnormalities when the control system is normal as processed comprehensive scene data of the time points marked with driving abnormalities.