Construction of a Driving Behavior Safety Monitoring Platform Based on Artificial Intelligence

Abstract

The existing driver driving behavior supervision platform is slightly insufficient in the overall control of fleet safety management. This is reflected in the lack of a clear definition of safety monitoring and the inability to implement timely intervention in case of bad driving behavior. Thus, the current fleet safety accident rate is still high. Based on this background, this paper proposes a safety management method for drivers on the way. This method combines the analytic hierarchy process to analyze the driver's driving behavior, identify the bad driving behavior on the way and push it to the team administrator in time. The team manager can intervene in time when facing the bad driving behavior of drivers. This can effectively prevent and control the occurrence of fleet safety accidents. Combined with this theory and method, this paper designs and implements a safety supervision platform based on driving behavior. The platform mainly includes six functional modules: data preprocessing, driving behavior calculation, safety value calculation, data storage and push, and real-time display. The test shows that the platform can reduce the human factors affecting vehicle driving safety.