Danger early warning method and system based on vehicle driving and storage medium

Abstract

The invention discloses a danger early warning method and a danger early warning system based on vehicle driving, and a storage medium. The danger early warning method comprises the steps of: acquiring movement state information of each set of terminal equipment in real time, and enabling the terminal equipment to represent the terminal equipment associated with driving vehicles or pedestrians; and according to the real-time position of each set of terminal equipment in a road map and in combination with the movement state information of the terminal equipment, predicting the terminal equipment with a traffic risk. The danger early warning method and the danger early warning system have the technical scheme that: by adopting the danger early warning method and the danger early warning system, the pedestrians and vehicles participating in traffic on the road can be monitored in real time; movement state information of the terminal equipment associated with pedestrians or vehicles on theroad can be collected; pedestrians and vehicles with traffic risks are predicted in time, and therefore the problem that the risks cannot be predicted in time due to emergencies such as pedestrian intrusion or existence of a detection blind area when the vehicle detects risks by means of a detector in the prior art is solved.

Description

technical field [0001] The present invention relates to the field of communication technology, in particular to a method, system and storage medium for dangerous early warning based on vehicle driving. Background technique [0002] Vehicle driving safety is a systemic issue. The unmanned driving system perceives road surface information from an individual perspective through perception sensors such as vision sensors, laser sensors, and radar sensors. Relying on GPS+IMU to provide richer information such as yaw rate, angular acceleration, etc., to help the positioning and decision-making control of autonomous vehicles. For example, the application number 201910542034.6 "Intelligent Connected Hybrid Vehicle Active Collision Avoidance Enhanced Learning Control System and Method" discloses an active collision avoidance enhanced learning control system and method. The vehicle obtains traffic information through the data perception module, and the vehicle autonomously detects Th...

AI Technical Summary

Problems solved by technology

However, there are many traffic accidents that could have been avoided, but they still happened. It is not as good as for incidents such as "ghost probes", which are difficult to deal with regardless of automatic driving or highly skilled drivers.

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