Abnormal driving behavior recognition method and system based on multi-modal data fusion

By using multimodal data fusion to identify abnormal driver states and assess fleet risks, a collaborative control strategy is generated, which solves the problem of driver abnormalities affecting fleet safety in existing technologies. This enables proactive risk management at the fleet level and improves the safety and reliability of the collaborative driving system.

CN122058928BActive Publication Date: 2026-06-23SICHUAN UNIVERSITY OF SCIENCE AND ENGINEERING
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Patent Information

Application Number
CN202610542169.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-04-23
Publication Date
2026-06-23
Estimated Expiration
2046-04-23

AI Technical Summary

Technical Problem

Existing cooperative driving systems fail to effectively incorporate driver behavior into the fleet-level cooperative safety assessment and decision-making system. This results in follower vehicles being unable to proactively perceive non-dynamic risks when the lead vehicle driver exhibits abnormal behavior, leading to fleet safety threats.

Method used

By using multimodal data fusion to identify abnormal driver states, determine the role of vehicles in the fleet, simulate the probability distribution of future vehicle trajectories under abnormal states, assess fleet risks, generate fleet collaborative control strategies, and transmit risk information through vehicle-to-vehicle communication.

Benefits of technology

It enables seamless information transmission across the entire chain of driver-related risks, from risk warnings for the vehicle itself to coordinated action signals at the fleet level, thereby enhancing the fleet's proactive response capabilities and strengthening the robustness and reliability of the cooperative driving system.

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Abstract

The application discloses an abnormal driving behavior recognition method and system based on multi-modal data fusion, and particularly relates to the technical field of multi-vehicle cooperative driving safety control, and is used for solving the problem that the existing cooperative driving system cannot convert the abnormal behavior risk of a driver into a cooperative control instruction at the vehicle fleet level; the abnormal state of the driver is recognized by collecting and fusing multi-modal sensor data of the vehicle; when the vehicle is a lead vehicle of a vehicle fleet, the comprehensive risk of the abnormal state to the safety of the vehicle fleet is evaluated based on the abnormal state through probabilistic evolution simulation; the expected effect of different vehicle fleet control strategies is simulated according to the risk evaluation result, and the best strategy is selected; vehicle fleet risk information containing the strategy is generated and sent to all following vehicles through vehicle-to-vehicle communication; the whole process from single-vehicle driver state monitoring to vehicle fleet level cooperative risk prevention and control is realized, and the overall safety of the vehicle fleet system is improved.
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