Man-machine co-driving takeover decision-making, stability control and optimization method

By constructing a dynamic coupling model of the safety field and the human factor capability field, assessing environmental risks and driver capabilities, and optimizing HMI prompts and control allocation, the instability of takeover timing in L3-level human-machine co-driving was resolved, achieving safe and timely transfer of driving control.

CN121291498AActive Publication Date: 2026-01-09JILIN UNIVERSITY
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Patent Information

Application Number
CN202511852484.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-10
Publication Date
2026-01-09
Estimated Expiration
2045-12-10

AI Technical Summary

Technical Problem

Under the current Level 3 human-machine co-driving conditions, the autonomous driving system has difficulty in stably determining the timing of takeover in complex situations, resulting in unstable quality of driver takeover. Existing takeover strategies lack dynamic coupling between driving risks and driver capabilities, and cannot adapt to individual differences and environmental changes.

Method used

Construct a driving safety field model and a driver human factor capability field, assess environmental risks and driver capabilities through multimodal data collection, and combine spatiotemporal continuous representation with the temporal recovery process of driver capabilities to form a dynamically coupled takeover decision, thereby optimizing HMI prompts and control allocation.

Benefits of technology

It enables safe, timely, and efficient transfer of driving control in complex environments, ensuring the quality of takeover and driver safety, and avoiding the problems of misjudgment and mismatched prompts in traditional takeover strategies.

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Abstract

The invention belongs to the technical field of vehicle safety control and man-machine interaction, and particularly relates to a man-machine co-driving takeover decision-making, stability control and optimization method, which comprises the following steps of: A, constructing a driving safety field model, reflecting the influence of a dynamic environment and road elements on a vehicle by constructing the safety field model, and providing a real-time basis for takeover decision-making; b, constructing a driver human factor capability field, and evaluating the takeover capability of the driver through multi-modal data acquisition and real-time analysis; c, dynamically coupling a safety field and a human factor capability field in the takeover decision; d, a man-machine takeover mode judgment and control power distribution method; e, the takeover scheme is screened and optimized, and further screening and adjustment are carried out through the comprehensive cost function of the optimization algorithm and used for being executed in actual operation. The method has the advantages that the handover between the automatic driving system and the driver is intelligently controlled by evaluating the environmental risk, the driver ability and the take-over time in real time, and it is ensured that driving right transfer is safely, timely and efficiently completed.
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