Autonomous driving takeover methods and systems

CN122078445APending Publication Date: 2026-05-26HUIZHOU DESAY SV AUTOMOTIVE
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HUIZHOU DESAY SV AUTOMOTIVE
Filing Date
2026-03-06
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing autonomous driving takeover technologies suffer from signal distortion or perception blind spots in harsh environments, leading to takeover delays and increased accident rates, and are unable to be reliably triggered in critical emergency scenarios.

Method used

By integrating a diamond NV color center quantum sensor, an infrared camera, and a roadside MEC system, real-time global traffic data is acquired, the global risk field and driver cognitive arousal index are calculated, and combined with the vehicle-side risk field, the human-machine control weights are dynamically calculated to achieve autonomous driving takeover.

Benefits of technology

It achieves precise autonomous driving takeover in harsh environments, reduces accident rates, and improves system reliability and driver takeover capabilities through quantitative assessment.

✦ Generated by Eureka AI based on patent content.

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Abstract

This application relates to an autonomous driving takeover method and system. The method includes: obtaining a global risk field based on real-time global data, where the global risk field represents the risk level of each area, forming a unified global view beyond the perspective of a single vehicle for subsequent judgment; calculating the cognitive arousal index of the target vehicle driver based on the global risk field and the first real-time physiological data of the target vehicle driver, quantifying the driver's current cognitive state and takeover readiness; calculating the driver's takeover capability decay index based on the second real-time physiological data of the target vehicle driver and the cognitive arousal index; obtaining the vehicle-side risk field based on the target vehicle's perception data; calculating a comprehensive risk field based on the vehicle-side risk field and the global risk field; obtaining human-machine control weights based on the driver's takeover capability decay index and the comprehensive risk field; and realizing autonomous driving takeover based on the human-machine control weights, achieving more precise autonomous driving takeover.
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