BSD camera-based driving risk assessment method and intelligent braking method and device

By using a BSD camera-based driving risk assessment method, which combines object information and motion information of dynamic targets to predict target trajectories and incorporates environmental and driver behavior data, the problem of inaccurate risk assessment in traditional intelligent braking systems is solved, improving the comprehensiveness of assessment results and driving experience.

CN121004960BActive Publication Date: 2026-07-21ANCHE INTELLIGENT STRIP (BEIJING) TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
ANCHE INTELLIGENT STRIP (BEIJING) TECHNOLOGY CO LTD
Filing Date
2025-10-16
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Traditional intelligent braking systems lack dynamism in assessing the risk of targets in the vehicle's blind spot. They cannot combine changes in target behavior with the surrounding environment, resulting in inaccurate assessment results, which are prone to misjudgment and affect the driving experience.

Method used

A driving risk assessment method based on BSD cameras is adopted. By acquiring image data around the vehicle, object information and motion information of dynamic targets are identified, the expected trajectory of the targets is predicted, and risk assessment is carried out by combining environmental and driver behavior data.

Benefits of technology

It enables dynamic risk assessment of targets around the vehicle, improving the accuracy of risk assessment and the reliability of the intelligent braking system, and optimizing the driving experience.

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Abstract

The application provides a BSD camera-based driving risk assessment method and an intelligent braking method and device. The assessment method comprises: acquiring image data of a current driving environment of a vehicle, and acquiring object information in the current driving environment and motion information of dynamic targets in the current driving environment according to the image data; predicting the predicted running trajectories of the dynamic targets according to the object information in the current driving environment and the motion information of the dynamic targets in the current driving environment; and assessing the risk level of the current driving environment based on the predicted running trajectories of the dynamic targets, combining the environmental information of the current driving environment and the driving behavior data of the current driver of the vehicle, and obtaining the risk assessment result of the current driving environment. The application can predict the trajectories of all targets around the vehicle when performing blind area risk assessment of the vehicle, and comprehensively assesses by combining the behavior data of the driver and the environmental data of the current driving environment, thereby effectively improving the accuracy of the assessment result.
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