Car following control method and related product

By fusing multi-source data and predicting multi-modal trajectories, the risk level of following other vehicles is quantified, a multi-objective cost function is constructed, and vehicle control parameters are adjusted. This solves the lag problem of traditional adaptive cruise control systems, realizes active safety control and rapid risk identification, and improves following safety.

CN121947486APending Publication Date: 2026-05-01LIUZHOU WULING NEW ENERGY VEHICLE CO LTD
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Patent Information

Application Number
CN202511849112.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-09
Publication Date
2026-05-01

AI Technical Summary

Technical Problem

Traditional adaptive cruise control systems rely on local sensors, which have significant lag issues, making it difficult to achieve active safety control and unable to effectively improve the speed and safety of risk identification in following vehicle control.

Method used

By acquiring and fusing multi-source data, multimodal trajectory prediction is performed. Multi-dimensional risk indicators are used to quantify risk levels. A multi-objective cost function is constructed and vehicle control parameters are adjusted to achieve proactive prediction and risk identification of the driving behavior of the vehicle in front.

Benefits of technology

It significantly improves following safety by predicting in advance and adaptively adjusting control parameters to achieve a fast and stable safety response, shorten risk identification time, and increase the success rate of collision avoidance.

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Abstract

The invention discloses a car following control method and a related product. According to the scheme, multi-source data are acquired, and the multi-source data are fused to obtain a fusion result; based on the fusion result, performing multi-modal trajectory prediction on the driving behavior of the preceding vehicle to obtain a prediction result of the driving behavior of the preceding vehicle; the multi-modal trajectory prediction comprises longitudinal motion prediction and transverse motion prediction; quantifying a car following risk by using a multi-dimensional risk index to obtain a risk level; constructing a multi-target cost function based on the fusion result, the front vehicle driving behavior prediction result and a control vector, and solving the multi-target cost function by using a preset constraint condition to obtain an initial control parameter of the vehicle; and on the basis of the risk level, initial control parameters of the vehicle are adjusted, and target control parameters of the vehicle are obtained. Compared with the problem of response lag in adaptive cruise control in the prior art, the adaptive cruise control method has obvious advantages.
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