ACC longitudinal kinematical modeling method based on relative motion relation

Abstract

The invention discloses an ACC longitudinal kinematical modeling method based on a relative motion relation. When ACC longitudinal kinematical modeling is carried out, self vehicle state parameters and front vehicle state parameters are comprehensively considered, the state quantity xf(k) is set to be [delta d(k), delta v(k), af(k)]T, a system disturbance quantity ap (k) and an expected vehicle distance ddes are introduced, and by means of a fixed time interval strategy, a variable time interval strategy or a quadratic regression model fitting driver following behaviors, the relative motion relation between a self vehicle and a front vehicle and influences of environment parameters are brought into the ACC longitudinal kinematical modeling process. The accuracy of model calculation is improved, prediction errors are reduced, and then the prediction precision and the anti-interference capacity of a following vehicle prediction model are improved.

Description

technical field [0001] The invention belongs to the technical field of vehicle control, and relates to an ACC longitudinal kinematics modeling method, in particular to an ACC longitudinal kinematics modeling method based on a relative motion relationship. Background technique [0002] As an advanced ADAS driver assistance system, ACC (Adaptive Cruise Control) aims to relieve driving fatigue and improve driving comfort and safety. Traditional PID control, LQR control, fuzzy control and other control strategies mostly take vehicle distance control as the main goal, that is, safety is mainly considered while comfort and other requirements are ignored. In recent years, the research on ACC has been diversified, such as full-speed ACC, coordinated CACC for improving traffic flow, ACC and LCA lane change assistance, ACC for simulating driver following behavior, ACC for fuel economy, multi-objective coordinated control ACC, etc. In order to further improve the user utilization rate...

AI Technical Summary

Problems solved by technology

[0007] However, the modeling of this method is only considered from the self-vehicle state, and its current observed state quantity x f (k)=[s f (k), v f (k),a f (k)] T , only includes the state parameters of the ego vehicle (displacement, ego speed, and ego acceleration), lacks the relative motion relationship, and ignores the influence of environmental parameters, resulting in inaccurate model calculation and large prediction errors, which affect the performance of vehicle adaptive cruise control. accuracy

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