Intelligent automobile following decision and control method based on cognitive risk balance

Abstract

The invention relates to an intelligent automobile following decision and control method based on cognitive risk balance. The method comprises the steps: obtaining a cognitive risk based on the time headway and reverse collision time when an intelligent automobile is in a following working condition; judging whether the risk is within a cognitive risk balance interval or not, if not, predicting the response acceleration of the human driver under the action of the corresponding risk intensity through a function between the longitudinal acceleration and the cognitive risk, taking the response acceleration as anthropomorphic response acceleration of the intelligent automobileto deal with the corresponding risk, and using the anthropomorphic response acceleration to adjust the cognitive risk; keeping the balance; and based on the response acceleration, acquiring the throttle opening and the brake pressure intensity through prediction of an automobile longitudinal inverse dynamic model so that the automobile speed is adjusted in real time, and intelligent automobile following is achieved. Therefore, the application of an anthropomorphic cognitive risk balance mechanism to the longitudinal driving behavior decision of the intelligent automobile is realized, and the driving decision rule of an excellent driver is learned to guide the anthropomorphic automobile following driving of the intelligent automobile.

Description

technical field

[0001] This application relates to the technical field of smart car applications, in particular to a smart car follow-up decision-making and control method based on cognitive risk balance. Background technique

[0002] For smart cars, such as in smart driving and automatic driving, the car-following behavior models used in its longitudinal dynamics motion decision-making mainly include: (1) Stimulus-response models, such as GM model (general motor) and OVM (Optimal velocity model) model ; (2) Safety distance model, such as Gipps model; (3) Social force model, such as IDM (Intelligent driver model) model and LCM (longitudinal control model) model; (4) Statistical learning model, such as neural network model, deep learning model and reinforcement learning models.

[0003] The existing car-following models are usually empirical models or semi-empirical models, which have insufficient physical meaning, insufficient interpretability, poor transferability, and fai...

Examples

Embodiment Construction

[0050] The intelligent car follow-up decision and control method based on cognitive risk balance according to the embodiments of the present application will be described below with reference to the accompanying drawings.

[0051] in particular, figure 1 It is a schematic flowchart of a smart car following decision-making and control method based on cognitive risk balance provided in the embodiment of the present application.

[0052] Such as figure 1 As shown, the smart car follow-up decision-making and control method based on cognitive risk balance includes the following steps:

[0053] In step S101, when it is determined that the smart car is in the vehicle-following condition, based on the distance between the vehicle and the vehicle in front, the speed of the vehicle and the speed of the vehicle in front, the time headway and the reverse collision time between the vehicle in front and the vehicle in front are obtained, And based on the headway and reverse collision ti...

technical field

[0001] This application relates to the technical field of smart car applications, in particular to a smart car follow-up decision-making and control method based on cognitive risk balance. Background technique

[0002] For smart cars, such as in smart driving and automatic driving, the car-following behavior models used in its longitudinal dynamics motion decision-making mainly include: (1) Stimulus-response models, such as GM model (general motor) and OVM (Optimal velocity model) model ; (2) Safety distance model, such as Gipps model; (3) Social force model, such as IDM (Intelligent driver model) model and LCM (longitudinal control model) model; (4) Statistical learning model, such as neural network model, deep learning model and reinforcement learning models.

[0003] The existing car-following models are usually empirical models or semi-empirical models, which have insufficient physical meaning, insufficient interpretability, poor transferability, and fai...

Examples

Embodiment Construction

[0050] The intelligent car follow-up decision and control method based on cognitive risk balance according to the embodiments of the present application will be described below with reference to the accompanying drawings.

[0051] in particular, figure 1 It is a schematic flowchart of a smart car following decision-making and control method based on cognitive risk balance provided in the embodiment of the present application.

[0052] Such as figure 1 As shown, the smart car follow-up decision-making and control method based on cognitive risk balance includes the following steps:

[0053] In step S101, when it is determined that the smart car is in the vehicle-following condition, based on the distance between the vehicle and the vehicle in front, the speed of the vehicle and the speed of the vehicle in front, the time headway and the reverse collision time between the vehicle in front and the vehicle in front are obtained, And based on the headway and reverse collision ti...

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