Method for predicting the travel path of a motor vehicle and prediction apparatus

Patent metadata

Field	Value
Publication number	US-2016288787-A1
Application number	US-201415035253-A
Country	US
Kind code	A1
Filing date	Oct 2, 2014
Priority date	Nov 12, 2013
Publication date	Oct 6, 2016
Grant date	—

Abstract

A method for predicting the expected travel path of a moving vehicle by numerical integration of a dynamic vehicle model using at least one rotational travel state and at least one longitudinal travel state is disclosed. To provide a prediction of the expected travel path, which does not depend on map information and provides the maximum possible accuracy even for non-steady-state travel states of a vehicle, time-related function rules for the rotational travel state φ Pre (t) and/or for the longitudinal travel state v Pre (t) are determined, and values for the travel state concerned φ Pre , v Pre are predicted at specific points in time by integration using said function rule φ Pre (t), v Pre (t). In this process, the time-related function rule of the travel state concerned is determined by obtaining respective (rotational or longitudinal) input variables for at least two time-derivatives of the travel state concerned from measured values.

First claim

1 . A method for predicting the expected travel path of a moving vehicle by numerical integration of a dynamic vehicle model using at least one rotational travel state, which is affected by the angular motion of the vehicle, and at least one longitudinal travel state, which is affected by the linear motion of the vehicle, the method comprising:
determining time-related function rules for a predicted rotational travel state and/or for a predicted longitudinal travel state; and prediction values for the travel state concerned at specific points in time by numerical integration using said function rule, wherein the time-related function rule of a predicted travel state is determined by:
obtaining respective rotational longitudinalinput variables for at least two time-derivatives of the travel state concerned from measured values, and
relating the input variables in linear dynamic models of order equal to the number of input variables of the travel state concerned using specified time constants, by determining from the linear model a time-related function rule for prediction values for the input variable concerned, and by analytically integrating the function rule for the prediction values of the input variable.

2 . The method according to claim 1 , wherein the dependent variables obtained using the instantaneous input variables are derived from the linear dynamic model and taken into account as weighting factors in determining the time-related function rules, wherein the weighting factors are updated whenever a new measured value is acquired.

3 . The method according to claim 1 , wherein measured values are acquired for each input variable.

4 . The method according to claim 1 , wherein the speed of the vehicle is used as the longitudinal travel state for the numerical integration, wherein as the input variables for the travel-path prediction, the acceleration and a sudden linear movement are determined from measured values or estimated from measured values.

5 . The method according to claim 1 , wherein a bearing angle of the vehicle is used as the rotational travel state for the numerical integration, wherein as the input variables for the travel-path prediction, the yaw rate and the yaw acceleration are determined from measured values or estimated from measured values.

6 . The method according to claim 1 , wherein the vehicle-specific specified time constants lie in a time range of 0.3 s to 15.0 s.

7 . A prediction apparatus for implementing the method as claimed in claim 1 , wherein the prediction apparatus comprises a measured-value input for connecting to a vehicle-sensor unit of the vehicle.

8 . The prediction apparatus according to claim 7 , wherein the prediction apparatus is allocated to a camera-based or optoelectronic driver assistance system or is integrated in such a driver assistance system.

Assignees

• Valeo Schalter & Sensoren Gmbh

Inventors

• Kraehling Martin

Classifications

• B60W2520/10

  Longitudinal speed · CPC title

• B60W2520/14

  Yaw · CPC title

• B60W2050/0031

  Mathematical model of the vehicle · CPC title

• B60W30/0953 Primary

  the prediction being responsive to vehicle dynamic parameters · CPC title

• B60W30/095 Primary

  Predicting travel path or likelihood of collision · CPC title