Method for course prediction in driver assistance systems for motor vehicles

Patent metadata

Field	Value
Publication number	US-8990015-B2
Application number	US-79511406-A
Country	US
Kind code	B2
Filing date	Jan 12, 2006
Priority date	Jan 24, 2005
Publication date	Mar 24, 2015
Grant date	Mar 24, 2015

Abstract

In a method for course prediction in driver assistance systems for motor vehicles, a dynamic course hypothesis is created on the basis of vehicle-dynamics data of the vehicle, which method includes the following steps:
creating an infrastructure course hypothesis on the basis of data from at least one information source that describe the traffic infrastructure; calculating, on the basis of features of the information source, a weighting factor that describes the reliability of the infrastructure course hypothesis; and fusing the infrastructure course hypothesis with the dynamic course hypothesis, with weighting in accordance with the calculated weighting factor, to produce a definitive course hypothesis.

First claim

What is claimed is:

1. A method for providing a course prediction in a driver assistance system of a motor vehicle, comprising:
generating, by the driver assistance system, a dynamic course hypothesis for the vehicle on the basis of vehicle-dynamics data of the vehicle;
generating, by the driver assistance system, an infrastructure course hypothesis for the vehicle on the basis of data from at least one information source that describe a traffic infrastructure;
calculating, by the driver assistance system, on the basis of features of the at least one information source, a weighting factor that describes the reliability of the infrastructure course hypothesis; and
fusing the infrastructure course hypothesis with the dynamic course hypothesis, with weighting in accordance with the calculated weighting factor, to produce a definitive course hypothesis
transmitting the definitive course hypothesis to an adaptive cruise control system.

2. The method as recited in claim 1 , wherein the at least one information source includes at least one of a radar system, a lidar system, a video system, and
a navigation system.

3. The method as recited in claim 2 , wherein the infrastructure course hypothesis is generated by combining data from multiple information sources, and
wherein the data from multiple information sources are adjusted to one another and checked for plausibility, and wherein the weighting factor is varied as a function of the result of at least one of the adjustment and the plausibility check.

4. The method as recited in claim 3 , wherein a separate weighting factor is allocated to each of the data from the multiple information sources, the separate weighting factor indicating at least one of the reliability and plausibility of the corresponding data, and wherein the infrastructure course hypothesis is created by fusion of the data from the multiple information sources, weighted with the respective weighting factors, and
wherein the separate weighting factors are combined to generate the weighting factor used for fusion of the infrastructure course hypothesis with the dynamic course hypothesis.

5. The method as recited in claim 4 , wherein the dynamic course hypothesis and the infrastructure course hypothesis are represented by polynomials of the same form.

6. The method as recited in claim 5 , wherein the polynomials are third order polynomials.

7. The method as recited in claim 1 , further comprising:
initially generating multiple course hypotheses on the basis of data from the at least one information source that describe the traffic infrastructure, wherein each of the multiple course hypotheses describes a course that is possible in light of the traffic infrastructure;
wherein one of the multiple course hypotheses is selected as the infrastructure course hypothesis based on additional information about one of detectable and presumable intentions of the driver.

8. The method as recited in claim 7 , wherein the selection of the infrastructure course hypothesis is made as a function of vehicle-dynamics data of the vehicle.

9. The method as recited in claim 7 , wherein the selection of the infrastructure course hypothesis is made as a function of at least one of the status of a travel-direction indicator and a route information of a navigation system.

10. The method as recited in claim 8 , wherein the selection of the infrastructure course hypothesis is made as a function of at least one of the status of a travel-direction indicator and a route information of a navigation system.

11. A driver assistance system for a vehicle, comprising:
generating, using a microcomputer, a dynamic course hypothesis for the vehicle on the basis of vehicle-dynamics data of the vehicle;
generating, using a microcomputer an infrastructure course hypothesis for the vehicle on the basis of data from at least one information source that describe a traffic infrastructure;
calculating, using a microcomputer, on the basis of features of the at least one information source, a weighting factor that describes the reliability of the infrastructure course hypothesis; and
fusing the infrastructure course hypothesis with the dynamic course hypothesis, with weighting in accordance with the calculated weighting factor, to produce a definitive course hypothesis
transmitting the definitive course hypothesis to an adaptive cruise control system.

Assignees

• Citelli Silvia
• Bosch Gmbh Robert

Inventors

• Citelli Silvia

Classifications

• B60W30/16

  Control of distance between vehicles, e.g. keeping a distance to preceding vehicle · CPC title

• B60W30/146

  Speed limiting · CPC title

• B60W40/02 Primary

  related to ambient conditions · CPC title

• Y02T10/84

  Data processing systems or methods, management, administration · CPC title

• G01C21/26

  specially adapted for navigation in a road network · CPC title