Method for estimating a probability distribution of the maximum coefficient of friction at a current and/or future waypoint of a vehicle

Patent metadata

Field	Value
Publication number	US-11091165-B2
Application number	US-201716307227-A
Country	US
Kind code	B2
Filing date	May 11, 2017
Priority date	Jun 7, 2016
Publication date	Aug 17, 2021
Grant date	Aug 17, 2021

Abstract

A method for estimating a probability distribution of the maximum coefficient of friction (μ) at a current and/or future waypoint (s, s*) of a vehicle. According to the method, a first probability distribution (WV 1 ) for the maximum coefficient of friction (μ) at the waypoint (s) of the vehicle is determined by a Bayesian network from a first data set, which is, or was determined, for one, in particular current, waypoint (s) of the vehicle and which characterizes the maximum coefficient of friction (μ) at the waypoint (s) of the vehicle.

First claim

The invention claimed is:

1. A method for estimating a probability distribution of a maximum coefficient of friction of at least one of a current and a future waypoint of a vehicle, the method comprising:
determining topology of a roadway of the current waypoint along which the vehicle is currently traveling as well as the future waypoint of the vehicle via map data provided by a navigation system of the vehicle;
determining at least one characteristic of at least one tire of the vehicle via at least one tire sensor of the vehicle;
determining a first data set for the current waypoint of the vehicle which characterizes the maximum coefficient of friction at the current waypoint of the vehicle; and
determining, from the first data set, a first probability distribution for the maximum coefficient of friction at the waypoint of the vehicle by a Bayesian network.

2. The method of claim 1 , further comprising estimating a second probability distribution for the maximum coefficient of friction at the future waypoint of the vehicle from a second data set of the future waypoint by the Bayesian network; and
determining a resulting probability distribution using a combination of the first and the second probability distributions.

3. The method of claim 2 , wherein the Bayesian network forms a multi-level causal chain, and at least one of:
subdividing the input nodes into one or more hierarchical levels, and
subdividing the output nodes into one or more hierarchical levels.

4. The method according to claim 2 , further comprising determining the second probability distribution by processing, as the second data set, data about the future waypoint which comprises at least one of:
data related to a preceding vehicle, data related to a driver of a preceding vehicle, road topology, type of road surface, road conditions, road surface, type of intermediate medium, weather data comprising outside temperature, rain intensity, humidity, barometric pressure, precise map data (PRD), or data from intelligent infrastructure components.

5. The method according to claim 2 , further comprising performing a convex combination for combining the first and the second probability distributions (WV 1 , WV 2 ).

6. The method according to claim 5 , further comprising, during the combination, processing a projection parameter (α) which represents a measure of a preview by weighting the first and the second probability distributions (WV 1 , WV 2 ).

7. The method according to claim 6 , further comprising processing at least one further projection parameter (α) in the combination.

8. The method according to claim 6 , further comprising selecting the projection parameter (α) as a function of at least one of:
a determined entropy of a relevant probability distribution; and
data of a sensor of the vehicle available for determination of the maximum coefficient of friction (μ).

9. The method according to claim 2 , further comprising processing, in addition to the second probability distribution for the maximum coefficient of friction (μ) at the future waypoint (s*), one or more further probability distributions for the maximum coefficient of friction (μ) at one or more further future waypoints (s*) to determine the resulting probability distribution for the maximum coefficient of friction (μ).

10. The method according to claim 1 , further comprising forming a three-stage causal chain from nodes and edges of the Bayesian network, wherein
input nodes of the Bayesian network each represent a factor influencing the maximum coefficient of friction,
output nodes, which depend on the maximum coefficient of friction, represent either an impact or an effect of the maximum coefficient of friction, and
any conditional interdependence, between the input node and either the maximum coefficient of friction or the maximum coefficient of friction and the output node, is represented by the edges.

11. The method according to claim 1 , further comprising assigning a conditional probability to every node of the Bayesian network.

12. The method according to claim 1 , further comprising defining at least one of the first data set and a second data set as:
at least one of a longitudinal and a lateral acceleration of the vehicle, yaw rate, wheel speed(s), of estimation of a friction coefficient due to longitudinal dynamics of the vehicle;
vehicle speed;
slip;
estimation of a friction coefficient based on lateral dynamics of the vehicle;
estimation of a friction coefficient based on a combination of the longitudinal and the lateral dynamics of the vehicle;
type of driver or driving style;
outside temperature, intensity of rain, humidity, or barometric pressure
moisture on road surface, type and condition of roadway or type of intermediate medium.

13. The method according to claim 1 , wherein at least one of the first data set and a second data set being either provided or determined from one or more data sources, the data sources being:
electronic stability control/brake, radar, camera, lidar, ultrasound, infrared system, driving state observer, steering system, weather data service, precise map data, and intelligent infrastructure components.

14. The method according to claim 1 , further comprising determining a prior probability distribution for the maximum coefficient of friction for determining the first probability distribution from the first data set;
for each output node, determining from the first data set a likelihood-probability distribution from an observation of a concrete output value at the current waypoint;
for at least some output nodes, determining, in a correction step, a relevant posterior probability distribution for the maximum coefficient of friction using a Bayes formula from the prior probability distribution and the likelihood-probability distribution.

15. The method of claim 14 , further comprising evaluating a relevant posterior probability distribution for the maximum coefficient of friction.

16. The method of claim 14 , further comprising performing an additional consideration of vehicle acceleration in at least one of the determination and evaluation of the likelihood-probability distribution.

17. The method according to claim 16 , further comprising performing evaluation of the relevant posterior probability distribution and selection of one of the posterior probability distributions by an entropy.

18. The method according to claim 1 , further comprising selecting the future waypoint to be adaptive.

19. A method for estimating a probability distribution of a maximum coefficient of friction of at least one of a current and a future waypoint of a vehicle, the method comprising:
determining a first data set for the current waypoint of the vehicle which characterizes the maximum coefficient of friction at the current waypoint of the vehicle; and
determining, from the first data set, a first probability distribution for the maximum coefficient of friction at the waypoint of the vehicle by a Bayesian network;
determining a prior probability distribution for the maximum coefficient of friction for determining the first probability distribution from the first data set;
for each output node, determining from the first data set a likelihood-probability distribution from an observation of a concrete output value at the current waypoint;
for at least some output nodes, determining, in a correction step, a relevant posterior probability distribution for the maximum coefficient of friction using a Bayes formula from the prior probability distribution an

Assignees

• Zahnradfabrik Friedrichshafen
• Lucas Automotive Gmbh

Inventors

• Schlegel Matthias
• Abhau Jochen
• Gilsdorf Heinz-Joachim
• King Julian
• Köhler Jochen
• Krimmel Horst
• Niederbrucker Gerhard
• Turner Lara Ruth
• Wagner Volker
• Zdych Robert
• Adler Vanessa
• Heinrichs-Bartscher Sascha
• Raffauf Ralf
• Simon Holger
• Stein Walter

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• B60W2556/50

  of positioning data, e.g. GPS [Global Positioning System] data · CPC title

• B60W2050/0051

  combined with averaging · CPC title

• B60W2520/26

  Wheel slip · CPC title

• B60W40/068 Primary

  Road friction coefficient · CPC title