Vehicle speed profile prediction using neural networks

Patent metadata

Field	Value
Publication number	US-9663111-B2
Application number	US-201414291858-A
Country	US
Kind code	B2
Filing date	May 30, 2014
Priority date	May 30, 2014
Publication date	May 30, 2017
Grant date	May 30, 2017

Abstract

A vehicle includes a powertrain having an electric machine and an engine. The vehicle also includes a controller programmed to operate the powertrain according to a predicted vehicle speed profile for a predetermined route segmented according to a group of driving zone types, wherein each driving zone type is associated with a different characteristic speed profile shape and vehicle location. The controller is further programmed to update the predicted segment speed profile in response to deviation between the predicted speed profile and a measured speed profile.

First claim

What is claimed is:

1. A vehicle comprising:
a powertrain including an electric machine and an engine; and
a controller programmed to for each of a plurality of segments defining a route (i) operate the powertrain according to a predicted vehicle speed profile for the segment associated with one of a group of driving zone types each defining a different characteristic speed profile and vehicle location, and (ii) in response to deviation between the predicted speed profile and a measured speed profile, update the characteristic speed profile associated with the driving zone type.

2. The vehicle of claim 1 wherein the controller further comprises at least one neural network processor programmed to partition the route into segments according to driving zone type.

3. The vehicle of claim 1 wherein the controller further comprises at least one neural network processor programmed to generate the predicted vehicle speed profiles based on data corresponding to historical driving patterns.

4. The vehicle of claim 3 wherein the at least one neural network processor further comprises a plurality of neural network processors each programmed to generate one of the predicted speed profiles.

5. The vehicle of claim 1 wherein the group of driving zone types includes a free flow traffic area, a stop sign traffic area, a traffic light traffic area, a turn traffic area, a freeway entrance ramp area, a freeway exit ramp area, or an inter-freeway ramp area.

6. A vehicle comprising:
a powertrain; and
a controller having a neural network assigned to a driving zone type and programmed to operate the powertrain along a route segment defined by the driving zone type according to a predicted speed profile associated with the driving zone type, and update the predicted speed profile for use in subsequent trips along the route segment based on a measured deviation from the predicted speed profile.

7. The vehicle of claim 6 wherein another neural network is programmed to classify each of a series of segments of a route into one of a plurality of driving zone types.

8. The vehicle of claim 7 wherein each of the series of segments of the route is classified based on a speed profile shape that is characteristic of a single driving zone type.

9. The vehicle of claim 7 wherein the plurality of driving zone types includes a free flow traffic area, a stop sign traffic area, a traffic light traffic area, a turn traffic area, a freeway entrance ramp area, a freeway exit ramp area, or an inter-freeway ramp area.

10. The vehicle of claim 6 wherein the neural network is programmed to generate the predicted speed profile based on data corresponding to historical driving patterns.

11. The vehicle of claim 6 wherein the powertrain includes an electric machine and an engine, each capable of selectively providing output torque to propel the vehicle.

Assignees

• Ford Global Tech Llc
• Univ Michigan Regents

Inventors

• Kristinsson Johannes Geir
• Mcgee Ryan Abraham
• Phillips Anthony Mark
• Kuang Ming Lang
• Wang Wenduo
• Park Jungme
• Murphey Yi
• Fang Chen

Classifications

• B60W2555/20

  Ambient conditions, e.g. wind or rain · CPC title

• B60W2552/00

  Input parameters relating to infrastructure · CPC title

• B60W20/12

  using control strategies taking into account route information {(estimation or calculation of non-directly measurable driving parameters B60W40/00)} · CPC title

• B60W40/06 Primary

  Road conditions · CPC title

• B60W10/06 Primary

  including control of combustion engines · CPC title