System and method for controlling ride comfort in a vehicle

Patent metadata

Field	Value
Publication number	US-10254122-B2
Application number	US-201615151921-A
Country	US
Kind code	B2
Filing date	May 11, 2016
Priority date	May 11, 2016
Publication date	Apr 9, 2019
Grant date	Apr 9, 2019

Abstract

Herein is disclosed methods and systems for automated route analysis and recommendation of preferred routes. Recommended routes may be vehicle specific, and custom-selected based on historical data and weighting factors such as road roughness and traversal time.

First claim

The invention claimed is:

1. A method, the method comprising:
determining, by at least one processor, a first predicted damage factor and a first predicted traversal time associated with a vehicle traversing a first route based on sensor data from one or more sensors on one or more preceding vehicles associated with the first route;
determining, by the at least one processor, a second predicted damage factor and a second predicted traversal time associated with the vehicle traversing a second route different from the first route based on sensor data from one or more sensors on one or more preceding vehicles associated with the second route;
weighting, by the at least one processor, the first predicted damage factor, the first predicted traversal time, the second predicted damage factor, and the second predicted traversal time, to obtain, respectively, a weighted first predicted damage factor, a weighted first predicted traversal time, a weighted second predicted damage factor, and a weighted second predicted traversal time;
calculating, by the at least one processor, a recommended route based on the weighted first predicted damage factor, the weighted second predicted damage factor, the weighted first predicted traversal time, and the weighted second predicted traversal time; and
communicating, by a communications module coupled to the at least one processor, the recommended route to a user device of a user;
wherein the communicated recommended route is configured such that the user device, upon receiving the recommended route, carries out an action selected from: modifying a graphical user interface to display the recommended route; and initiating an autonomous driving system to navigate a vehicle along the recommended route.

2. The method of claim 1 , wherein the user is the autonomous driver system and the recommended route comprises a set of instructions configured to cause the autonomous driver system to proceed along the recommended route.

3. The method of claim 1 , wherein the user is a human driver and the recommended route comprises a set of directions for output in a driver alert system.

4. The method of claim 1 , wherein the first route is between a first geo-location and a second geo-location, and wherein the second route is between the first geolocation and second geo-location.

5. The method of claim 1 , wherein the first route is a sequence of connected road segments, each with an associated roughness factor, and wherein the first predicted damage factor is determined based on the roughness factors of the road segments of the first route, and wherein the second route is a sequence of connected road segments, each with an associated roughness factor, and wherein the second predicted damage factor is determined based on the roughness factors of the road segments of the second route.

6. The method of claim 1 , wherein the determined first and second predicted damage factors are specific to the vehicle.

7. The method of claim 1 , wherein calculating the recommended route further factors a history of the vehicle selected from historical sensor data collected by the vehicle along at least one of the road segments on the recommended route, and an aggregated wear factor determined from sensor data collected by the vehicle over a period of the vehicle's history.

8. The method of claim 1 , wherein the first route is a sequence of connected road segments, each road segment having an associated roughness factor, the roughness factor being determined from historical sensor readings obtained from the one or more preceding vehicles fitted with one or more sensors and traversing the road segment.

9. The method of claim 1 , wherein the determined first and second predicted damage factors are determined for an average speed for the first and second routes, respectively.

10. The method of claim 1 , wherein the recommended route is selected to minimize damage to the vehicle and to simultaneously minimize time required to traverse the recommended route.

11. The method of claim 1 , wherein the recommended route comprises a recommended sequence of connected road segments and wherein the vehicle traverses the recommended sequence of road segments of the recommended route.

12. A method for reducing damage to a vehicle traveling from a first geo-location to a second geo-location, the method comprising:
transmitting, from a server including at least one processor, via a network, a recommended route to a device on the vehicle, the device configured to receive the recommended route and instruct a user to take the recommended route, wherein the recommended route is selected from at least two available routes connecting the first and second geo-locations, and is selected at least in part to optimize predicted traversal time and predicted damage factor, the predicted traversal time and predicted damage factor determined based on sensor data obtained from one or more sensors on one or more vehicles;
wherein the transmitted recommended route is configured such that the device on the vehicle, upon receiving the recommended route, carries out an action selected from: modifying a graphical user interface to display the recommended route; and initiating an autonomous driving system to navigate the vehicle along the recommended route.

13. The method of claim 12 , further comprising determining an identifying characteristic of the vehicle and a status of the vehicle, wherein the recommended route is selected at least in part on the determined identifying characteristic and status.

14. The method of claim 12 , wherein the user is the autonomous driver system and the recommended route comprises a set of instructions configured to cause the autonomous driver system to proceed along the recommended route.

15. The method of claim 12 , wherein the user is a human driver and the recommended route comprises a set of directions for output in a driver alert system.

16. A system comprising a processor, a memory coupled to the processor, the memory configured to store program instructions for instructing the processor to:
(a) receive an inquiry from a user device specifying a first geo-location and a second geo-location,
(b) calculate an aggregate predicted traversal time and aggregate predicted damage factor for at least two available routes between the first and second geolocations, the aggregate predicted traversal time and the aggregate predicted damage factor being determined based on sensor data obtained from one or more sensors on one or more vehicles,
(c) select a recommended route from the at least two available routes, the selection at least in part made to optimize predicted traversal time and predicted damage factor, and
(d) transmit, via a network and a communications module, the recommended route to the user device, wherein the transmitted recommended route is configured such that the user device, upon receiving the recommended route, carries out an action selected from: modifying a graphical user interface to display the recommended route; and initiating an autonomous driving system to navigate a vehicle along the recommended route.

17. The system of claim 16 , wherein the memory is further configured to store program instructions for instructing the processor to:
(e) receive sensor data from a plurality of vehicles traversing a road segment,
(f) calculate a predicted traversal time and a predicted damage factor for the road segment based on the received sensor data, and
(g) repeat steps (e) and (f) for a plurality of road segments to calculate a plurality of predicted traversal times and predicted

Assignees

• IBM

Inventors

• Bills Tierra
• Bryant Reginald
• Kaguma David
• Tatsubori Michiaki
• Walcott Aisha
• Wamburu John Mbari

Classifications

• G05D1/0285

  using signals transmitted via a public communication network, e.g. GSM network · CPC title

• G01C21/367

  Details, e.g. road map scale, orientation, zooming, illumination, level of detail, scrolling of road map or positioning of current position marker · CPC title

• G05D1/0212

  with means for defining a desired trajectory (involving a plurality of land vehicles G05D1/0287) · CPC title

• G01C21/3484 Primary

  Personalized, e.g. from learned user behaviour or user-defined profiles · CPC title

• G05D1/0088

  characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00) · CPC title