Personalized Routing Based on Driver Fatigue Map

Patent metadata

Field	Value
Publication number	US-2022011132-A1
Application number	US-202117486335-A
Country	US
Kind code	A1
Filing date	Sep 27, 2021
Priority date	Mar 29, 2019
Publication date	Jan 13, 2022
Grant date	—

Abstract

The disclosure relates to technology for a navigation system that enhances the safety of drivers using fatigue detection mapping. The navigation system accesses data sources storing map data a route for drivers of one or more first vehicles. Based on the map data, a personalized fatigue map for a driver of a second vehicle is generated based on the map data. The personalized fatigue map displays predicted driver fatigue of the driver of the second vehicle on the route. Drivers in the first and second vehicles are monitored to detect driver fatigue and a level of the driver fatigue is measured according to a calculated fatigue score. When driver fatigue is detected, a recommendation is output to the driver of the second vehicle that is based on the level of the driver fatigue. The personalized fatigue map is updated to reflect the recommendation.

First claim

What is claimed is:

1 . A computer-implemented method, comprising:
accessing one or more data sources storing map data including historical fatigue data and current fatigue data for one or more segments of a route for drivers of one or more first vehicles; generating a personalized fatigue map for a driver of a second vehicle based on the map data obtained by accessing the one or more data sources, where the personalized fatigue map displays predicted driver fatigue of the driver of the second vehicle for the one or more segments on the route; monitoring the drivers of the one or more first vehicles and the second vehicles to detect driver fatigue based on readings captured by one or more sensors, where a level of the driver fatigue is measured according to a fatigue score; and outputting a recommendation, upon detection of the driver fatigue, to the driver of the second vehicle that is based on the level of the driver fatigue and updating the personalized fatigue map to reflect the recommendation.

2 . The computer-implemented method of claim 1 , wherein
the map data further includes online map data acquired from an online mapping data source; the historical fatigue data is collected for the drivers of the one or more first vehicles during a specified period in the past, and the historical fatigue data is used to generate a historical fatigue map that indicates the driver fatigue in each of the one or more first vehicles along the one or more segments of the route; and the current fatigue data is collected for the drivers of the one or more first vehicles in real-time, and the current fatigue data is used to generate a current fatigue map that indicates the driver fatigue in each of the one or more first vehicles along the one or more segments of the route.

3 . The computer-implemented method of claim 2 , wherein generating the personalized fatigue map comprises applying the online map, the historical fatigue map and the current fatigue map to a learning algorithm to generate the personalized fatigue map.

4 . The computer-implemented method of claim 2 , further comprising:
calculating the fatigue score for the drivers in the one or more first vehicles based on a set of parameters and an objective function; and when the drivers in the one or more first vehicles is a neighboring driver of the driver in the second vehicle and poses a safety risk to the driver of the second vehicle based on the detected driver fatigue of the drivers of the one or more first vehicles, alerting the driver of the second vehicle to avoid the neighboring driver determined to pose a safety risk as the recommendation.

5 . The computer-implemented method of claim 4 , further comprising:
updating the historical fatigue map and the current fatigue map to reflect the level of fatigue of the drivers in the one or more first vehicles; and updating the personalized fatigue map to alter the route of the second vehicle to avoid the neighboring driver determined to pose a safety risk.

6 . The computer-implemented method of claim 5 , further comprising:
calculating the fatigue score for the driver in the second vehicle based on a set of parameters and an objective function when the driver in the second vehicle is detected to have driver fatigue; and alerting the driver of the second vehicle based on the historical fatigue data and current fatigue data for the drivers of the one or more first vehicles and the calculated fatigue score of the driver of the second vehicle.

7 . The computer-implemented method of claim 6 , further comprising:
updating the personalized fatigue map to alter the route of the second vehicle based on the calculated fatigue score for the driver of the second vehicle; and updating the historical fatigue map and the current fatigue map to reflect the level of fatigue of the drivers in the one or more first vehicles.

8 . The computer-implemented method claim of 4 , wherein the set of parameters includes one or more of a time duration, number of users, road scenario, fatigue time, fatigue duration, climate and driver long term driving patterns.

9 . The computer-implemented method of claim 5 , wherein the altering comprises:
providing a warning to the driver of the second vehicle when the level of fatigue is calculated to be a first level; providing a warning and an alternative route to the driver of the second vehicle to a nearby location for rest when the level of fatigue is calculated to be a second level; and providing a warning to the driver of the second vehicle and enter the second vehicle into autonomous driving mode when the level of fatigue is calculated to be a third level.

10 . A device, comprising:
a non-transitory memory storage comprising instructions; and one or more processors in communication with the memory, wherein the one or more processors execute the instructions to:
access one or more data sources storing map data including historical fatigue data and current fatigue data for one or more segments of a route for drivers of one or more first vehicles;
generate a personalized fatigue map for a driver of a second vehicle based on the map data obtained by accessing the one or more data sources, where the personalized fatigue map displays predicted driver fatigue of the driver of the second vehicle for the one or more segments on the route;
monitor the drivers of the one or more first vehicles and the second vehicles to detect driver fatigue based on readings captured by one or more sensors, where a level of the driver fatigue is measured according to a fatigue score; and
output a recommendation, upon detection of the driver fatigue, to the driver of the second vehicle that is based on the level of the driver fatigue and update the personalized fatigue map to reflect the recommendation.

11 . The device of claim 10 , wherein
the map data further includes online map data acquired from an online mapping data source; the historical fatigue data is collected for the drivers of the one or more first vehicles during a specified period in the past, and the historical fatigue data is used to generate a historical fatigue map that indicates the driver fatigue in each of the one or more first vehicles along the one or more segments of the route; and the current fatigue data is collected for the drivers of the one or more first vehicles in real-time, and the current fatigue data is used to generate a current fatigue map that indicates the driver fatigue in each of the one or more first vehicles along the one or more segments of the route.

12 . The device of claim 11 , wherein generating the personalized fatigue map comprises applying the online map, the historical fatigue map and the current fatigue map to a learning algorithm to generate the personalized fatigue map.

13 . The device of claim 11 , the one or more processors further execute the instructions to:
calculate the fatigue score for the driver in the one or more first vehicles based on a set of parameters and an objective function; and when the driver in the one or more first vehicles is a neighboring driver of the driver in the second vehicle and poses a safety risk to the driver of the second vehicle based on the detected driver fatigue of the driver of the one or more first vehicles, alert the driver of the second vehicle to avoid the neighboring driver determined to pose a safety risk as the recommendation.

14 . The device of claim 13 , wherein the one or more processors further execute the instructions to:
update the historical fatigue map and the current fatigue map to reflect the level of fatigue of the drivers in the one

Assignees

• Huawei Tech Co Ltd

Inventors

• Jia Chengcheng
• Yang Lei
• Yu Hai
• Huang Wei

Classifications

• B60W40/04

  Traffic conditions · CPC title

• B60Q9/008

  for anti-collision purposes · CPC title

• G01C21/3617 Primary

  using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement · CPC title

• B60W2540/22

  Psychological state; Stress level or workload · CPC title

• B60W2554/4046

  Behavior, e.g. aggressive or erratic · CPC title