# US 11,906,318 B2 · Personalized routing based on driver fatigue map

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- Number: US 11,906,318 B2
- Document ID: US11906318B2
- Status: Granted
- Kind: B2
- Application number: 17/486,335
- Filed: 2021-09-27
- Published: 2022-01-13
- Granted: 2024-02-20
- Earliest claimed: 2019-03-29
- Assignees: Huawei Technologies Co., Ltd.
- Applicants: Huawei Technologies Co., Ltd.
- Inventors: Chengcheng Jia; Lei Yang; Hai Yu; Wei Huang
- Web page: https://patentgrep.com/patent/US11906318B2

## CPC

- G01C 21/3617: {using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement}
- B60Q 9/008: {for anti-collision purposes}
- B60W 40/04: Traffic conditions
- B60W 40/08: related to drivers or passengers
- B60W 50/14: Means for informing the driver, warning the driver or prompting a driver intervention
- G01C 21/3484: {Personalized, e.g. from learned user behaviour or user-defined profiles}
- G06N 20/00: Machine learning
- B60W 2040/0818: {Inactivity or incapacity of driver}
- B60W 2050/146: {Display means}
- B60W 2540/22: Psychological state; Stress level or workload
- B60W 2554/4046: Behavior, e.g. aggressive or erratic
- B60W 2556/10: Historical data
- B60W 2556/40: High definition maps
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)
- B60Q: ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
- B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## 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.

## Claims

### Claim 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 neighboring vehicles;
generating a personalized fatigue map for a driver of a current 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 current vehicle for the one or more segments on the route;
monitoring the drivers of the one or more neighboring vehicles and the current vehicle 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;
outputting a recommendation, upon detection of the driver fatigue, to the driver of the current vehicle that is based on the level of the driver fatigue and updating the personalized fatigue map to reflect the recommendation;
updating a historical fatigue map and a current fatigue map to reflect a level of fatigue of the drivers in the one or more neighboring vehicles; and
updating the personalized fatigue map to alter the route of the current vehicle to avoid the drivers in the one or more neighboring vehicles determined to pose a safety risk.

### Claim 2 (depends on 1)

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

### Claim 3 (depends on 2)

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

### Claim 4 (depends on 2)

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

### Claim 5 (depends on 4)

The computer-implemented method of claim 4, further comprising:
calculating the fatigue score for the driver in the current vehicle based on a set of parameters and an objective function when the driver in the current vehicle is detected to have the driver fatigue; and
alerting the driver of the current vehicle based on the historical fatigue data and the current fatigue data for the drivers of the one or more neighboring vehicles and the calculated fatigue score of the driver of the current vehicle.

### Claim 6 (depends on 5)

The computer-implemented method of claim 5, further comprising:
updating the personalized fatigue map to alter the route of the current vehicle based on the calculated fatigue score for the driver of the current 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 neighboring vehicles.

### Claim 7 (depends on 4)

The computer-implemented method of claim 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.

### Claim 8 (depends on 4)

The computer-implemented method of claim 4, wherein the altering comprises:
providing a warning to the driver of the current 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 current 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 current vehicle and enter the current vehicle into autonomous driving mode when the level of fatigue is calculated to be a third level.

### Claim 9

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 neighboring vehicles;
generate a personalized fatigue map for a driver of a current 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 current vehicle for the one or more segments on the route;
monitor the drivers of the one or more neighboring vehicles and the current vehicle 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;
output a recommendation, upon detection of the driver fatigue, to the driver of the current vehicle that is based on the level of the driver fatigue and update the personalized fatigue map to reflect the recommendation;
update a historical fatigue map and a current fatigue map to reflect a level of fatigue of the drivers in the one or more neighboring vehicles; and
update the personalized fatigue map to alter the route of the current vehicle to avoid the drivers in the one or more neighboring vehicles determined to pose a safety risk.

### Claim 10 (depends on 9)

The device of claim 9, wherein the map data further includes online map data acquired from an online mapping data source;
wherein the historical fatigue data is collected for the drivers of the one or more neighboring vehicles during a specified period in the past, and the historical fatigue data is used to generate the historical fatigue map that indicates the driver fatigue in each of the one or more neighboring vehicles along the one or more segments of the route; and
wherein the current fatigue data is collected for the drivers of the one or more neighboring first vehicles in real-time, and the current fatigue data is used to generate the current fatigue map that indicates the driver fatigue in each of the one or more neighboring vehicles along the one or more segments of the route.

### Claim 11 (depends on 10)

The device of claim 10, wherein generating the personalized fatigue map comprises applying the online map data, the historical fatigue map and the current fatigue map to a learning algorithm to generate the personalized fatigue map.

### Claim 12 (depends on 10)

The device of claim 10, the one or more processors further execute the instructions to:
calculate the fatigue score for the driver in the one or more neighboring vehicles based on a set of parameters and an objective function; and
when the driver in the one or more neighboring vehicles is a neighboring driver of the driver in the current vehicle and poses a safety risk to the driver of the current vehicle based on the detected driver fatigue of the driver of the one or more neighboring vehicles, alert the driver of the current vehicle to avoid the neighboring driver determined to pose a safety risk as the recommendation.

### Claim 13 (depends on 12)

The device of claim 12, wherein the one or more processors further execute the instructions to:
calculate the fatigue score for the driver in the current vehicle based on a set of parameters and an objective function when the driver in the current vehicle is detected to have the driver fatigue; and
alert the driver of the current vehicle based on the historical fatigue data and the current fatigue data for the drivers of the one or more neighboring vehicles and the calculated fatigue score of the driver of the current vehicle.

### Claim 14 (depends on 13)

The device of claim 13, wherein the one or more processors further execute the instructions to:
update the personalized fatigue map to alter the route of the current vehicle based on the calculated fatigue score for the driver of the current vehicle; and
update the historical fatigue map and the current fatigue map to reflect the level of fatigue of the drivers in the one or more neighboring vehicles.

### Claim 15 (depends on 12)

The device of claim 12, 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.

### Claim 16 (depends on 12)

The device of claim 12, wherein the altering comprises:
provide a warning to the driver of the current vehicle when the level of fatigue is calculated to be a first level;
provide a warning and an alternative route to the driver of the current vehicle to a nearby location for rest when the level of fatigue is calculated to be a second level; and
provide a warning to the driver of the current vehicle and enter the current vehicle into autonomous driving mode when the level of fatigue is calculated to be a third level.

### Claim 17

A non-transitory computer-readable medium storing computer instructions, that when executed by one or more processors, cause the one or more processors to perform the steps of:
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 neighboring vehicles;
generating a personalized fatigue map for a driver of a current 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 current vehicle for the one or more segments on the route;
monitoring the drivers of the one or more neighboring vehicles and the current vehicle 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;
outputting a recommendation, upon detection of the driver fatigue, to the driver of the current vehicle that is based on the level of the driver fatigue and updating the personalized fatigue map to reflect the recommendation;
updating a historical fatigue map and a current fatigue map to reflect a level of fatigue of the drivers in the one or more neighboring vehicles; and
updating the personalized fatigue map to alter the route of the current vehicle to avoid the drivers in the one or more neighboring vehicles determined to pose a safety risk.

### Claim 18 (depends on 17)

The non-transitory computer-readable medium of claim 17, wherein the map data further includes online map data acquired from an online mapping data source;
wherein the historical fatigue data is collected for the drivers of the one or more neighboring vehicles during a specified period in the past, and the historical fatigue data is used to generate the historical fatigue map that indicates the driver fatigue in each of the one or more neighboring vehicles along the one or more segments of the route; and
wherein the current fatigue data is collected for the drivers of the one or more neighboring vehicles in real-time, and the current fatigue data is used to generate the current fatigue map that indicates the driver fatigue in each of the one or more neighboring vehicles along the one or more segments of the route.

### Claim 19 (depends on 18)

The non-transitory computer-readable medium of claim 18, wherein generating the personalized fatigue map comprises applying the online map data, the historical fatigue map and the current fatigue map to a learning algorithm to generate the personalized fatigue map.

### Claim 20 (depends on 19)

The non-transitory computer-readable medium of claim 19, further causing the one or more processors to perform the steps of:
calculating the fatigue score for the drivers in the one or more neighboring vehicles based on a set of parameters and an objective function; and
when the drivers in the one or more neighboring vehicles is a neighboring driver of the driver in the current vehicle and poses a safety risk to the driver of the current vehicle based on the detected driver fatigue of the drivers of the one or more neighboring vehicles, alerting the driver of the current vehicle to avoid the neighboring driver determined to pose a safety risk as the recommendation.

### Claim 21 (depends on 20)

The non-transitory computer-readable medium of claim 20, cause the one or more processors to perform the steps of:
calculating the fatigue score for the driver in the current vehicle based on a set of parameters and an objective function when the driver in the current vehicle is detected to have the driver fatigue; and
alerting the driver of the current vehicle based on the historical fatigue data and the current fatigue data for the drivers of the one or more neighboring vehicles and the calculated fatigue score of the driver of the current vehicle.

### Claim 22 (depends on 21)

The non-transitory computer-readable medium of claim 21, further comprising:
updating the personalized fatigue map to alter the route of the current vehicle based on the calculated fatigue score for the driver of the current 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 neighboring vehicles.

### Claim 23 (depends on 20)

The non-transitory computer-readable medium of claim 20, 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.

### Claim 24 (depends on 20)

The non-transitory computer-readable medium of claim 20, wherein the altering comprises:
providing a warning to the driver of the current 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 current 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 current vehicle and enter the current vehicle into autonomous driving mode when the level of fatigue is calculated to be a third level.