Granted Patent B2

US 12,479,405 · App. 18/258,377 · Granted Nov 25, 2025

Inventor: Chidambaram Subramanian (Greensboro, NC)

Assignee: VOLVO TRUCK CORPORATION

B60T8/17636B60T8/17616B60T2210/122B60T2240/03

Abstract

Claims (84)

1 . A method comprising:

receiving, at a processor within a vehicle, from a plurality of tire sensors associated with tires of the vehicle, first road condition data, wherein:

the vehicle is towing a trailer;

the vehicle has first operational parameters which are associated with braking of the vehicle;

the trailer has second operational parameters which are associated with braking of the trailer;

determining, via the processor and based on the first road condition data, a current surface condition;

receiving, at the processor from a plurality of other vehicles, second road condition data;

executing, via the processor, a machine learning algorithm, wherein:

inputs to the machine learning algorithm comprise the first road condition data and the second road condition data; and

outputs of the machine learning algorithm comprise an approaching surface condition; and

modifying, via the processor, at least one of the first operational parameters and the second operational parameters based on:

a distinction between the current surface condition and the approaching surface condition; and

distinct slip ratios of the vehicle and the trailer based on the current surface condition.

2 . The method of claim 1 , further comprising:

generating, via the processor, an operating mode of the vehicle, the operating mode being based the current surface condition, the approaching surface condition, the first operational parameters, and the second operational parameters, the operating mode comprising one of:

a warning mode with a severity, where at least one operational parameter of the first operational parameters and the second operational parameters is to be modified based on a distinction between the current surface condition and the approaching surface condition; and

a poor surface mode, where at least one operational parameter of the first operational parameters and the second operational parameters is to be modified based on the current surface condition, the first operational parameters, and the second operational parameters.

3 . The method of claim 2 , wherein the severity of the warning mode indicates at least one of a deadline for performing the modifying and a risk level for not performing the modifying.

4 . The method of claim 1 , wherein the modifying of at least one of the first operational parameters and the second operational parameters results a slip ratio of the vehicle being within a predetermined threshold difference of a slip ratio of the trailer.

5 . The method of claim 1 , wherein the machine learning algorithm is generated by:

performing a sensitivity analysis which defines correlations between known values current road condition data and known values of future road condition data;

forming, via a computing device, a neural network using the correlations;

converting, via the computing device, the neural network to computer executable code, resulting in the machine learning algorithm.

6 . The method of claim 5 , wherein the correlations are further based on GPS (Global Positioning System) data, traffic data, route data, and weather data.

7 . The method of claim 1 , wherein the second road condition data is also received from roadside infrastructure components.

8 . The method of claim 1 , wherein the modifying of at least one of the first operational parameters and the second operational parameters comprises changing at least one of:

an understeer allowable band;

an oversteer allowable band;

a slip ratio band; and

a pedal movement to acceleration demand.

9 . An articulated vehicle, comprising:

a trailer;

a truck configured to pull the trailer;

a plurality of tire sensors;

a processor;

a non-transitory computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising:

receiving first operational parameters associated with braking of the truck;

receiving second operational parameters associated with braking of the trailer;

receiving, from the plurality of tire sensors, first road condition data;

determining, via the processor and based on the first road condition data, a current surface condition;

receiving, from a plurality of other vehicles, second road condition data;

executing a machine learning algorithm, wherein:

inputs to the machine learning algorithm comprise the first road condition data and the second road condition data; and

outputs of the machine learning algorithm comprise an approaching surface condition; and

generating an operating mode of the vehicle, the operating mode being based the current surface condition, the approaching surface condition, the first operational parameters, and the second operational parameters, the operating mode being one of a plurality of modes selected from a list comprising:

a normal mode, where no changes to at least one operational parameter of the first operational parameters and the second operational parameters are needed based on a distinction between the current surface condition and the approaching surface condition;

a warning mode with a severity, where at least one operational parameter of the first operational parameters and the second operational parameters is to be modified based on a distinction between the current surface condition and the approaching surface condition; and

a poor surface mode, where at least one operational parameter of the first operational parameters and the second operational parameters is to be modified based on the current surface condition, the first operational parameters, and the second operational parameters; and

modifying at least one of the first operational parameters and the second operational parameters based on:

a distinction between the current surface condition and the approaching surface condition; and

distinct slip ratios of the truck and the trailer based on the current surface condition.

10 . The articulated vehicle of claim 9 , wherein the severity of the warning mode indicates a deadline for performing the modifying.

11 . The articulated vehicle of claim 9 , wherein the severity of the warning mode indicates a risk level for not performing the modifying.

12 . The articulated vehicle of claim 9 , wherein the modifying of at least one of the first operational parameters and the second operational parameters results a slip ratio of the truck being within a predetermined threshold difference of a slip ratio of the trailer.

13 . The articulated vehicle of claim 9 , wherein the machine learning algorithm is generated by:

performing a sensitivity analysis which defines correlations between known values current road condition data and known values of future road condition data;

forming, via a computing device, a neural network using the correlations;

converting, via the computing device, the neural network to computer executable code, resulting in the machine learning algorithm.

14 . The articulated vehicle of claim 13 , wherein the correlations are further based on GPS (Global Positioning System) data, traffic data, route data, and weather data.

15 . The articulated vehicle of claim 9 , wherein the second road condition data is also received from roadside infrastructure components.

16 . The articulated vehicle of claim 9 , wherein the modifying of at least one of the first operational parameters and the second operational parameters comprises changing at least one of:

an understeer allowable band;

an oversteer allowable band;

a slip ratio band; and

a pedal movement to acceleration demand.

17 . A non-transitory computer-readable storage medium stored within a vehicle, the non-transitory computer-readable storage medium having instructions stored which, when executed by a processor, cause the processor to perform operations comprising:

receiving, at the processor within the vehicle, from a plurality of tire sensors associated with tires of the vehicle, first road condition data, wherein:

the vehicle is towing a trailer;

the vehicle has first operational parameters which are associated with braking of the vehicle; and

the trailer has second operational parameters which are associated with braking of the trailer;

determining, via the processor and based on the first road condition data, a current surface condition;

receiving, at the processor from a plurality of other vehicles, second road condition data;

executing, via the processor, a machine learning algorithm, wherein:

inputs to the machine learning algorithm comprise the first road condition data and the second road condition data; and

outputs of the machine learning algorithm comprise an approaching surface condition; and

modifying, via the processor, at least one of the first operational parameters and the second operational parameters based on:

a distinction between the current surface condition and the approaching surface condition; and

distinct slip ratios of the vehicle and the trailer based on the current surface condition.

18 . The non-transitory computer-readable storage medium stored within a vehicle of claim 17 , having additional instructions stored which, when executed by the processor, cause the processor to perform operations comprising:

generating, via the processor, an operating mode of the vehicle, the operating mode being based the current surface condition, the approaching surface condition, the first operational parameters, and the second operational parameters, the operating mode comprising one of:

a warning mode with a severity, where at least one operational parameter of the first operational parameters and the second operational parameters is to be modified based on a distinction between the current surface condition and the approaching surface condition; and

a poor surface mode, where at least one operational parameter of the first operational parameters and the second operational parameters is to be modified based on the current surface condition, the first operational parameters, and the second operational parameters.

19 . The non-transitory computer-readable storage medium stored within a vehicle of claim 18 , wherein the severity of the warning mode indicates at least one of a deadline for performing the modifying and a risk level for not performing the modifying.

20 . The non-transitory computer-readable storage medium stored within a vehicle of claim 17 , wherein the modifying of at least one of the first operational parameters and the second operational parameters results a slip ratio of the vehicle being within a predetermined threshold difference of a slip ratio of the trailer.

Assignments (1)

Continuity (1)

References Cited (12)