# US 12,759,558 B2 · Convolutional neural networks for pavement roughness assessment using calibration-free vehicle dynamics

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- Number: US 12,759,558 B2
- Document ID: US12759558B2
- Status: Granted
- Kind: B2
- Application number: 18/554,891
- Filed: 2022-04-15
- Published: 2024-06-13
- Granted: 2026-10-13
- Earliest claimed: 2021-04-15
- Assignees: Arizona Board of Regents on Behalf of the University of Arizona
- Applicants: Arizona Board of Regents On Behalf of the University of Arizona
- Inventors: Hongki Jo; Jong-Hyun Jeong; Gregory Ditzler
- Web page: https://patentgrep.com/patent/US12759558B2

## CPC

- B60W 40/06: Road conditions
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/09: Supervised learning
- G07C 5/02: Registering or indicating driving, working, idle, or waiting time only (apparatus forming part of taximeters G07B13/00)
- B60W 2552/35: Road bumpiness, e.g. potholes
- 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
- G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE

## Abstract

The present invention is directed to a method of identifying road roughness by collecting sensor data from a computing device and applying deep learning techniques to estimate the road's roughness index. The present invention features a system comprising computing devices. Each computing device may be mounted to a vehicle. Each computing device may be capable of measuring, while the vehicle is driving on a road. GPS, driving speed, vertical acceleration, and angular velocity of pitch motion. The system may further comprise a neural network computing device comprising a CNN comprising convolutional layers and global average pooling layers. The CNN may be trained by a data set comprising previous data from the plurality of computing devices. The CNN may be capable of accepting the plurality of parameters from the computing devices as input and generating an international roughness index (IRI) value of the road as output.

## Claims

### Claim 1

A system for estimating road roughness under real-world driving conditions through training and implementation of a convolutional neural network (CNN) (210), the system comprising:
a. a plurality of computing devices (100), wherein each computing device (110) is mounted to a vehicle (130), wherein each computing device (110) is capable of measuring, while the vehicle (130) is driving on a road, a plurality of parameters comprising:
i. global positioning system (GPS),
ii. driving speed,
iii. vertical acceleration, and
iv. angular velocity of pitch motion; and
b. a neural network computing device (200) communicatively coupled to the plurality of computing devices (100), the neural network computing device (200) comprising the CNN (210), the CNN (210) comprising a plurality of convolutional layers;
wherein the CNN (210) is capable of accepting the plurality of parameters from the plurality of computing devices (100) as input and generating an international roughness index (IRI) value of the road as output based on the input without calibration of the plurality of computing device (100).

### Claim 2 (depends on 1)

The system of claim 1, wherein the CNN (210) comprises 7 convolutional layers and a plurality of global average pooling layers.

### Claim 3 (depends on 1)

The system of claim 1, wherein each convolutional layer is zero-padded to maintain spatial features, batch normalized to accelerate training speed and prevent overfitting, and has leaky-relu activation for non-linear operations.

### Claim 4 (depends on 1)

The system of claim 1, wherein the plurality of computing devices (100) comprise a plurality of portable computing devices, a plurality of vehicle-embedded computing devices, or a combination thereof, wherein each computing device (110) of the plurality of computing devices (100) is mounted to the vehicle (130) by a mounting device (120) selected from a group comprising a vent clip, a vent magnet, a section clip, a suction magnet, and a CDP clip.

### Claim 5 (depends on 1)

The system of claim 1, wherein measuring GPS comprises GPS resolution enhancement by interpolation and grid snapping.

### Claim 6 (depends on 1)

The system of claim 1, wherein the plurality of computing devices (100) are capable of measuring vertical acceleration and angular velocity at 100 Hz.

### Claim 7 (depends on 1)

The system of claim 1, wherein each parameter of the plurality of parameters is converted into a fixed-size image array before being used as input to the CNN (210).

### Claim 8 (depends on 1)

The system of claim 1, wherein a batch size of the CNN (210) is 64 and a learning rate of the CNN (210) is 0.0001.

### Claim 9 (depends on 1)

The system of claim 1, wherein the CNN (210) is trained by a data set comprising previous data from the plurality of computing devices (100) comprising the plurality of parameters.

### Claim 10

A method for estimating road roughness under real-world driving conditions through training and implementation of a convolutional neural network (CNN) (210), the method comprising:
a. mounting a computing device (110) to a vehicle (130);
b. measuring, by the computing device (110), while the vehicle (130) is driving on a road, a plurality of parameters comprising:
i. global positioning system (GPS),
ii. driving speed,
iii. vertical acceleration, and
iv. angular velocity of pitch motion;
c. transmitting the plurality of parameters as input to a neural network computing device (200) communicatively coupled to the computing device (110), the neural network computing device (200) comprising a CNN (210) comprising a plurality of convolutional layers;
d. processing, by the CNN (210), the input from the computing device (110) to generate an IRI value of the road as output without calibration of the plurality of computing device (100).

### Claim 11 (depends on 10)

The method of claim 10, wherein the CNN (210) comprises 7 convolutional layers and a plurality of global average pooling layers.

### Claim 12 (depends on 10)

The method of claim 10, wherein the plurality of convolutional layers comprises 7 convolutional layers.

### Claim 13 (depends on 10)

The method of claim 10, wherein each convolutional layer is zero-padded to maintain spatial features, batch normalized to accelerate training speed and prevent overfitting, and has leaky-relu activation for non-linear operations.

### Claim 14 (depends on 10)

The method of claim 10, wherein the computing device (100) is selected from a group comprising a portable computing device, a vehicle-embedded computing device, or a combination thereof, wherein the computing device (100) is mounted to the vehicle (130) by a mounting device (120) selected from a group comprising a vent clip, a vent magnet, a section clip, a suction magnet, and a CDP clip.

### Claim 15 (depends on 10)

The method of claim 10, wherein measuring GPS comprises GPS resolution enhancement by interpolation and grid snapping.

### Claim 16 (depends on 10)

The method of claim 10, wherein the computing device (110) is capable of measuring vertical acceleration and angular velocity at 100 Hz.

### Claim 17 (depends on 10)

The method of claim 10, wherein each parameter of the plurality of parameters is converted into a fixed-size image array before being used as input to the CNN (210).

### Claim 18 (depends on 10)

The method of claim 10, wherein a batch size of the CNN (210) is 64 and a learning rate of the CNN (210) is 0.0001.

### Claim 19 (depends on 10)

The method of claim 10, wherein the CNN (210) is trained by a data set comprising previous data from the plurality of computing devices (100) comprising the plurality of parameters.