Prediction of road grade for autonomous vehicle navigation

Patent metadata

Field	Value
Publication number	US-12534087-B2
Application number	US-202318222384-A
Country	US
Kind code	B2
Filing date	Jul 14, 2023
Priority date	Jul 14, 2023
Publication date	Jan 27, 2026
Grant date	Jan 27, 2026

Abstract

Systems and methods of predicting a grade of a road upon which a vehicle is traveling are disclosed. An autonomous vehicle system can receive sensor data from a sensor measuring a response from at least one mechanical component of the autonomous vehicle as the autonomous vehicle navigates a road; detect a speed of the autonomous vehicle; determine a predicted grade of the road based on the sensor data and the speed; and navigate the autonomous vehicle based on the predicted grade of the road.

First claim

What is claimed is:

1 . A system, comprising:
one or more processors of an autonomous vehicle, the one or more processors configured to:
receive sensor data from a sensor measuring a response from at least one mechanical component of the autonomous vehicle as the autonomous vehicle navigates a road, the response including a power output of an engine of the autonomous vehicle;
determine a speed of the autonomous vehicle;
determine a predicted grade of the road based on the power output and the speed; and
navigate the autonomous vehicle based on the predicted grade of the road,
wherein the autonomous vehicle includes a light detection and ranging (LiDAR) sensor, and the one or more processors are further configured to:
determine a first predicted grade of the road based on the power output and the speed;
determine a second predicted grade of the road based on LiDAR points captured by the LiDAR sensor;
compare the first predicted grade with the second predicted grade; and
generate a confidence value of grade prediction based on the comparison.

2 . The system of claim 1 , wherein the sensor comprises a rotational speed sensor, a torque sensor, or a throttle position sensor.

3 . The system of claim 1 , wherein the one or more processors are further configured to determine the predicted grade of the road further based on a weight of the autonomous vehicle.

4 . The system of claim 3 , wherein the one or more processors are further configured to receive the weight of the autonomous vehicle from an external computing device.

5 . The system of claim 1 , wherein the one or more processors are further configured to transmit the predicted grade of the road to one or more remote servers.

6 . The system of claim 1 , wherein the one or more processors are further configured to execute an object detection model to detect an object on the road based on the predicted grade of the road.

7 . The system of claim 1 , wherein the one or more processors are further configured to update map data stored in memory of the autonomous vehicle based on the grade of the road.

8 . A method, comprising:
receiving, by one or more processors of an autonomous vehicle, sensor data from a sensor measuring a response from at least one mechanical component of the autonomous vehicle as the autonomous vehicle navigates a road, the response including a power output of an engine of the autonomous vehicle; determine, by the one or more processors, a speed of the autonomous vehicle; determining, by the one or more processors, a predicted grade of the road based on the power output and the speed; and navigating, by the one or more processors, the autonomous vehicle based on the predicted grade of the road, wherein the autonomous vehicle includes a light detection and ranging (LiDAR) sensor, determining the predicted grade further comprising:
determining a first predicted grade of the road based on the power output and the speed;
determining a second predicted grade of the road based on the LiDAR points;
comparing the first predicted grade with the second predicted grade; and
generating a confidence value of grade prediction based on the comparison.

9 . The method of claim 8 , wherein the sensor comprises a rotational speed sensor, a torque sensor, or a throttle position sensor.

10 . The method of claim 8 , further comprising determining, by the one or more processors, the predicted grade of the road further based on a weight of the autonomous vehicle.

11 . The method of claim 10 , further comprising receiving, by the one or more processors, the weight of the autonomous vehicle from an external computing device.

12 . The method of claim 8 , further comprising transmitting, by the one or more processors, the predicted grade of the road to one or more remote servers.

13 . The method of claim 8 , further comprising executing, by the one or more processors, an object detection model to detect an object on the road based on the predicted grade of the road.

14 . The method of claim 8 , further comprising updating, by the one or more processors, map data stored in memory of the autonomous vehicle based on the grade of the road.

15 . The system of claim 1 , wherein the one or more processors are further configured to determine, via an artificial intelligence model, the predicted grade of the road, wherein the artificial intelligence model is trained with historical speed and power data.

16 . The method of claim 8 , wherein determining the predicted grade further comprises:
determining, via an artificial intelligence model, the predicted grade of the road, wherein the artificial intelligence model is trained with historical speed and power data.

Assignees

• Torc Robotics Inc

Inventors

• Pullagurla Harish
• Miller Zachary
• Cunningham Andrew

Classifications

• B60W2556/45

  External transmission of data to or from the vehicle · CPC title

• B60W2420/408

  Radar; Laser, e.g. lidar · CPC title

• B60W60/001

  Planning or execution of driving tasks · CPC title

• B60W40/076 Primary

  Slope angle of the road · CPC title

• B60W2530/10

  Weight · CPC title