Camera-based enhancement of vehicle kinematic state estimation
View Patent ↗Methods and systems implemented in a vehicle involve obtaining a single camera image from a camera arranged on the vehicle. The image indicates a heading angle ψ 0 between a vehicle heading x and a tangent line that is tangential to road curvature of a road on which the vehicle is traveling and also indicates a perpendicular distance y 0 from a center of the vehicle to the tangent line. An exemplary method includes obtaining two or more inputs from two or more vehicle sensors, and estimating kinematic states of the vehicle based on applying a Kalman filter to the single camera image and the two or more inputs to solve kinematic equations. The kinematic states include roll angle and pitch angle of the vehicle.
1. A method implemented in a vehicle, comprising:
obtaining, using a processor, a single camera image from a camera arranged on the vehicle, the image indicating a heading angle ψ 0 between a vehicle heading x of the vehicle and a tangent line that is tangential to road curvature of a road on which the vehicle is traveling and also indicating a perpendicular distance y 0 from a center of the vehicle to the tangent line;
obtaining, using the processor, two or more inputs from two or more vehicle sensors of the vehicle;
estimating, using the processor, kinematic states of the vehicle based on applying a Kalman filter to the single camera image and the two or more inputs to solve kinematic equations, wherein the kinematic states include roll angle and pitch angle of the vehicle; and
implementing autonomous or semi-autonomous control of the vehicle based on the kinematic states.
2. The method according to claim 1 , wherein the obtaining the two or more inputs includes obtaining acceleration components a x , a y of the vehicle from an inertial measurement unit (IMU).
3. The method according to claim 2 , wherein the obtaining the two or more inputs includes additionally obtaining roll rate ω x , pitch rate ω y , and yaw rate ω z from the IMU.
4. The method according to claim 3 , wherein the estimating the kinematic states includes estimating a velocity component v y of the vehicle based on the yaw rate ω z .
5. The method according to claim 1 , wherein the obtaining the two or more inputs includes obtaining a velocity component v x of the vehicle from an odometer.
6. The method according to claim 1 , wherein the obtaining the two or more inputs includes obtaining a position X, Y of the vehicle on a global frame from a global navigation satellite system (GNSS).
7. The method according to claim 6 , wherein the estimating the kinematic states includes estimating global heading {circumflex over (ψ)} based on the position X, Y of the vehicle.
8. The method according to claim 1 , further comprising implementing a dynamic model using the kinematic states estimated using the single camera image and the two or more inputs and steering angle of the vehicle.
9. The method according to claim 8 , further comprising obtaining tire forces from the dynamic model and performing a force calculation to determine external disturbances on the vehicle, wherein the external disturbances include crosswinds.
10. The method according to claim 9 , further comprising compensating for the external disturbances.
11. A system in a vehicle, the system comprising:
a camera arranged on the vehicle and configured to obtain a single camera image, the image indicating a heading angle ψ 0 between a vehicle heading x and a tangent line that is tangential to road curvature of a road on which the vehicle is traveling and also indicating a perpendicular distance y 0 from a center of the vehicle to the tangent line; and
a processor configured to obtain the single camera image, to obtain two or more inputs from two or more vehicle sensors of the vehicle, to estimate kinematic states of the vehicle based on applying a Kalman filter to the single camera image and the two or more inputs to solve kinematic equations, wherein the kinematic states include roll angle and pitch angle of the vehicle; and to implement autonomous or semi-autonomous control of the vehicle based on the kinematic states.
12. The system according to claim 11 , wherein the processor is configured to obtain acceleration components a x , a y of the vehicle from an inertial measurement unit (IMU) as the two or more inputs.
13. The system according to claim 12 , wherein the processor is additionally configured to obtain roll rate ω x , pitch rate ω y , and yaw rate ω z from the IMU as additional ones of the two or more inputs.
14. The system according to claim 13 , wherein the processor is configured to estimate the kinematic states by estimating a velocity component v y of the vehicle based on the yaw rate ω z .
15. The system according to claim 11 , wherein the processor is configured to obtain a velocity component v x of the vehicle from an odometer as one of the two or more inputs.
16. The system according to claim 11 , wherein the processor is configured to obtain a position X, Y of the vehicle on a global frame from a global navigation satellite system (GNSS) as the two or more inputs.
17. The system according to claim 16 , wherein the processor is configured to estimate the kinematic states by estimating global heading {circumflex over (ψ)} based on the position X, Y of the vehicle.
18. The system according to claim 11 , wherein the processor is additionally configured to implement a dynamic model using the kinematic states estimated using the single camera image and the two or more inputs and steering angle of the vehicle.
19. The system according to claim 18 , wherein the processor is additionally configured to obtain tire forces from the dynamic model and perform a force calculation to determine external disturbances on the vehicle, wherein the external disturbances include crosswinds.
20. The system according to claim 19 , wherein the processor is further configured to compensate for the external disturbances.