Adaptive Active Suspension System With Road Preview

• US 20140195112A1
• Filed: 01/08/2013
• Published: 07/10/2014
• Est. Priority Date: 01/08/2013
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Lu, Jianbo, Filev, Dimitar P., Hrovat, Davor, Hoffmann, Uwe, Tseng, Eruc Hongtei, Baales, Simon, Seemann, Micheal
• Granted Patent: US 8,788,146 B1
• Time in Patent Office: Days
• US Class Current: 701/37
• CPC Class Codes: B60G 17/015 the regulating means comprising electric or electronic elements B60G17/002, B60G17/005 take precedence
• CPC Class Codes: B60G 17/016 characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input B60G17/017 takes precedence
• CPC Class Codes: B60G 17/0165 to an external condition, e.g. rough road surface, side wind
• CPC Class Codes: B60G 17/018 characterised by the use of a specific signal treatment or control method
• CPC Class Codes: B60G 17/0195 characterised by the regulation being combined with other vehicle control systems conjoint control of vehicle sub-units including control of suspension systems B60W10/22
• CPC Class Codes: B60G 17/02 Spring characteristics , e.g. mechanical springs and mechanical adjusting meansB60G17/005, B60G17/015 take precedence
• CPC Class Codes: B60G 17/06 Characteristics of dampers , e.g. mechanical dampersB60G17/015 takes precedence
• CPC Class Codes: B60G 2400/0521 Roll rate
• CPC Class Codes: B60G 2400/0522 Pitch rate
• CPC Class Codes: B60G 2400/0523 Yaw rate
• CPC Class Codes: B60G 2400/102 vertical
• CPC Class Codes: B60G 2400/104 lateral or transversal with regard to vehicle
• CPC Class Codes: B60G 2400/106 longitudinal with regard to vehicle, e.g. braking
• CPC Class Codes: B60G 2400/20 Speed
• CPC Class Codes: B60G 2400/25 Stroke; Height; Displacement
• CPC Class Codes: B60G 2400/52 in tyre
• CPC Class Codes: B60G 2400/82 Ground surface
• CPC Class Codes: B60G 2400/96 Presence, absence or inactivity of driver
• CPC Class Codes: B60G 2401/14 Photo or light sensitive means, e.g. Infrared
• CPC Class Codes: B60G 2401/16 GPS track data
• CPC Class Codes: B60G 2401/21 : Laser
• CPC Class Codes: B60G 2500/10 : Damping action or damper
• CPC Class Codes: B60G 2500/20 : Spring action or springs
• CPC Class Codes: B60G 2500/30 : Height or ground clearance
• CPC Class Codes: G01S 17/88 : Lidar systems specially adapted for specific applications
• CPC Class Codes: G01S 17/931 : of land vehicles

Abstract

A method for controlling an active suspension includes steps of determining a dimension of a road abnormality ahead of the vehicle and comparing the dimension with a vehicle dimension. Responsive to the comparison, the abnormality is classified as one type of a plurality of predetermined types. Responsive to a height dimension of the abnormality, the abnormality is further classified as having one of a small, medium, or large severity. The suspension is controlled responsive to the type and severity.

Claims

1. A method for generating a representation of a road surface ahead of a moving vehicle, comprising steps of:

a) acquiring road surface data out to a distance d2 ahead of the vehicle at a time n−

N and vehicle data time-correlated with the surface data;

b) generating, using the data, a plurality of cloud vectors representing the road surface; and

c) generating, using the cloud vectors, a temporal occupancy grid containing a representation of the road surface out to the distance d2 at time n−

N.

2. The method of claim 1 further comprising the steps of:

d) acquiring road surface data out to the distance d2 ahead of the vehicle at a time (n-N+1) and vehicle data time-correlated with the road surface data;

e) updating, using the newly-acquired data, the plurality of cloud vectors representing the road surface up to the distance d2 ahead of the vehicle;

f) updating the temporal occupancy grid using the updated cloud vectors, so that the grid contains a representation of the abnormal road surface out to the distance d2 at time (n−

N+1)

g) incrementing the time by one time period;

h) repeating steps d) through g) until the time is equal to n.

3. The method of claim 2 further comprising the step of identifying cloud vectors of the plurality of cloud vectors usable for representing a reference portion of the road surface ahead of the vehicle.

4. The method of claim 3 further comprising the step of, using the acquired road surface data, calculating a probability density function for the road surface represented in the temporal occupancy grid.

5. The method of claim 4 further comprising the step of, using the probability density function and the acquired road surface data, calculating an estimated road surface height at a location of each cloud vector of the plurality of cloud vectors.

13. The method of claim 5 further comprising the step of acquiring data vehicle suspension response data resulting from an interaction between a vehicle suspension and a portion of the road surface represented by a corresponding portion of the plurality of cloud vectors.

14. The method of claim 13 further comprising the step of initializing a set of correction factors for application to selected ones of the road surface heights calculated at the locations corresponding to the plurality of cloud vectors.

15. The method of claim 14 further comprising the step of calculating, using values of the correction factors and the selected ones of the road surface heights calculated at the locations of the plurality of cloud vectors, estimated heights h_wl′

and h_wr′

of the road surface at left and right front wheels of the vehicle.

16. The method of claim 13 further comprising the step of calculating, using the vehicle suspension response data resulting from the interaction between a vehicle suspension and the portion of the road surface, a road profile vector representing a vertical deviation of the portion of the road surface from the reference portion of the road surface.

17. The method of claim 16 further comprising the step of transmitting, to another vehicle, information relating to the portion of the road surface.

18. The method of claim 16 further comprising the step of transmitting, to a vehicle-related infrastructure, information relating to the portion of the road surface.

19. The method of claim 16 further comprising the step of transmitting, to a cloud server, information relating to the portion of the road surface.

6. A method for controlling an active suspension, comprising steps of:

determining a dimension of a road abnormality ahead of the vehicle;

comparing the dimension with a vehicle dimension;

responsive to the comparison, classifying the abnormality as one type of a plurality of predetermined types;

responsive to a dimension of the abnormality, further classifying the abnormality as having one of a small, medium, and large severity; and

controlling the suspension responsive to the type and severity.

7. The method of claim 6 wherein the step of controlling the suspension comprises controlling the suspension by implementing a Diagonal Rolling Control scheme.

8. The method of claim 6 wherein the step of controlling the suspension comprises controlling the suspension by implementing a Three Wheel Support control scheme.

9. The method of claim 6 wherein the step of controlling the suspension comprises controlling the suspension by implementing a Zigzag Maneuver Control (ZMC) scheme.

10. The method of claim 6 wherein the step of controlling the suspension comprises controlling the suspension by implementing an Alternating Damping Control scheme.

11. The method of claim 6 wherein the step of controlling the suspension comprises controlling the suspension by implementing an Alternating Stiffness Control scheme.

12. The method of claim 6 wherein the step of controlling the suspension comprises controlling the suspension by implementing a Jumping Control scheme.

20. A method for controlling an active suspension system, comprising the steps of:

a) by a computing means operatively coupled to the suspension system;

initializing at least one correction factor for use in calculating an estimated height of a location on the road surface ahead of the vehicle;

b) by at least one road condition sensor operatively coupled to the suspension system, previewing a portion of a road surface ahead of a moving vehicle to acquire road surface condition data;

c) by at least one vehicle parameter sensor operatively coupled to the suspension system, acquiring vehicle parameter data that is time-correlated with the road surface data;

d) by a computing means operatively coupled to the suspension system;

generating, using the road surface data, the vehicle parameter data, and a current value of the at least one correction factor, an estimated height of a location on the road surface ahead of the vehicle; and

e) by a controller operatively coupled to the suspension system, controlling the active suspension system responsive to the estimated height of the road surface location, when the vehicle is moving on the road surface.

21. The method of claim 20 wherein the step of generating the estimated height of the location on the road surface comprises the step of integrating the road surface data with the vehicle parameter data to generate a plurality of cloud vectors representing an associated plurality of points on the road surface at the road surface location.

22. The method of claim 21 wherein the step of generating the estimated height of the location on the road surface further comprises the step of generating, using cloud vectors of the plurality of cloud vectors, a temporal occupancy grid containing a representation of the road surface location.

23. The method of claim 22 wherein the step of generating the estimated height of the location on the road surface further comprises the step of calculating an estimated road surface height at each point on the road surface within the temporal occupancy grid.

24. The method of claim 23 further comprising the step of, by a computing means operatively coupled to the suspension system, identifying cloud vectors from the plurality of cloud vectors usable for a reference, normal road surface representation.

25. The method of claim 24 wherein the step of calculating an estimated road surface height at each point within the temporal occupancy grid comprises the step of calculating a probability density function for the road surface location represented in the temporal occupancy grid.

26. The method of claim 20 further comprising the steps of:

by at least one vehicle parameter sensor operatively coupled to the suspension system, acquiring data from resulting contact between the road surface location and front wheels of the vehicle operatively coupled to the suspension system;

by a computing means operatively coupled to the suspension system;

calculating, using the contact data, an estimated road surface height at the road surface location; and

updating the at least one correction factor; and

repeating steps b)-e).