Driver assist

• US 10,551,836 B2
• Filed: 06/06/2018
• Issued: 02/04/2020
• Est. Priority Date: 06/06/2018
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Nath, Nitendra, Danko, Ryan, Mills, Aaron L.
• Primary Examiner(s): Rink, Ryan
• Application Number:
  US16/001,109
• Publication Number:
  US 20190377339A1
• Time in Patent Office: 608 Days
• CPC Class Codes: B60W 10/04 including control of propulsion units
• CPC Class Codes: B60W 10/18 including control of braking systems
• CPC Class Codes: B60W 10/20 including control of steering systems
• CPC Class Codes: B60W 2510/30 Auxiliary equipments
• CPC Class Codes: B60W 2520/06 Direction of travel
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2520/125 Lateral acceleration
• CPC Class Codes: B60W 2520/18 Roll
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 30/12 Lane keeping
• CPC Class Codes: B60W 30/182 Selecting between different operative modes, e.g. comfort and performance modes
• CPC Class Codes: B60W 40/02 related to ambient conditions
• CPC Class Codes: B60W 40/105 Speed
• CPC Class Codes: B60W 40/109 Lateral acceleration
• CPC Class Codes: B60W 40/112 Roll movement
• CPC Class Codes: G05D 1/0061 for transition from automatic pilot to manual pilot and vice versa
• CPC Class Codes: G05D 1/0219 ensuring the processing of the whole working surface

Abstract

A computing system can determine a probability of precipitation based on Bayesian inference conditioned on probabilities associated with vehicle wiper status, vehicle jerk, vehicle sway, and vehicle lateral offset. The computing system can disable a vehicle lane assist system based on the probability of precipitation and operate a vehicle with the disabled vehicle lane assist system.

Claims

1. A method, comprising:

determining a probability of precipitation based on Bayesian inference conditioned on probabilities associated with vehicle wiper status, vehicle jerk, vehicle sway, and vehicle lateral offset; and

disabling a vehicle lane assist system based on the probability of precipitation.

2. The method of claim 1, further comprising determining vehicle wiper status, vehicle jerk, vehicle sway, and vehicle lateral offset based on vehicle sensor data.

3. The method of claim 2, wherein vehicle sensor data includes path offset coefficients, path polynomial coefficients, vehicle speed, vehicle lateral acceleration, and path polynomial confidence.

4. The method of claim 3, wherein vehicle wiper status includes wiper off, wiper interval, wiper low and wiper high.

5. The method of claim 3, wherein vehicle lateral jerk is determined based on a constant velocity model-based Kalman Filter applied to vehicle lateral acceleration.

6. The method of claim 3, wherein vehicle sway is determined based on harmonic analysis of vehicle lateral jerk.

7. The method of claim 3, wherein vehicle lateral offset is based on determining a polynomial function based on path polynomial coefficients and vehicle speed.

8. The method of claim 3, further comprising re-enabling the vehicle lane assist system based on an empirically determined time period, the probability of precipitation, and the path polynomial confidence.

9. The method of claim 8, further comprising determining path polynomial confidence by determining a number of times the path polynomial is below an empirically determined threshold in an empirically determined time period.

10. A system, comprising a processor;

and

a memory, the memory including instructions to be executed by the processor to;

determine a probability of precipitation based on Bayesian inference conditioned on probabilities associated with vehicle wiper status, vehicle jerk, vehicle sway, and vehicle lateral offset; and

disabling a vehicle lane assist system based on the probability of precipitation.

11. The system of claim 10, further comprising determining vehicle wiper status, vehicle jerk, vehicle sway, and vehicle lateral offset based on vehicle sensor data.

12. The system of claim 11, wherein vehicle sensor data includes path offset coefficients, path polynomial coefficients, vehicle speed, vehicle lateral acceleration, and path polynomial confidence.

13. The system of claim 12, wherein vehicle wiper status includes wiper off, wiper interval, wiper low and wiper high.

14. The system of claim 12, wherein vehicle lateral jerk is determined based on a constant velocity model-based Kalman Filter applied to vehicle lateral acceleration.

15. The system of claim 12, wherein vehicle sway is determined based on harmonic analysis of vehicle lateral jerk.

16. The system of claim 12, wherein vehicle bad lateral offset is based on determining a polynomial function based on path polynomial coefficients and vehicle speed.

17. The system of claim 12, further comprising re-enabling the vehicle lane assist system based on an empirically determined time period, the probability of precipitation, and the path polynomial confidence.

18. The system of claim 17, further comprising determining path polynomial confidence by determining a number of times the path polynomial is below an empirically determined threshold in an empirically determined time period.

19. A system, comprising:

means for controlling vehicle steering, braking and powertrain;

computer means for;

determining a probability of precipitation based on Bayesian inference conditioned on probabilities associated with vehicle wiper status, vehicle jerk, vehicle sway, and vehicle lateral offset; and

disabling a vehicle lane assist system based on the probability of precipitation; and

operating a vehicle with the disabled vehicle lane assist system based on the means for controlling vehicle steering, braking and powertrain.

20. The system of claim 19, further comprising determining vehicle wiper status, vehicle jerk, vehicle sway, and vehicle lateral offset based on vehicle sensor data.