Predictive collision sensing system

• US 6,085,151 A
• Filed: 01/20/1998
• Issued: 07/04/2000
• Est. Priority Date: 01/20/1998
• Status: Expired due to Term

• Current Assignee: Joyson Safety Systems Acquisition, LLC
• Original Assignee: Automotive Systems Laboratory, Incorporated (Ningbo Junsheng Investment Group Co., Ltd.)
• Inventors: Farmer, Michael E., Bruce, Michael P.
• Primary Examiner(s): NGUYEN, TAN QUANG
• Application Number:
  US09/009,035
• Time in Patent Office: 896 Days
• Field of Search: 701/300, 701/301, 701/96, 340/903, 340/943, 340/435, 340/436, 280/734, 280/735, 356/3, 367/87, 367/90, 367/99, 367/103, 342/118, 342/70, 342/71, 342/72
• US Class Current: 701/301
• CPC Class Codes: B60R 21/0134 responsive to imminent contact with an obstacle , e.g. using radar systems
• CPC Class Codes: B60T 2201/022 Collision avoidance systems
• CPC Class Codes: G01S 13/34 using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal
• CPC Class Codes: G01S 13/726 Multiple target tracking
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 17/931 of land vehicles
• CPC Class Codes: G01S 2013/9318 Controlling the steering
• CPC Class Codes: G01S 2013/93185 Controlling the brakes
• CPC Class Codes: G01S 2013/93271 in the front of the vehicles
• CPC Class Codes: G01S 2013/93273 on the top of the vehicles
• CPC Class Codes: G01S 2013/93275 in the bumper area
• CPC Class Codes: G01S 2013/93276 in the windshield area
• CPC Class Codes: G01S 7/023 Interference mitigation, e.g. reducing or avoiding non-intentional interference with other HF-transmitters, base station transmitters for mobile communication or other radar systems, e.g. using electro-magnetic interference [EMI] reduction techniques auxiliary means for detecting or identifying radar signals or the like G01S7/021; means for anti-jamming G01S7/36
• CPC Class Codes: G01S 7/0232 Avoidance by frequency multiplex
• CPC Class Codes: G01S 7/0234 Avoidance by code multiplex
• CPC Class Codes: G01S 7/493 Extracting wanted echo signals

Abstract

A relatively narrow beam of either RF or optical electromagnetic radiation is scanned over a relatively wide azimuthal range. The return signal is processed to detect the range and velocity of each point of reflection. Individual targets are identified by clustering analysis and are tracked in a Cartesian coordinate system using a Kalman filter. The threat to the vehicle for a given target is assessed from estimates of the relative distance, velocity, and size of each target, and one or more vehicular devices are controlled responsive to the assessment of threat so as to enhance the safety of the vehicle occupant. In a preferred embodiment, a quantized linear frequency modulated continuous wave RF signal is transmitted from and received by a multi-beam antenna having an aziumthal range of at least +/-100 degrees and an individual beam width of approximately 10 degrees.

Claims

1. A predictive collision sensing system for a vehicle, comprising:

a. a radar transmitter/receiver module for illuminating a zone proximate the vehicle with a beam of electromagnetic energy and for receiving portions of said beam of electromagnetic energy reflected by one or more objects illuminated by said electromagnetic energy thereby providing an estimate of the distance to said target relative to the vehicle;

b. a signal processor operatively coupled to said radar transmitter/receiver module;

c. a means under control of said signal processor for scanning said beam of electromagnetic energy over an azimuth range in accordance with an azimuth coverage pattern, wherein said azimuth coverage pattern is continuous;

d. a target tracking algorithm implemented by said signal processor for tracking said one or more objects relative to the vehicle;

e. a clustering algorithm implemented by said signal processor for grouping separate tracks of said one or more objects;

f. a target size estimating algorithm implemented by said signal processor for estimating the azimuthal extent of said one or more objects tracked by said target tracking algorithm; and

g. a threat assessment algorithm implemented by said signal processor for assessing the threat of said one or more objects to the vehicle, whereby said signal processor controls one or more vehicular devices responsive to said assessment of threat, so as to enhance the safety of the occupant of the vehicle.

2. A predictive collision sensing system for a vehicle as recited in claim 1, wherein said radar transmitter/receiver module further provides an estimate of the velocity relative to the vehicle for each of said one or more objects.

3. A predictive collision sensing system for a vehicle as recited in claim 1, wherein said radar transmitter/receiver module incorporates an antenna having a plurality of distinct beam positions.

4. A predictive collision sensing system for a vehicle as recited in claim 3, wherein said radar transmitter/receiver module incorporates a multi-beam antenna.

5. A predictive collision sensing system for a vehicle as recited in claim 1, wherein the range of azimuth angles associated with said azimuth coverage pattern is at least +/-100 degrees relative to the front of the vehicle.

6. A predictive collision sensing system for a vehicle as recited in claim 1, wherein the size of said beam of electromagnetic energy is approximately 10 degrees.

7. A predictive collision sensing system for a vehicle as recited in claim 1, wherein said beam of electromagnetic energy is sequentially scanned over said azimuth coverage pattern if said target tracking algorithm is not tracking an object, and said beam of electromagnetic energy is adaptively scanned responsive to the path of said one or more objects when target tracking algorithm is tracking said one or more objects.

8. A predictive collision sensing system for a vehicle as recited in claim 1, wherein the trajectory of said one or more objects is tracked in Cartesian coordinates relative to the vehicle.

9. A predictive collision sensing system for a vehicle as recited in claim 1, wherein said target tracking algorithm incorporates a Kalman filter.

10. A predictive collision sensing system for a vehicle as recited in claim 9, wherein said Kalman filter is augmented with a state to track the target size.

11. A predictive collision sensing system for a vehicle as recited in claim 1, wherein said one or more vehicular devices are selected from the group consisting of one or more warning devices, the vehicle braking system, the vehicle steering system, and one or more occupant restraint devices.

12. A predictive collision sensing system for a vehicle as recited in claim 11, wherein said radar transmitter/receiver module operates in the pulsed mode.

13. A predictive collision sensing system for a vehicle as recited in claim 12, wherein said linear frequency modulated continuous wave mode incorporates a quantized waveform.

14. A predictive collision sensing system for a vehicle as recited in claim 11, wherein said radar transmitter/receiver module operates in the linear frequency modulated continuous wave mode.

15. A predictive collision sensing system for a vehicle as recited in claim 1, wherein said electromagnetic energy comprises energy selected from group consisting of RF energy, microwave energy, and millimeter wave energy.

16. A predictive collision sensing system for a vehicle as recited in claim 1, wherein said electromagnetic energy comprises optical energy.

17. A predictive collision sensing system for a vehicle as recited in claim 1, wherein said clustering algorithm is responsive to range, cross-range, and speed in accordance with a normalized distance function.

18. A method of predicting a crash for a vehicle and for controlling one or more vehicular devices responsive thereto, comprising:

a. measuring the range of one or more objects relative to the vehicle using an azimuthally scanned radar beam;

b. calculating the trajectory of said one or more objects from said range measurements;

c. clustering separate tracks of said one or more objects;

d. measuring the azimuthal extent of said one or more objects from said range measurements and from the azimuthal scan angle of said radar beam;

e. assessing the threat of said one or more objects to the vehicle from a combination of said trajectory and said azimuthal extent of said one or more objects; and

f. controlling one or more vehicular devices responsive to said assessment of threat, so as to enhance the safety of the occupant of the vehicle.

19. A method of predicting a crash for a vehicle and for controlling one or more vehicular devices responsive thereto as recited in claim 18, further comprising the operation of radar sensing the velocity of one or more objects relative to the vehicle using a scanned radar beam.

20. A method of predicting a crash for a vehicle and for controlling one or more vehicular devices responsive thereto as recited in claim 18, wherein the scanning of said scanned radar beam is responsive to said threat assessment of said one or more objects to the vehicle.

21. A method of predicting a crash for a vehicle and for controlling one or more vehicular devices responsive thereto as recited in claim 18, wherein said trajectory of said one or more objects is calculated in Cartesian coordinates by Kalman filtering.

22. A method of predicting a crash for a vehicle and for controlling one or more vehicular devices responsive thereto as recited in claim 18, wherein said aziumuthal extent of said one or more objects is estimated by clustering analysis.

23. A method of predicting a crash for a vehicle and for controlling one or more vehicular devices responsive thereto as recited in claim 18, wherein said threat assessment incorporates an estimate of crash severity.

24. A method of predicting a crash for a vehicle and for controlling one or more vehicular devices responsive thereto as recited in claim 18, said threat assessment incorporates an estimate of crash time.

25. A method of predicting a crash for a vehicle and for controlling one or more vehicular devices responsive thereto as recited in claim 18, said threat assessment incorporates an estimate of crash direction.

26. A method of predicting a crash for a vehicle and for controlling one or more vehicular devices responsive thereto as recited in claim 18, said threat assessment incorporates an estimate of crash probability.

27. A method of predicting a crash for a vehicle and for controlling one or more vehicular devices responsive thereto as recited in claim 18, wherein said one or more vehicular devices are selected from the group consisting of one or more warning devices, the vehicle braking system, the vehicle steering system, and one or more occupant restraint devices.

28. A method of predicting a crash for a vehicle and for controlling one or more vehicular devices responsive thereto as recited in claim 18, wherein the clustering of said separate tracks of said one or more objects comprises the step of calculating a normalized distance function that is responsive to range, range variance, cross-range, cross-range variance, speed, and speed variance.

29. A method of predicting a crash for a vehicle and for controlling one or more vehicular devices responsive thereto as recited in claim 18, wherein said Kalman filter is augmented with a state to track target size.

30. A method of predicting a crash for a vehicle and for controlling one or more vehicular devices responsive thereto, comprising:

a. measuring the range of one or more objects relative to the vehicle using an azimuthally scanned radar beam;

b. measuring the velocity of one or more objects relative to the vehicle using said azimuthally scanned radar beam, wherein the operations of measuring the range and velocity of one or more objects relative to the vehicle using said azimuthally scanned radar beam comprises;

i. generating a continuous wave RF signal comprising a repetitive sequence of uniformly spaced frequencies so as to form a transmitted signal;

ii. illuminating the object with said continuous wave RF signal;

iii. receiving the component of said RF signal reflected by said one or more objects so as to form a received signal;

iv. mixing said received signal with said transmitted signal so as to form a complex amplitude frequency difference signal from the difference in frequency therebetween;

v. comparing the real part of said complex frequency difference signal with a plurality of monotonically increasing threshold values, wherein adjacent threshold values corresponds to the bounds of an associated range cell,

vi. storing the complex amplitudes of said received signal for each said associated range cell so as to form an associated list of complex amplitudes;

vii. calculating the Doppler shift for each said associated range cell from a spectral analysis of said associated list of complex amplitudes; and

viii. collecting said Doppler shift calculations for all said associated range cells so as to form a range-Doppler map for said one or more objects, thereby providing the distance and velocity of said one or more objects relative to the vehicle;

c. calculating the trajectory of said one or more objects from said range measurements;

d. clustering separate tracks of said one or more objects;

e. measuring the azimuthal extent of said one or more objects from said range measurements and from the azimuthal scan angle of said radar beam;

f. assessing the threat of said one or more objects to the vehicle from a combination of said trajectory and said azimuthal extent of said one or more objects; and

g. controlling one or more vehicular devices responsive to said assessment of threat, so as to enhance the safety of the occupant of the vehicle.