System and method for traffic side detection and characterization

• US 9,378,640 B2
• Filed: 06/15/2012
• Issued: 06/28/2016
• Est. Priority Date: 06/17/2011
• Status: Active Grant

• Current Assignee: LeddarTech, Inc.
• Original Assignee: LeddarTech, Inc.
• Inventors: Mimeault, Yvan, Gidel, Samuel, Poulin, Michael, Arrouart, David
• Primary Examiner(s): Tweel, Jr., John A
• Assistant Examiner(s): Aziz, Adnan
• Application Number:
  US14/126,012
• Publication Number:
  US 20140232566A1
• Time in Patent Office: 1,474 Days
• US Class Current: 1/1
• CPC Class Codes: G01S 17/04 Systems determining the presence of a target
• CPC Class Codes: G01S 17/10 using transmission of interrupted, pulse-modulated waves determination of distance by phase measurements G01S17/32
• CPC Class Codes: G01S 17/86 Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• CPC Class Codes: G01S 17/88 Lidar systems specially adapted for specific applications
• CPC Class Codes: G01S 17/894 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g. time-of-flight cameras or flash lidar
• CPC Class Codes: G01S 7/4802 using analysis of echo signal for target characterisation; Target signature; Target cross-section
• CPC Class Codes: G01S 7/4808 Evaluating distance, position or velocity data
• CPC Class Codes: G01S 7/4865 Time delay measurement, e.g. time-of-flight measurement, time of arrival measurement or determining the exact position of a peak peak detection in noise, signal conditioning G01S7/487
• CPC Class Codes: G06V 20/54 of traffic, e.g. cars on the road, trains or boats
• CPC Class Codes: G08G 1/04 using optical or ultrasonic detectors
• CPC Class Codes: G08G 1/048 with provision for compensation of environmental or other condition, e.g. snow, vehicle stopped at detector
• CPC Class Codes: G08G 1/052 with provision for determining speed or overspeed speed measuring in general G01P
• CPC Class Codes: G08G 1/056 with provision for distinguishing direction of travel

Abstract

A method for detecting a vehicle comprising: providing a multi-channel scannerless full-waveform lidar system operating in pulsed Time-Of-Flight operation oriented towards a surface of the roadway to cover the detection zone; providing at least one initialization parameter; emitting pulses at an emission frequency; receiving reflections of the pulses from the detection zone; and acquiring and digitalizing a series of individual complete traces at each channel of system; identifying at least one detection in at least one of the traces; obtaining a height and an intensity for the detection; determining a nature of the detection to be one of an environmental particle detection, a candidate object detection and a roadway surface detection; if the nature of the detection is the candidate object detection, detecting a presence of a vehicle in the detection zone.

Claims

1. A method for detecting a vehicle located in a detection zone of a roadway having at least one lane, said detection zone on said roadway at least partly covering a width of said lane, the method comprising:

providing an optical detection multi-channel scannerless full-waveform lidar system operating in pulsed Time-Of-Flight operation, an optical window of said full-waveform lidar system being oriented towards a surface of said roadway in order for said full-waveform lidar system to cover said detection zone;

providing at least one initialization parameter for said full-waveform lidar system;

using said full-waveform lidar system,

emitting pulses at an emission frequency;

receiving reflections of said pulses from said detection zone; and

acquiring and digitalizing a series of individual complete traces at each optical detection channel of said multi-channel system;

identifying at least one detection in at least one of said individual complete traces;

obtaining a height of said detection and an intensity for said detection using said individual complete trace;

determining a nature of said detection to be one of an environmental particle detection, a candidate object detection and a roadway surface detection using at least one of said individual complete traces, said height of said detection, said intensity and said at least one initialization parameter;

if said nature of said detection is said candidate object detection, detecting a presence of a vehicle in said detection zone;

the method further comprising obtaining a distance of said detection from said full-waveform lidar system using said individual complete trace and said initialization parameter, wherein said determining said nature includes using at least one of said individual complete traces, said height of said detection, said intensity, said distance of said detection from said full-waveform lidar system, and said at least one initialization parameter;

wherein said determining said nature includes;

determining a probability that said nature of said detection is said environment particle if said tracking said evolution determines that said height decreases by more than a height threshold and said distance increases by more than a distance threshold;

if said probability is higher than a probability threshold, determining said nature to be said environmental particle.

2. The method as claimed in claim 1, further comprising tracking an evolution of said detection in a time-spaced individual complete trace, said time-spaced individual complete trace being acquired after said individual complete trace, wherein said determining said nature includes comparing at least one of said height and said intensity in said time-spaced individual complete trace and said individual complete trace.

3. The method as claimed in claim 1, wherein said determining said nature to be said environmental particle includes determining a presence of at least one of fog, water, rain, liquid, dust, dirt, vapor, snow, smoke, gas, smog, pollution, black ice and hail.

4. The method as claimed in claim 1, further comprising identifying a presence of a retroreflector on said vehicle using said individual complete traces and said initialization parameters, by comparing an intensity of said detections with an intensity threshold and identifying detections having an intensity higher than said intensity threshold to be caused by a retroreflector on said vehicle.

5. The method as claimed in claim 4, further comprising tracking an evolution of said detection in a time-spaced individual complete trace, said time-spaced individual complete trace being acquired at a time delay after said individual complete trace, wherein said identifying said presence of said retroreflector is carried out for said individual complete trace and said time-spaced individual complete trace, determining a distance of said retroreflector using said individual complete trace and said time-spaced individual complete trace and estimating a speed of said vehicle based on said initialization parameter, said distance and said time delay.

6. The method as claimed in claim 1, wherein said optical detection multi-channel scannerless full-waveform lidar system includes a light emitting diode (LED) light source adapted to emit said pulses.

7. The method as claimed in claim 1, wherein said digitalizing said series of individual complete traces at each optical detection channel of said optical detection multi-channel system includes digitalizing said series at a high frame rate, said high frame rate being greater than 100 Hz.

8. The method as claimed in claim 1, further comprising providing an image sensing module adapted and positioned to acquire an image covering at least said detection zone;

synchronizing acquisition of said image with said acquiring and digitalizing of said full-waveform lidar system;

acquiring said image with said image sensing module.

9. The method as claimed in claim 8, further comprising recognizing a pattern in said image using said initialization parameter.

10. The method as claimed in claim 9, wherein said pattern is a circle, said pattern in said image corresponding to a wheel of said vehicle.

11. The method as claimed in claim 9, further comprising determining a position of said pattern in said image, taking a second image after an elapsed time delay, recognizing said pattern in said second image and determining a second position of said pattern, determining a displacement of said pattern between said image and said second image.

12. The method as claimed in claim 11, further comprising obtaining a distance for said pattern using said individual complete traces and said initialization parameter, and estimating a speed of said vehicle using said displacement, said distance for said pattern in said image and said pattern in said second image, said elapsed time delay and said initialization parameter.

13. The method as claimed in claim 1, wherein a longitudinal dimension of said detection zone is perpendicular to said roadway.

14. The method as claimed in claim 1, further comprising identifying a section of said vehicle detected to be present in said detection zone using said individual complete trace, said section being one of a front, a side, a top and a rear of said vehicle, said identifying said section including comparing a height of said detection with a height threshold and comparing an intensity of said detection with an intensity threshold.

15. The method as claimed in claim 14, further comprising determining a position of said section of said vehicle in said detection zone using at least one of said individual complete traces and said at least one initialization parameter.

16. The method as claimed in claim 1, further comprising determining a current lane of said roadway in which said vehicle is present using said initialization parameter and said individual complete trace.

17. The method as claimed in claim 1, wherein said obtaining said height and said intensity for said detection using said individual complete trace further comprises converting said detections in Cartesian coordinates.

18. The method as claimed in claim 1, further comprising generating a profile of one of a side and a top of said vehicle using a plurality of said individual complete traces.

19. The method as claimed in claim 5, further comprising determining a length of said vehicle using a plurality of said individual complete traces and said speed of said vehicle, said time delay and said initialization parameter.

20. The method as claimed in claim 1, further comprising providing a second one of said optical detection multi-channel scannerless full-waveform lidar system, an optical window of said second optical detection multi-channel scannerless full-waveform lidar system being oriented towards a surface of said roadway in order for said second system to cover a second detection zone, said second detection zone at least partly overlapping said detection zone, operation of said full-waveform lidar system and said second full-waveform lidar system being synchronized.

21. The method as claimed in claim 1, further comprising providing a second one of said optical detection multi-channel scannerless full-waveform lidar system, an optical window of said second optical detection multi-channel scannerless full-waveform lidar system being oriented towards a surface of said roadway in order for said second system to cover a second detection zone, operation of said full-waveform lidar system and said second full-waveform lidar system being synchronized, wherein said second system is provided at a lateral offset on said roadway with respect to said full-waveform lidar system;

determining a speed of the vehicle using a delay between detection of said vehicle by said full-waveform lidar system and said second full-waveform lidar system and said initialization parameter.

22. The method as claimed in claim 1, further comprising associating a type to said vehicle to classify said vehicle using said height.

23. The method as claimed in claim 19, further comprising associating a type to said vehicle to classify said vehicle using at least one of said height and said length.

24. The method as claimed in claim 10, further comprising associating a type to said vehicle to classify said vehicle using at least one of said height, said length and said pattern.

25. The method as claimed in claim 18, further comprising associating a type to said vehicle to classify said vehicle using at least one of said height, said length, said pattern and said profile.

26. The method as claimed in claim 1, further comprising generating a detection signal upon said detecting said presence.

27. The method as claimed in claim 26, wherein said detection signal controls at least one of a hardware trigger and a software trigger.

28. The method as claimed in claim 26, wherein said detection signal includes information about said detection.

29. The method as claimed in claim 27, further comprising generating a recall signal to invalidate at least one of said hardware trigger and said software trigger.

30. The method as claimed in claim 1, further comprising storing information about said detection.

31. The method as claimed in claim 30, further comprising generating and storing statistical information.

32. The method as claimed in claim 11, further comprising determining a direction of displacement of said vehicle using said displacement and identifying a wrong-way vehicle using said direction of displacement and said initialization parameter.