# US RE50,261 E1 · System and method for multipurpose traffic detection and characterization

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- Number: US RE50,261 E1
- Document ID: USRE50261E1
- Status: Granted
- Kind: E1
- Application number: 17/412,092
- Filed: 2021-08-25
- Granted: 2025-01-07
- Earliest claimed: 2012-03-02
- Assignees: LeddarTech Inc.
- Applicants: LeddarTech Inc.
- Inventors: Yvan Mimeault; Samuel Gidel
- Web page: https://patentgrep.com/patent/USRE50261E1

## CPC

- G01S 17/58: Velocity or trajectory determination systems; Sense-of-movement determination systems
- G01S 7/4802: {using analysis of echo signal for target characterisation; Target signature; Target cross-section}
- G01S 7/4808: {Evaluating distance, position or velocity data}
- G01S 7/484: Transmitters
- G01S 17/66: Tracking systems using electromagnetic waves other than radio waves
- G01S 17/86: Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
- G01S 17/88: Lidar systems specially adapted for specific applications
- G01S 17/89: for mapping or imaging
- G06T 7/277: involving stochastic approaches, e.g. using Kalman filters
- G06V 10/255: {Detecting or recognising potential candidate objects based on visual cues, e.g. shapes}
- G06V 20/54: of traffic, e.g. cars on the road, trains or boats
- G08G 1/0125: {Traffic data processing}
- G08G 1/015: with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
- G08G 1/0175: {by photographing vehicles, e.g. when violating traffic rules}
- G08G 1/04: using optical or ultrasonic detectors
- G08G 1/054: photographing overspeeding vehicles
- G06T 2200/04: involving 3D image data
- G06T 2207/10028: Range image; Depth image; 3D point clouds
- G06T 2207/30236: Traffic on road, railway or crossing
- G06V 2201/08: Detecting or categorising vehicles
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93})

## Abstract

A method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment, comprising: providing a 3D optical emitter; providing a 3D optical receiver with a wide and deep field of view; driving the 3D optical emitter into emitting short light pulses; receiving a reflection/backscatter of the emitted light, thereby acquiring an individual digital full-waveform LIDAR trace for each detection channel of the 3D optical receiver; using the individual digital full-waveform LIDAR trace and the emitted light waveform, detecting a presence of a plurality of vehicles, a position of at least part of each vehicle and a time at which the position is detected; assigning a unique identifier to each vehicle; repeating the steps of driving, receiving, acquiring and detecting, at a predetermined frequency; tracking and recording an updated position of each vehicle and an updated time at which the updated position is detected.

## Claims

### Claim 1

A method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment, the method comprising:
providing a 3D optical emitter at an installation height oriented to allow illumination of a 3D detection zone in said environment;
providing a 3D optical receiver oriented to have a wide and deep field of view within said 3D detection zone, said 3D optical receiver having a plurality of detection channels in said field of view;
driving the 3D optical emitter into emitting short light pulses toward the detection zone, said light pulses having an emitted light waveform;
receiving a reflection/backscatter of the emitted light on the vehicles in the 3D detection zone at said 3D optical receiver, thereby acquiring an individual digital full-waveform LIDAR trace for each detection channel of said 3D optical receiver;
using said individual digital full-waveform LIDAR trace and said emitted light waveform, detecting a presence of a plurality of vehicles in said 3D detection zone, a position of at least part of each said vehicle in said 3D detection zone and a time at which said position is detected;
assigning a unique identifier to each vehicle of said plurality of vehicles detected;
repeating said steps of driving, receiving, acquiring and detecting, at a predetermined frequency;
at each instance of said repeating step, tracking and recording an updated position of each vehicle of said plurality of vehicles detected and an updated time at which said updated position is detected, with said unique identifier;
wherein said detecting said presence includes:
extracting observations in the individual digital full-waveform LIDAR trace;
using the location for the observations to remove observations coming from a surrounding environment;
extracting lines using an estimate line and a covariance matrix using polar coordinates;
removing observations located on lines parallel to the x axis.

### Claim 2 (depends on 1)

The method as claimed in claim 1, wherein said traffic control environment is at least one of a traffic management environment and a traffic enforcement environment.

### Claim 3 (depends on 1)

The method as claimed in claim 1, wherein said detecting said presence includes
extracting observations in the individual digital full-waveform LIDAR trace and intensity data for the observations;
finding at least one blob in the observations;
computing an observation weight depending on the intensity of the observations in the blob;
computing a blob gravity center based on the weight and a position of the observations in the blob.

### Claim 4 (depends on 1)

The method as claimed in claim 1, further comprising setting at least one trigger line location and recording trigger line trespassing data with the unique identifier.

### Claim 5 (depends on 4)

The method as claimed in claim 4, further comprising setting said trigger line location relative to a visible landmark in said environment.

### Claim 6 (depends on 1)

The method as claimed in claim 1, wherein said detecting said time at which said position is detected includes assigning a timestamp for said detecting said presence and wherein said timestamp is adapted to be synchronized with an external controller.

### Claim 7 (depends on 1)

The method as claimed in claim 1, further comprising obtaining a classification for each detected vehicles using a plurality of detections in the 3D detection zone caused by the same vehicle.

### Claim 8 (depends on 1)

The method as claimed in claim 1, wherein said detecting said presence further comprises detecting a presence of a pedestrian in said environment.

### Claim 9 (depends on 1)

The method as claimed in claim 1, wherein said part of said vehicle is one of a front, a side and a rear of the vehicle.

### Claim 10 (depends on 1)

The method as claimed in claim 1, wherein emitting short light pulses includes emitting short light pulses of a duration of less than 50 ns.

### Claim 11 (depends on 1)

The method as claimed in claim 1, wherein said 3D optical emitter is at least one of an infrared LED source, a visible-light LED source and a laser.

### Claim 12 (depends on 1)

The method as claimed in claim 1, wherein said providing said 3D optical receiver to have a wide and deep field of view includes providing said 3D optical receiver to have a horizontal field of view angle of at least 20° and a vertical field of view angle of at least 4°.

### Claim 13 (depends on 1)

The method as claimed in claim 1, further comprising determining and recording a speed for each said vehicle using said position and said updated position of one of said instances of said repeating step and an elapsed time between said time of said position and said updated time of said updated position, with said unique identifier.

### Claim 14 (depends on 13)

The method as claimed in claim 13, further comprising using a Kalman filter to determine an accuracy for said speed to validate said speed; comparing said accuracy to a predetermined accuracy threshold; if said accuracy is lower than said predetermined accuracy threshold, rejecting said speed.

### Claim 15 (depends on 14)

The method as claimed in claim 14, further comprising retrieving a speed limit and identifying a speed limit infraction by comparing said speed recorded for each said vehicle to said speed limit.

### Claim 16 (depends on 1)

The method as claimed in claim 1, further comprising:
providing a 2D optical receiver, wherein said 2D optical receiver being an image sensor adapted to provide images of said 2D detection zone;
driving the 2D optical receiver to capture a 2D image;
using image registration to correlate corresponding locations between said 2D image and said detection channels;
extracting vehicle identification data from said 2D image at a location corresponding to said location for said detected vehicle;
assigning said vehicle identification data to said unique identifier.

### Claim 17 (depends on 16)

The method as claimed in claim 16, wherein the vehicle identification data is at least one of a picture of the vehicle and a license plate alphanumerical code present on the vehicle.

### Claim 18 (depends on 17)

The method as claimed in claim 17, wherein the vehicle identification data includes said 2D image showing a traffic violation.

### Claim 19 (depends on 17)

The method as claimed in claim 17, further comprising extracting at least one of a size of characters on the license plate and a size of the license plate and comparing one of said size among different instances of the repeating to determine an approximate speed value.

### Claim 20 (depends on 16)

The method as claimed in claim 16, further comprising providing a 2D illumination source oriented to allow illumination of a 2D detection zone in said 3D detection zone and driving the 2D illumination source to emit pulses to illuminate said 2D detection zone and synchronizing said driving the 2D optical receiver to capture images with said driving the 2D illumination source to emit pulses to allow capture of said images during said illumination.

### Claim 21 (depends on 20)

The method as claimed in claim 20, wherein driving the 2D illumination source includes driving the 2D illumination source to emit pulses of a duration between 10 μs and 10 ms.

### Claim 22 (depends on 19)

The method as claimed in claim 19, wherein the 2D illumination source is at least one of a visible light LED source, an infrared LED light source and laser.

### Claim 23 (depends on 19)

The method as claimed in claim 19, wherein the 3D optical emitter and the 2D illumination source are provided by a common infrared LED light source.

### Claim 24 (depends on 19)

The method as claimed in claim 19, wherein the vehicle identification data is at least two areas of high retroreflectivity apparent on the images, said detecting a presence includes extracting observations in the individual digital signals and intensity data for the observations, the method further comprising correlating locations for the areas of high retroreflectivity and high intensity data locations in the observations, wherein each said area of high retroreflectivity is created from one of a retroreflective license plate, a retro-reflector affixed on a vehicle and a retro-reflective lighting module provided on a vehicle.

### Claim 25 (depends on 16)

The method as claimed in claim 16, further comprising combining multiples ones of said captured images into a combined image with the vehicle and the vehicle identification data apparent.

### Claim 26

A system for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment, the system comprising:
a 3D optical emitter provided at an installation height and oriented to allow illumination of a 3D detection zone in the environment;
a 3D optical receiver provided and oriented to have a wide and deep field of view within the 3D detection zone, the 3D optical receiver having a plurality of detection channels in said field of view;
a controller for driving the 3D optical emitter into emitting short light pulses toward the detection zone, the light pulses having an emitted light waveform;
the 3D optical receiver receiving a reflection/backscatter of the emitted light on the vehicles in the 3D detection zone, thereby acquiring an individual digital full-waveform LIDAR trace for each channel of the 3D optical receiver;
a processor for detecting a presence of a plurality of vehicles in the 3D detection zone using the individual digital full-waveform LIDAR trace and the emitted light waveform, detecting a position of at least part of each the vehicle in the 3D detection zone, recording a time at which the position is detected, assigning a unique identifier to each vehicle of the plurality of vehicles detected and tracking and recording an updated position of each vehicle of the plurality of vehicles detected and an updated time at which the updated position is detected, with the unique identifier;
a 2D optical receiver, wherein the 2D optical receiver is an image sensor adapted to provide images of the 2D detection zone; and
a driver for driving the 2D optical receiver to capture a 2D image;
the processor being further adapted for using image registration to correlate corresponding locations between said 2D image and said detection channels and extracting vehicle identification data from the 2D image at a location corresponding to the location for the detected vehicle; and assigning the vehicle identification data to the unique identifier.

### Claim 27 (depends on 26)

The system as claimed in claim 26, wherein said processor is further for determining and recording a speed for each the vehicle using the position and the updated position of one of the instances of the repeating step and an elapsed time between the time of the position and the updated time of the updated position, with the unique identifier.

### Claim 28 (depends on 26)

The system as claimed in claim 26, further comprising
a 2D illumination source provided and oriented to allow illumination of a 2D detection zone in the 3D detection zone;
a source driver for driving the 2D illumination source to emit pulses;
a synchronization module for synchronizing said source driver and said driver to allow capture of said images while said 2D detection zone is illuminated.

### Claim 29

A method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment, the method comprising:
providing a 3D optical emitter at an installation height oriented to allow illumination of a 3D detection zone in said environment;
providing a 3D optical receiver oriented to have a wide and deep field of view within said 3D detection zone, said 3D optical receiver having a plurality of detection channels in said field of view;
driving the 3D optical emitter into emitting short light pulses toward the detection zone, said light pulses having an emitted light waveform;
receiving a reflection/backscatter of the emitted light on the vehicles in the 3D detection zone at said 3D optical receiver, thereby acquiring an individual digital full-waveform LIDAR trace for each detection channel of said 3D optical receiver;
using said individual digital full-waveform LIDAR trace and said emitted light waveform, detecting a presence of a plurality of vehicles in said 3D detection zone, a position of at least part of each said vehicle in said 3D detection zone and a time at which said position is detected;
assigning a unique identifier to each vehicle of said plurality of vehicles detected;
repeating said steps of driving, receiving, acquiring and detecting, at a predetermined frequency;
at each instance of said repeating step, tracking and recording an updated position of each vehicle of said plurality of vehicles detected and an updated time at which said updated position is detected, with said unique identifier,
wherein said detecting said presence includes:
extracting observations in the individual digital full-waveform LIDAR trace and intensity data for the observations;
finding at least one blob in the observations;
computing an observation weight depending on the intensity of the observations in the blob;
computing a blob gravity center based on the weight and a position of the observations in the blob.

### Claim 30

A vehicle detection system, comprising:
a 3D optical emitter oriented to allow illumination of a detection zone;
a 3D optical receiver having a plurality of detection channels;
a controller for driving the 3D optical emitter into emitting short light pulses toward the detection zone;
the 3D optical receiver for receiving a reflection/backscatter of the emitted light on vehicles in the detection zone, thereby for acquiring an individual full-waveform light detection and ranging (LIDAR) trace for each channel of the 3D optical receiver;
a processor configured to detect a presence of a plurality of vehicles in the detection zone using the individual full-waveform LIDAR trace for each channel, capture a plurality of vehicle measurements from the LIDAR trace in time steps, detect a position of at least part of each vehicle in the detection zone, and assign a unique identifier to each vehicle of the plurality of vehicles detected;
an image sensor adapted to provide 2D images of the detection zone; and
a driver for driving the image sensor to capture the 2D images;
the processor being further configured to:
correlate said vehicle measurements with corresponding locations within said 2D images;
associate individual ones of the measurements with one another based on a spatial relationship to measurements from preceding time steps to define one or more measurement sets, each measurement set associated with a unique identifier and representing a vehicle of the plurality of vehicles;
extract vehicle identification features for a vehicle of the plurality of vehicles from a corresponding location within a 2D image and compute a distance estimate using the vehicle identification features; and
use the measurement set representing a vehicle of the plurality of vehicles to estimate a position of the vehicle using individual measurements of the measurement set, weighted according to measurement intensity.

### Claim 31 (depends on 30)

The vehicle detection system of claim 30, wherein said spatial relationship is defined using a Kalman filter.

### Claim 32 (depends on 30)

The vehicle detection system of claim 30, wherein said processor is configured to match ones of said series of measurements with measurements from preceding time steps based on proximity to a position predicted from said measurements from preceding time steps.

### Claim 33 (depends on 32)

The vehicle detection system of claim 32, wherein said processor is configured to define a spatial mask based on a kinematic movement model of a set of said measurements and associating new measurements with the set of said measurements based on said new measurements falling within said spatial mask.

### Claim 34 (depends on 33)

The vehicle detection system of claim 33, wherein said processor is configured to suppress measurements not associated with other measurements within a defined period of time.

### Claim 35 (depends on 30)

The vehicle detection system of claim 30, wherein said processor is configured to model occlusion caused by interacting targets in the area using a traffic light state in the area.

### Claim 36 (depends on 30)

The vehicle detection system of claim 30, wherein said processor is configured to encode state information comprising a number of vehicles detected in the detection zone and a position for each vehicle detected.

### Claim 37 (depends on 36)

The vehicle detection system of claim 36, wherein said state information comprises a detection age for each vehicle detected.

### Claim 38 (depends on 30)

The vehicle detection system of claim 30, wherein said vehicle identification features comprise license plate features and said processor is configured to compute a distance estimate based on said license plate features.

### Claim 39 (depends on 38)

The vehicle detection system of claim 38, wherein said processor is configured to compute a speed estimate based on vehicle identification data extracted from a sequence of 2D images.

### Claim 40

A vehicle-mounted detection system, comprising:
a 3D optical emitter oriented to illuminate a detection zone;
a 3D optical receiver having a plurality of detection channels;
a controller configured to drive the 3D optical emitter into emitting short light pulses toward the detection zone;
the 3D optical receiver configured to receive a reflection/backscatter of the emitted light on vehicles in the detection zone, for acquiring an individual full-waveform light detection and ranging (LIDAR) trace for each channel of the 3D optical receiver;
a processor configured to detect a presence of a plurality of vehicles in the detection zone using the individual full-waveform LIDAR trace for each channel, capture a series of vehicle measurements from the LIDAR trace, the vehicle measurements comprising measurements at a plurality of time steps, detect a position of at least part of each vehicle of said plurality of vehicles in the detection zone, and assign a unique identifier to each vehicle of the plurality of vehicles detected;
an image sensor adapted to provide 2D images of the detection zone; and
a driver configured to drive the image sensor to capture the 2D images;
the processor further configured to:
correlate said vehicle measurements with corresponding locations within said 2D images;
group the vehicle measurements based on spatial relationship of the vehicle measurements to measurements from preceding time steps, to define one or more measurement sets, each measurement set representing a vehicle of the plurality of other vehicles;
extract vehicle feature data for a vehicle of the plurality of other vehicles from a corresponding location within a 2D image and compute a distance estimate for the vehicle based on the vehicle feature data; and
use the measurement set representing a vehicle of the plurality of other vehicles to estimate a position of said vehicle using individual measurements of the measurement set, weighted according to measurement intensity.

### Claim 41 (depends on 40)

The detection system of claim 40, wherein said spatial relationship is defined using a Kalman filter.

### Claim 42 (depends on 40)

The detection system of claim 40, wherein said processor is configured to match ones of said series of measurements with measurements from preceding time steps based on proximity to a position predicted from said measurements from preceding time steps.

### Claim 43 (depends on 42)

The detection system of claim 42, wherein said processor is configured to define a spatial mask based on a kinematic movement model of a set of said measurements and associating new measurements with the set of said measurements based on said new measurements falling within said spatial mask.

### Claim 44 (depends on 40)

The detection system of claim 40, wherein said processor is configured to suppress measurements not associated with other measurements within a defined period of time.

### Claim 45 (depends on 40)

The detection system of claim 40, wherein said processor is configured to model occlusion caused by interacting targets in the area using a traffic light state in the area.

### Claim 46 (depends on 40)

The detection system of claim 40, wherein said processor is configured to encode state information comprising a number of vehicles detected in the detection zone and a position for each vehicle detected.

### Claim 47 (depends on 46)

The detection system of claim 46, wherein said state information comprises an age for each vehicle detected.

### Claim 48 (depends on 40)

The detection system of claim 40, wherein said vehicle feature data comprises license plate features and said processor is configured to compute a distance estimate based on said license plate features.

### Claim 49 (depends on 48)

The detection system of claim 48, wherein said processor is configured to compute a speed estimate based on vehicle feature data extracted from a sequence of 2D images.