System and method for multipurpose traffic detection and characterization

A vehicle detection 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 zonein 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 channelsin 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 for receiving a reflection/backscatter of the emitted light on the vehicles in the 3D detection zone, thereby for acquiring an individual digital full-waveform light detection and ranging (LIDAR) trace for each channel of the 3D optical receiver;
• a processor configured for detecting a presence of a plurality of vehicles in the 3D detection zone using the individual digital full-waveform LIDAR traceand the emitted light waveform, capturing a series of vehicle measurements from the LIDAR trace, 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, and assigning a unique identifier to each vehicle of the plurality of vehicles detectedand 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 image sensor to capture a 2D image the images;
• the processor being further adapted configured for using image registration to correlate corresponding locations between said 2D image images and said detection channels, estimating a length of a vehicle by fitting a first line to a first subset of said series of vehicle measurements, estimating a width of a vehicle by fitting a second line to a second subset of the vehicle measurements, and extracting vehicle identification data from the 2D image at a location corresponding to the location for the a detected vehicle;
• and assigning the vehicle identification data to the unique identifier.

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.