System and method for multipurpose traffic detection and characterization

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.

Description

where −π<α≤π is the angle between the x axis and the normal of the line, r≥0 is the perpendicular distance of the line to the origin; (x, y) is the Cartesian coordinates of a point on the line. The covariance matrix of line parameters is:

$\mathrm{cov}\left(r,\alpha \right)=\left[\begin{array}{cc}{\sigma }_r^2& {\sigma }_{r\alpha }\\ {\sigma }_{r\alpha }& {\sigma }_{\alpha }^2\end{array}\right]$

FIG. 19 shows a state diagram for the 3D real-time detection multi-object tracker. The core of the tracker 91A is based on a Kalman Filter in all weather and lighting conditions. The observation model 90 is illustrated in FIG. 21 which presents an example method to compute the vehicle position by weighting each 3D observation according to its height amplitude. This method permits to improve the accuracy of the estimated position with respect to using only the x and y Cartesian positions.

Expression 301 computes the blob position as follows:
P_blob=Σ_n=1^Nπⁿ·Pⁿ

where πⁿ is the intensity weight for the observation n, nϵ{1, . . . , N}, and N is the number of observation grouped together. Step 301 is followed by computing the observation weight depending on the intensity at step 302.

The function 300 normalizes the weight πⁿ according to the amplitude Aⁿ of the observation Pⁿ:

${\pi }^n=\frac{A^n}{{\Sigma A}^n}$

The state evolution model 92 is represented by the classical model called speed constant. Kinematics model can be represented in a matrix form by:
p_k+1=F·p_k+G·V_k, V_k˜N(0,Q_k)

where p_k=(x_obs,{dot over (x)}_obs,y_obs,{dot over (y)}_obs) is the target state vector, F the transition matrix which models the evolution of p_k, Q_k the covariance matrix of V_k, and G the noise matrix which is modeled by acceleration.

$F=\left[\begin{array}{cccc}1& \mathrm{\Delta T}& 0& 0\\ 0& 1& 0& 0\\ 0& 0& 1& \Delta T\\ 0& 0& 0& 1\end{array}\right]$ $G=\left[\begin{array}{cc}\frac{{\Delta T}^2}{2}& 0\\ \mathrm{\Delta T}& 0\\ 0& \frac{{\Delta T}^2}{2}\\ 0& \mathrm{\Delta T}\end{array}\right]$ $Q_k=\left[\begin{array}{cc}{\sigma }_x^2& 0\\ 0& {\sigma }_y^2\end{array}\right]$

The equation observation can be written as:
Z_k=H·p_k+W_k, W_k˜N(0,R_k)

where Z_k=(x_obsk,y_obsk)^t is the measurement vector, H the measurement sensitivity matrix, and R_k the covariance matrix of W_k.

$H=\left[\begin{array}{cccc}1& 0& 0& 0\\ 0& 0& 0& 0\\ 0& 0& 1& 0\\ 0& 0& 0& 0\end{array}\right]$ $R_k=\left[\begin{array}{cc}{\sigma }_{{\mathrm{obs}}_x}^2& 0\\ 0& {\sigma }_{{\mathrm{obs}}_y}^2\end{array}\right]$

The state space model 93A is based on probabilistic framework where the evolution model is supposed to be linear and the observation model is supposed to be Gaussian noise. In a 3D image, the system state encodes the information observed in the scene, e.g. the number of vehicles and their characteristics is x_k^N=(p_k^N, l_k^N) with N as the number of detected vehicles, where p_k^N denotes the 2D position of object N at iteration k, l_k^N gives identification, age, lane and the object classification.

FIG. 20 shows a state diagram for 3D real-time detection multi-object joint tracker. The core of 91B is based on a Kalman Filter which addresses the issue of interacting targets, which cause occlusion issues. When an occlusion is present, 3D data alone can be unreliable, and is not sufficient to detect, at each frame, the object of interest. If the algorithm uses the traffic light state 85, occlusions can be modeled with a joint state space model 93B. The multi-object joint tracker includes a multi-object interaction distance which is implemented by including an additional interaction factor in the vehicle position. The state space model 93B encodes the observations detected in the scene, e.g. the number of vehicles, the traffic light state and the interaction between the vehicles located in the same lane by concatenating their configurations into a single super-state vector such as: X_k=(O_k, x_k¹, . . . , x_k^N) with O_k the size of state space at iteration k and x_k^N=(p_k^N, l_k^N) the state vector associated with the object N, where p_k^N denotes the 2D position of the object N at iteration k, l_k^N gives identification, age, lane, class, traffic light state and the object interaction.

Before integrating measures into the filter, a selection is made by a two-step procedure shown in FIGS. 22 and 23: first at step 400 validation gate, then at step 401A/B data association. The validation gate is the ellipsoid of size N_z (dimension of vector) defined such as:
θ^t·S⁻¹·θ≤γ

where θ^t=Z_k− is the innovation, S the covariance matrix of the predicted value of the measurement vector and γ is obtained from the chi-square tables for N_z degree of freedom. This threshold represents the probability that the (true) measurement will fall in the gate. Step 400 is followed by step 401A/B which makes the matching between a blob and a hypothesis. Then, (i) consider all entries as new blobs; (ii) find the corresponding entries to each blob by considering gating intervals around the predicted position of each hypothesis, (iii) choose the nearest entry of each interval as the corresponding final observation of each blob. At step 402, the tracking algorithm uses a track management module in order to change the number of hypothesis. This definition is: (i) if, considering the existing assumption, there occurs an observation that cannot be explained, the track management module proposes a new observation; (ii) if an assumption does not find any observation after 500 ms, the track management module proposes to suppress the assumption. In this case, of course, an evolution model helps to guide state space exploration of the Kalman filter algorithm with a prediction of the state. Finally, step 403 uses a Kalman framework to estimate the final position of the vehicle.

In a 3D image, the system state encodes the information observed in the scene, the number of vehicles and their characteristics is X_k=(O_k, x_k¹, . . . , x_k^N) with O_k the size of state space (number of detected vehicles) at iteration k and x_k^N=(p_k^N, l_k^N) the state vector associated with object N, where p_k^N denotes the 2D position of object N at iteration k, l_k^N gives identification, age, lane and the object classification. Step 90 and 92 are unchanged.

FIG. 24 shows the steps performed during the execution of the classification algorithm. At step 500, the algorithm checks if a line is detected in the 3D image. If a line is detected, step 500 is followed by step 501 which computes vehicle length. Vehicle length is defined as the overall length of the vehicle (including attached trailers) from the front to the rear. In order to calculate the length, two different positions are used: X₀ and X₁ . . . X₀ is given by the position of the first detected line and X₁ is given by the trigger line 1 (for example). Once the speed has been estimated, the vehicle length l can be determined such as:

l[m]=s[m/S]*(X₁(t)[s]−X₀(t)[s])−(X₁(x)[m])−X₀(x)[m])+Seg[m]+TH[m] where s is the vehicle speed, Seg is the length of the detected line and TH is a calibration threshold determined from a large dataset.

If the line is not detected at step 500, step 500 is followed by step 502 which computes the vehicle height. The vehicle height is estimated during the entry into the sensor field of view. As shown in FIG. 26, for a known configuration of the detection system, there is a direct geometric relationship between the height of a vehicle 601 and the detection distance 600. The accuracy 602 is dependent on the half-size of the vertical FOV angle 603. Height measurement is validated if the accuracy is lower than a threshold.

Finally, step 502 is followed by step 503 which computes the vehicle width. Over the vehicle blob, let (y_l, x) be leftmost pixel and (y_r, x) be the rightmost pixel in the vehicle blob for a given x. Then the width w of the object is determined from the following formula:
w=|y_r−y_l|

FIGS. 25A, 25B and 25C show a result of vehicle classification based on the classification algorithm. For example, in FIG. 25A, the classification result is a heavy vehicle; in FIG. 25B, it is a four-wheeled lightweight vehicle and in FIG. 25C, it is a two-wheeled lightweight vehicle. The information from the detection system is flexible and can be adapted to different schemes of classification. FIG. 25 illustrates graphically the basic elements of the concept of an object-box approach which is detailed below and in FIG. 27 and FIG. 28.

The object-box approach is mainly intended for vehicles because this approach uses the vehicle geometry in a LEDDAR image. The vehicles are represented by a 3D rectangular box of detected length, width and height. The 3D size of the rectangular box will vary depending on the detections in the FOV. FIGS. 27A, 27B, 27C and 27D show top view frames of a vehicle detected by the LEDDAR sensor. FIGS. 28A, 28B, 28C and 28D show corresponding side view frames of the vehicle of FIG. 27.

FIGS. 27A, 27B, 27C, 27D and FIGS. 28A, 28B, 28C, 28D show the changing 3D size of the rectangle 701 for four example positions of a vehicle 702 in the 3D sensor FOV 703. When a vehicle 702 enters the 3D sensor FOV 703, two detections are made on the side of the vehicle (see FIG. 27A) and one detection is made for the top of the vehicle (see FIG. 28A). The 3D rectangle is initialized with a length equal to 4 m, a width of 1.5 m and a height O_Hm given by:
O_Hm=H_s−dist*tan(θ)

where H_s is the sensor height 704, dist is the distance of the detected vehicle and θ is sensor pitch.

FIG. 27B and FIG. 28B represent detections when the vehicle is three-fourths of the way in the detection FOV. Eight side detections are apparent on FIG. 27B and one top detection is apparent on FIG. 28B. The dimensions of the 3D rectangle are calculated as follows:

The width is not yet adjusted because the vehicle back is not yet detected.
O_l(k)=max(L₂−L₁,O_l(k−1))
O_h(k)=max(O_Hm,O_h(k−1))

where the points of a segment are clockwise angle sorted so L₂ is the point with the smallest angle and L₁ is the segment-point with the largest angle. O_l(k) and O_h(k) are respectively the current length and height value at time k.

FIG. 27C and FIG. 28C represent detections when the back of the vehicle begins to enter in the detection FOV. Eight side detections and two rear detections are apparent on FIG. 27C while one detection is apparent on FIG. 28C. The dimensions of the 3D rectangle are calculated as follows:
O_l(k)=max(L₂−L₁,O_l(k−1))
O_h(k)=max(O_Hm,O_h(k−1))
O_w(k)=max(L₄−L₃,O_w(k−1))

As for the horizontal segment representing the side of the vehicle, the points of the vertical segment representing the rear and/or the top of the vehicle are clockwise angle sorted, so L₄ is the point with the smallest angle and L₃ is the segment-point with the largest angle. O_l(k), O_h(k) and O_w(k) are respectively the current length, height and width value at time k.

FIG. 27D and FIG. 28D represent detections when the back of the vehicle is fully in the detection FOV. Six side detections and four rear detections are apparent on FIG. 27D while one detection is apparent on FIG. 28D. The width O_lm dimension is calculated as follows:
O_lm(k)=α*(L₄−L₃)+(1−α)*O_lm(k−1)

where O_lm(k) is the current width at time k and α is the filtering rate.

The size of the vehicle can then be determined fully.

The segmentation algorithm 800 based on a 3D bounding box for selection of the relevant measures is illustrated in FIG. 29. The first three steps are identical to that of FIG. 17. If step 120 finds horizontal lines, then step 120 is followed by step 121. As explained above, the points of a segment are clockwise angle sorted with L₂, the smallest angle and L₁ the largest angle. This segment length is given by L₂−L₁. Otherwise, the next step 123 initializes the 3D bounding box with a default vehicle length. Step 121 is followed by step 122 which considers that two segments have a common corner if there is a point of intersection P_i between the two segments with |P_i−L₁| and |P_i−L₄| less than a distance threshold. If no corner is found, step 123 initializes the 3D bounding box with default values. Otherwise, step 124 computes the 3D bounding box dimensions from equations presented above with respect to FIG. 27C.

It is of interest to derive minimum variance bounds on estimation errors to have an idea of the maximum knowledge on the speed measurement that can be expected and to assess the quality of the results of the proposed algorithms compared with the bounds. In time-invariant statistical models, a commonly used lower bound is the Cramer-Rao Lower Bound (CRLB), given by the inverse of the Fisher information matrix. The PCRB can be used for estimating kinematic characteristics of the target.

A simulation was done according to the scenario shown in FIG. 30. The vehicle 130 is moving at a speed of 60 m/s along a straight line in lane 3. The PCRB was applied. As shown in FIG. 31, the tracking algorithm converges at point 903 at about σ_{{dot over (K)}F}=0.48 km/h after 80 samples. From point 900, it is apparent that after 16 samples, σ_{{dot over (K)}F}<3 km/h, from point 901 that after 28 samples, σ_{{dot over (K)}F}<1.5 km/h and from point 902 that after 39 samples, σ_{{dot over (K)}F}<1 km/h. Experimental tests confirmed the utility and viability of this approach.

Image Processing and Applications

The multipurpose traffic detection system uses a high-resolution image sensor or more than one image sensor with lower resolution. In the latter case, the control and processing unit has to process an image stitching by combining multiple images with different FOVs with some overlapping sections in order to produce a high-resolution image. Normally during the calibration process, the system can determine exact overlaps between images sensors and produce seamless results by controlling and synchronizing the integration time of each image sensor and the illumination timing and analyzing overlap sections. Infrared and color image sensors can be used with optical filters.

At night, a visible light is required to enhance the color of the image. A NIR flash is not visible to the human eye and does not blind drivers, so it can be used at any time of the day and night.

Image sensors can use electronic shutters (global or rolling) or mechanical shutters. In the case of rolling shutters, compensation for the distortions of fast-moving objects (skew effect) can be processed based on the information of the position and the speed of the vehicle. Other controls of the image sensor like Gamma and gain control can be used to improve the quality of the image in different contexts of illumination.

FIG. 32A is a photograph showing an example snapshot taken by a 5 Mpixels image sensor during the day. Vehicles are at a distance of approximately 25 m and the FOV at that distance covers approximately 9 m (almost equivalent to 3 lanes). FIGS. 32B, 32C and 32D show the quality of the image and resolution of FIG. 32A by zooming in on the three license plates.

FIG. 33A is a photograph showing an example snapshot taken by the image sensor at night without any light. This image is completely dark. FIG. 33B shows the same scene with infrared light. Two vehicles can be seen but the license plates are not readable even when zooming in as seen in FIG. 33C. The license plate acts as a retro-reflector and saturates the image sensing. FIGS. 34A and 34B use the same lighting with a lower integration time. The vehicle is less clear but the image shows some part of the license plate becoming less saturated. FIGS. 34C and 34D decrease a little more the integration time and produce a readable license plate.

One way to get a visible license plate at night and an image of the vehicle is to process several snapshots with different integration times (Ti). For example, when the 3D detection confirms the position of a vehicle in the detection zone, a sequence of acquisition of several snapshots (ex.: 4 snapshots with Ti1=50 μs, Ti2=100 μs, Ti3=250 μs and Ti4=500 μs), each snapshot taken at a certain frame rate (ex.: each 50 ms), will permit to get the information on a specific vehicle: information from the 3D sensor, a readable license plate of the tracked vehicle and an image from the context including the photo of the vehicle. If the system captures 4 images during 150 ms, a vehicle at 150 km/h would travel during 6.25 m (one snapshot every 1.5 m).

To enhance the quality of the image, high dynamic range (HDR) imaging techniques can be used to improve the dynamic range between the lightest and darkest areas of an image. HDR notably compensates for loss of information by a saturated section by taking multiple pictures at different integration times and using stitching process to make a better quality image.

The system can use Automatic License Plate Recognition (ALPR), based on Optical Character Recognition (OCR) technology, to identify vehicle license plates. This information of the vehicle identification and measurements is digitally transmitted to the external controller or by the network to back-office servers, which process the information and can traffic violation alerts.

The multipurpose traffic detection system can be used day or night, in good or bad weather condition, and also offers the possibility of providing weather information like the presence of fog or snowing conditions. Fog and snow have an impact on the reflection of the radiated light pulses of the protective window. In the presence of fog, the peak amplitude of the first pulse exhibits sizable time fluctuations, by a factor that may reach 2 to 3 when compared to its mean peak amplitude level. Likewise, the width of the first pulse also shows time fluctuations during these adverse weather conditions, but with a reduced factor, for example, by about 10 to 50%. During snow falls, the peak amplitude of the first pulse visible in the waveforms generally shows faster time fluctuations while the fluctuations of the pulse width are less intense. Finally, it can be noted that a long-lasting change in the peak amplitude of the first pulse can be simply due to the presence of dirt or snow deposited on the exterior surface of the protective window.

FIG. 35 shows an example image taken with infrared illumination with the overlay (dashed lines) representing the perimeter of the 16 contiguous detection zones of the 3DOR. Apparent on FIG. 35 are high intensity spots 140 coming from a section of the vehicle having a high retro-reflectivity characteristic. Such sections having a high retro-reflectivity characteristic include the license plate, retro-reflectors installed one the car and lighting modules that can include retro-reflectors. An object with retro-reflectivity characteristic reflects light back to its source with minimum scattering. The return signal can be as much as 100 times stronger than a signal coming from a surface with Lambertian reflectance. This retro-reflectivity characteristic has the same kind of impact on the 3DOR. Each 3D channel detecting a retro-reflector at a certain distance in its FOV will acquire a waveform with high peak amplitude at the distance of the retro-reflector. The numbers at the bottom of the overlay (in dashed lines) represent the distance measured by the multipurpose traffic detection system in each channel which contains a high peak in its waveform. Then, with a good image registration between the 2D image sensor and the 3D sensor, the 2D information (spot with high intensity) can be correlated with the 3D information (high amplitude at a certain distance). This link between 2D images and 3D detection ensures a match between the identification data based on reading license plates and measurements of position and velocity from the 3D sensor.

The license plate identification process can also be used as a second alternative to determine the speed of the vehicle with lower accuracy but useful as a validation or confirmation. By analyzing the size of the license plate and/or character on successive images, the progression of the vehicle in the detection zone can be estimated and used to confirm the measured displacement.

The embodiments described above are intended to be exemplary only. The scope of the invention is therefore intended to be limited solely by the appended claims.

Claims

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.

2. 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.

3. 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.

4. 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.

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

6. 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.

7. 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.

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

9. 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.

10. 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.

11. 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.

12. 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°.

13. 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.

14. 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.

15. 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.

16. 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.

17. 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.

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

19. 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.

20. 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.

21. 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.

22. 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.

23. 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.

24. 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.

25. 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.

26. 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.

27. 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 an updated position of one of the instances of the repeating step and an elapsed time between the time of the position and the an updated time of the updated position, with the unique identifier.

28. 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.

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.

30. The vehicle detection system of claim 26, wherein the processor is further configured to estimate a height of the vehicle.

31. The vehicle detection system of claim 26, wherein the processor is configured to estimate a volume of the vehicle based at least in part on the vehicle measurements.

32. The vehicle detection system of claim 26 wherein the processor is configured to identify a corner point of the vehicle less than a threshold distance from points on both of the first and second lines.

33. The vehicle detection system of claim 32, wherein the processor is configured to define a three-dimensional bounding box corresponding to the vehicle based on detection of corners.

34. The vehicle detection system of claim 33, wherein the three-dimensional bounding box represents an estimate of bounding dimensions of the vehicle.

35. The vehicle detection system of claim 34, wherein the processor is further configured to refine the estimate of the bounding dimensions as the light detection and ranging (LIDAR) trace is produced by reflection of the illumination signals from an increasing number of sides of the vehicle.

36. The vehicle detection system of claim 26, wherein the light detection and ranging (LIDAR) trace includes reflection of the illumination signals from a complete side of the vehicle, and wherein the processor is configured to determine dimensions of a three-dimensional bounding box corresponding to the vehicle based at least on full-waveform signal processing of the signal waveforms from the vehicle measurements for a complete side of the vehicle.

37. The vehicle detection system of claim 26, wherein the processor is configured to account for changes in the distance between the vehicle and the 3d optical emitter due to relative movement between the 3d optical emitter and the vehicle.

38. The vehicle detection system of claim 26, wherein the processor is configured to assign a classification to the vehicle based on a dimension of the vehicle.

39. The vehicle detection system of claim 38, wherein the classification is according to a classification scheme distinguishing two-wheeled and four-wheeled vehicles.

40. The vehicle detection system of claim 26, wherein the processor is configured to trigger an event based at least in part on a dimension of the vehicle.