Vehicle Classification by means of Inductive Loop Detectors and - - PowerPoint PPT Presentation

Vehicle Classification by means of Inductive Loop Detectors and Light Detection and Ranging Technology

Dan Brandesky NEXTRANS Intern, Ohio State University

Internship Overview

 Internship focused on vehicle classification  Use of new techniques applied to existing

technologies

– Precise vehicle classification through LIDAR imaging

process

– Length-based classification through use of freeway

loop detector impulse data

 Internship only partially completed due to later

start date (OSU still on quarter system)

LIDAR Overview

 LIDAR = Light Detection and Ranging  Works on same principle as radar, but uses

ultraviolet or infrared light

– Shorter wavelengths of light provide much greater

accuracy than radar

 LIDAR units can be used to produce a data

“image” of an object

 Units scan in single axis (horizontal or vertical)

LIDAR Animation

 LIDAR unit contains a

laser, reflected outward by a rotating mirror, shown in the top box

 Unit detects obstructions in

the laser’s path, shown in the middle box

 Processed data is shown in

the bottom box (converted from polar to Cartesian coordinates)

Image Source: http://en.wikipedia.org/wiki/File:LIDAR-scanned-SICK-LMS-animation.gif

LIDAR-Equipped Van

 Honda Odyssey minivan outfitted with one front

and one rear LIDAR unit, and two driver’s side LIDAR units

– Front LIDAR scan horizontally, side LIDAR scan

vertically

 Van also has one front and one rear camera, and a

driver’s side view camera

– Video data is then synchronized and cross-referenced

with LIDAR data during processing to develop algorithms for vehicle classification

LIDAR-Equipped Van

LIDAR Research

 Currently working with MATLAB-based software

tool to classify vehicular LIDAR detector data by category:

• Cars
• Small SUVs
• SUVs
• Pickups
• Buses
• Small/Large Single Unit Trucks
• Small/Large Dual Unit Trucks
• Motorcycles/Bicycles

 Use of LIDAR allows for more specific vehicle

classification than normal FHWA “axle count” methods

– Finer resolution of data – More accurate data for traffic surveys, etc.

LIDAR Data Collection

 Data is collected while van is being driven

– Several pre-defined routes are used to give multiple

data sets for single routes

– As van is driven, differential GPS unit logs van’s

location so that video and LIDAR data can be synced with a map of the route during processing

 At termination of a route, data is downloaded from

the van computer and transferred to the campus server

LIDAR Data Processing

 MATLAB-based processing tool  Two modes of operation:

– Ground Truth

 Manually compare video and corresponding LIDAR data to

determine if detected objects are indeed vehicles, or if they are

• ther objects (e.g. pedestrians)

– Vehicle Classification

 Manually re-process objects defined in earlier step as vehicles

into more specific categories mentioned in previous slide

LIDAR Data Processing

Ground Truth Mode

LIDAR Data Processing

Vehicle Classification Mode

LIDAR Detection

• Top image is stock photo of very

similar vehicle to the one shown in the LIDAR image

• LIDAR figure is clear enough to

discern axles and vehicle’s windows, and height and depth measurements (Y and Z axes, in meters) give even more information about detected vehicle

• From LIDAR data alone, it is clear

that this is not just a conventional pickup truck

• Allows for more accurate data

with fine resolution

Image Source: http://isuzu.sqserver.com/nprhd_vs_gm_c4500_16500.htm

Inductive Loop Detector Overview

 Inductive loops are wire loops buried in pavement,

connected to an inductive loop detector in the controller cabinet

 Detector is “tuned” to normal inductance of loop,

and when a vehicle passes over the loop, the loop inductance changes, and the detector sends and impulse to the controller

 Single loop detector stations (one per lane) are

typically deployed on freeways for real time traffic monitoring

Inductive Loop Detectors in Vehicle Classification

 Currently, only dual-loop detector stations (meaning a

station with two loops per lane, one after the other) are used for classifying vehicles and measuring speed

 Single loop detectors normally cannot classify vehicles

because of differences in vehicle speeds and lengths

– A long vehicle moving quickly could appear the same as a

shorter vehicle moving slowly

 Developing the ability to use single loop detectors for

vehicle classification is significant because there are many more single loops than dual loops, so it provides useful data from existing detectors

– Less implementation cost to municipalities

Single-Loop Vehicle Classification Research

 Developing software algorithms for single loop detectors to

classify vehicles by vehicle length

 Working to refine accuracy of algorithm

– Began with simple categories: short, medium, and long

 Currently working with software tool to develop more specific

categories by comparing loop detector impulse data from specific stations with video from adjacent traffic cameras

– Measure vehicle length in pixels with software tool, which matches loop

detector impulses with correct video frames

 Loop data is not always accurate due to various erroneous

detections

– Vehicles changing lanes cause impulses in two detectors by one vehicle – Incorrectly operating detectors may detect vehicles in adjacent lanes as

well as their own lanes

Single-Loop Vehicle Classification Data Processing

Work In Progress

 Continue to process LIDAR data  Work more with inductive loop vehicle

classification

 Learn more about programming of

MATLAB software tools

 Continue to collect data for analysis to

improve precision of classification algorithms