Cooperative Localization via DSRC and Multi-Sensor Multi-Target - - PowerPoint PPT Presentation

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Cooperative Localization via DSRC and Multi-Sensor Multi-Target Track Association

Presenter: Preeti Pillai Authors: Ahmed Hamdi Sakr and Gaurav Bansal Toyota InfoTechnology Center, Mountain View, California, U.S.A. PPNIV workshop, IEEE ITSC 2016

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Outline

• Overview of the Proposed System
• Positioning Improvement System
• Performance Evaluation and Results

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Outline

• Overview of the Proposed System
• Positioning Improvement System
• Performance Evaluation and Results

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DSRC Technology

• DSRC: Dedicated Short-Range

Communication

– Ad hoc networking technology that allows vehicles to communicate with each other, roadside devices, bicycles, pedestrians, trains, etc.

• An active research for many

years.

• Moving toward deployment.
• Many stakeholders in US and

elsewhere.

• Goal: use DSRC Basic

Safety Messages to improve localization.

Item Time 3D Position Position Accuracy Speed Heading Steering Wheel Angle Acceleration Brake Status Vehicle Size Event Flags Path History Path Prediction Other optional fields

Basic Safety Message (BSM)

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Main Idea

• The host vehicle (HV) uses Kalman

filters (KF) to fuse position information from two independent sources to reduce the uncertainty of the measurements.

• The two sources are:

1. its own position information

• btained by the on-board GPS

receiver. 2. position information of nearby vehicles (RV) collected by an on-board ranging sensor (RS) and the messages received via the DSRC transceiver from

• ther equipped vehicles.

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Example

• GPS of HV reports (1.25,-0.9).
• 2 RVs send their position to HV as:

(-4.1,6.25), (3.5,9).

• 3 RVs are detected by an RS at:

(6.9,-28.5°), (8.25,23°), (8.93,2°) =(-4.5,6,96), (0.94,9.8), (4.5,8.5).

• After matching vehicles, 2 estimates
• f HV position are:

– (-4.1,6.25) - (6.9,-28.5°) = (-0.81,0.19) – (3.5,9) - (8.25,23°) = (0.27,1.4)

• Take the average* of all estimates, HV is estimated at (0.24,0.23)

with 0.33 m error which is 78% more accurate compared to (1.25,- 0.9) with 1.54 m error.

* Note that using the average is just an example, in our proposed method, we use a Kalman filter-based approach to fuse data.

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Outline

• Overview of the Proposed System
• Positioning Improvement System
• Performance Evaluation and Results

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Positioning Improvement System

• The proposed solution consists of three steps:

1. Tracking and synchronization. 2. Multi-sensor multi-target track association (MTA). 3. Data fusion.

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Tracking and Synchronization

• KF has been proven to be optimal for tracking under linear Gaussian

noise assumption.

• Hence, each HV has N KFs to track N RVs based on an age

threshold and prioritization.

• DSRC messages arrive at different times and not synchronized.
• A zero-gain open-loop KF is used for

synchronization to predict the position at any given time instant.

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Track Association (MTA)

• DSRC detects a set of N RVs.
• Ranging sensor detects a set of K RVs.
• K and N are not necessarily equal.
• A Multi-sensor Multi-target Track Association problem to find the

intersection between the two sets.

• Calculate the statistical distance
• Each match has to pass a validation gate:

: Distance at time k : Covariance at time k

• : Position of vehicle at time k

: History window size 1 : Confidence region χ

: Chi-square distribution

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Track Association (MTA)

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Data Fusion

• Combine the different estimates of the position of the HV into one

solution.

• The inputs to DF-KF are:

– position information of the HV obtained by its on-board GPS receiver. – position estimates of the HV derived from the position of nearby RVs that are simultaneously detected by the RS and DSRC receiver.

• In the worst case, the DF-KF has only one input

from the on-board GPS receiver when: – No RVs are detected by the RS, – No BSM messages are received by the DSRC transceiver, or – No matching is found.

• This case is equivalent to the baseline case.

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Outline

• Overview of the Proposed System
• Positioning Improvement System
• Performance Evaluation and Results

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Performance Evaluation

• MATLAB-based simulation.
• Car-following model where the acceleration of a vehicle is evaluated

based on the current position and velocity of the leading vehicle:

• GPS errors are assumed to be Gaussian-distributed with zero mean.
• Standard deviation and correlation coefficient of GPS errors are
• btained experimentally.

: Accelation : Velocity : Position of vehicle : Separation distance : Position error weighting parameter : Velocity error weighting parameter

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Simulation Parameters

• Simulation parameters:

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Performance Metrics

• Mean Squared Error (MSE): average of the squares of the

deviations between the true position and the estimated position of the HV:

• MSE reduction: the reduction of MSE gained by using the proposed

approach compared to the baseline case:

• Track matching accuracy (TMA): the percentage of correct

matching decisions taken by the MTA algorithm.

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MSE vs. N

74.2% improvement 5 RVs 57.8% improvement 2 RVs 43.8% improvement 1 RV

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TMA vs. Spatial Error

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Conclusions

• We proposed an algorithm to improve HV positioning combining

GPS data, DSRC BSMs, and ranging sensor tracks.

• After performing track association, HV uses a Kalman filters-based

approach to fuse information from different sensors.

• Simulation results show that our algorithm highly improves

positioning and reduces spatial errors.

• The effect of correlation is also studied and we show that the

proposed system is beneficial even when errors at nearby GPS receivers are correlated.

• Future Work:

– Modelling errors in urban environments. – Conducting field experimentations.

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Questions.. Ahmed Hamdi Sakr ahmed.sakr@umanitoba.ca Gaurav Bansal gbansal@us.toyota-itc.com