Research Activities

AutoSense

Primary Researcher:
Dr. Darragh Mullins

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones

Description:
Multifaceted autonomous vehicle research. Investigating sensors and V2X communication systems for pedestrian and vehicle monitoring from both vehicle and fixed infrastructure point-of-view. Test vehicle and infrastructure test-site based in NUI Galway University campus in the west of Ireland.

Funding:
SFI, Lero

Industry Collaborator:
Valeo

Start date:
March 2018, this project is currently active

AutoSense — Image Quality and Machine Vision Performance

Primary Researcher:
Diarmaid Geever

Supervisors:
Prof. Edward Jones, Prof. Martin Glavin, Dr. Brian Deegan

Description:

Correlating Image Quality and Machine Vision Performance. Investigating how object detection is impacted by image quality. How Robust is object detection in adverse weather conditions such as rain?  What image quality metrics are the most relevant when designing machine vision systems, and what are desirable image characteristics?

Funding:
SFI, Lero

Industry Collaborator:
Valeo

Start date:
September 2023, this project is currently active

AutoSense — Investigation into Event Cameras and Emergent Sensors in Challenging Conditions.

Primary Researcher:
Ethan Delaney

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones, Dr. Brian Deegan

Description:

Using RGB cameras exclusively in cars has inherent problems, such as struggling in low light conditions, situations with glare, and/or high motion blur, all of which an event camera handles well. This project is based around using event cameras and other emergent sensors to overcome these weaknesses. Primarily focussing on event-based cameras, this project also looks at other emergent sensors such as polarised cameras, and  range-gated sensors, and their benefits for use in an automotive setting e.g. pedestrian and vehicle detection.

Funding:
SFI, Lero

Industry Collaborator:
Valeo

Start date:
September 2023, this project is currently active

AutoSense — Adverse Weather and Autonomous Vehicles

Primary Researcher:
Tim Brophy

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones, Dr. Darragh Mullins

Description:
The main objective of this project is to examine and quantify the effects of inclement weather conditions, on a suite of automotive sensors, in the context of autonomous vehicles. This will encompass both fundamental research into the impact of adverse weather conditions on automotive sensors and on the combination of a sensor and associated algorithm. A focus will be placed on the availability and reliability of different sensors in an autonomous car operating in adverse weather conditions, i.e. different sensors perform well in different conditions so selecting the best combination of sensors for different conditions is important. Different approaches will be implemented to attempt to improve the system’s availability. The approaches will focus on the sensors themselves, the algorithms processing the data and the ability to maintain effective vehicle operation while using a suite of sensors that has a good cost-performance trade-off. 

Funding:
SFI, Lero

Industry Collaborator:
Valeo

Start date:
September 2018, this project is currently active

AutoSense — Integration of Fixed Sensors Nodes into a Vehicle to Everything System

Primary Researcher:
Dara Molloy

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones, Dr. Darragh Mullins

Description:
This research involves evaluating the use of environmental sensor nodes to increase the awareness of self-driving cars. All sensor types, including RGB cameras, LIDARs, RADARs, thermal cameras, and event-based cameras, are being explored for this use case with the goal of informing the self-driving car of nearby objects that it would otherwise be unaware of. Finding the optimal suite of sensors for this application involves an extensive sensor comparison where the object detection performance, along with a set of other key performance indicators, for each sensor are quantified. 

Funding:
SFI, Lero

Industry Collaborator:
Valeo

Start date:
September 2018, this project is currently active

AutoSense — Optimization of Data Compression and Transmission in Connected and Autonomous Vehicles

Primary Researcher:
Jordan Cahill

Supervisors:
Prof. Edward Jones, Prof. Martin Glavin, Dr. Darragh Mullins

Description:
This research is concerned with the optimization of sensor data in autonomous vehicle networks and in particular examining the benefits of signal compression technology, and its impact on sensor signal quality, and the consequences of this quality impact. Camera sensors are of particular interest in this research. In the near-future, interactions between vehicles and road infrastructure will provide a huge benefit to traffic efficiency, and overall road safety. The advancement of autonomous technology also offers considerable benefits to quality of life. However, with this advancement, fully automated cars may end up impeded by resource constraints, while still generating an enormous amount of data from different sensors (camera, radar, lidar, amongst other sensors). These multiple sensors lead to problems with data transmission, storage, and computation. This research concerns itself with finding novel ways to optimise different steps along the autonomous vehicle data pipeline.

Funding:
SFI, Lero

Industry Collaborator:
Valeo

Start date:
September 2018, this project is currently active

AutoSense — Modelling the Behaviour of Road Users at Junctions

Primary Researcher:
Esteban Moreno

Supervisors:
Prof. Edward Jones, Prof. Martin Glavin, Dr. Darragh Mullins

Description:
Modelling the behaviour of road users at junctions. This model will allow the ego-car to plan its actions optimally through intersections and drive safely and smoothly. It will allow the autonomous vehicle to understand its surroundings and predict the intentions/states of other agents. With the accurate prediction of pedestrian and traffic behaviour, the autonomous car will be able to plan ahead intelligently instead of acting in a reactive way.

Modelling the behaviour of road users in this context is a challenging task because intersections are highly dynamic environments, representing a complex situation/scenario where the ego-car or (autonomous vehicle) must interact with other road users who may behave in complex and unpredictable ways. 

Current designs of Connected and Autonomous Vehicles (CAVs) rely heavily on on-board sensors which have a limited field of view. This could be augmented by fixed infrastructure sensors. The CAR group has an important arsenal of sensors at its disposal that the model could use to add strategic interactions with pedestrians and other vehicles beyond the autonomous vehicle onboard sensors.

Funding:
SFI, Lero

Industry Collaborator:
Valeo

Start date:
September 2019, this project is currently active

AutoSense — Vehicular Communications for Enhanced Safety Applications

Primary Researcher:
Joseph Clancy

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones, Dr. Darragh Mullins

Description:
This area of research pertains to investigating the use of wireless communications technologies to inter-connect intelligent vehicles, with the end goal of increasing safety and traffic efficiency. In scenarios and environments with significant obstructions e.g., large vehicles or buildings, the on-board sensors of an intelligent vehicle may not be sufficient to ensure safe and effective travel. The introduction of wireless communications would enable the intelligent vehicle to leverage environmental sensors or the sensors of other vehicles to increase the intelligent vehicle's awareness of the obstructed environment. In order to successfully aid an intelligent vehicle, the wireless communications system must be able to quickly and reliably transfer safety-critical information to highly mobile entities. To this end, the key issues in this field involve effective mobility, routing, and resource management. 

Funding:
SFI, Lero

Industry Collaborator:
Valeo

Start date:
September 2019, this project is currently active

AutoSense — Object Detection in Low Light Conditions

Primary Researcher:
Hao Lin

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones, Dr. Darragh Mullins

Description:
Engineers have made significant development in recent years in Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. Object detection is a crucial component of autonomous driving; however, it is still not perfect and there are still problems that needs to be solved. One of the problems for object detection is the poor performance of object detection in low light conditions due to objects being obscured by poor illumination. In contrary to reality, object detection algorithms are expected to perform at a high standard in all lighting conditions as there are people’s lives at risk. This project investigates the potential ways of improving object detection performance under low light conditions. 

Funding:
SFI, Lero

Industry Collaborator:
Valeo

Start date:
September 2020, this project is currently active

AutoSense — Car and Environment Sensor Fusion

Primary Researcher:
Roshan George

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones, Dr. Darragh Mullins

Description:
This area of research focuses on investigating the use of multi-sensor data fusion from both static and dynamic perspectives. A fixed sensor node (i.e., static perspective) in the environment can provide the vehicle with better perception in a scenario where the vehicle's vision system (i.e., dynamic perspective) is obstructed. To this end, static and dynamic multi-sensor data fusion provides better performance in object detection, object tracking, and object localization. 

Funding:
SFI, Lero

Industry Collaborator:
Valeo

Start date:
September 2021, this project is currently active

HyperSense

Supervisors:
Dr. Brian Deegan, Prof. Martin Glavin, Prof. Edward Jones

HyperSense — Hypespectral imaging for autonomous vehicles.

Primary Researcher:
Jiarong Li.

Supervisors:
Dr. Brian Deegan, Prof. Martin Glavin, Prof. Edward Jones.

Description:
Hyperspectral imaging enhances autonomous driving by capturing a wide range of electromagnetic wavelengths, surpassing traditional RGB cameras. This advanced technology enables vehicles to analyze the environment in intricate detail, identifying materials, road surfaces, and hazards with precision. The heightened awareness improves decision-making, allowing autonomous vehicles to navigate complex scenarios, enhance safety, and increase overall reliability, even in challenging conditions like adverse weather or low-light situations.

Funding:
SFI, Lero

Industry Collaborator:
Valeo

Start date:
September 2023, this project is currently active

HyperSense — Hyperspectral Imaging & Data Analysis for Autonomous Vehicles.

Primary Researcher:
Imad Ali Shah.

Supervisors:
Dr. Brian Deegan, Prof. Martin Glavin, Prof. Edward Jones.

Description:

The research project focuses on integrating hyperspectral imaging technology and data analysis techniques to elevate the perception capabilities of autonomous vehicles. Hyperspectral sensors, capturing a wide range of electromagnetic wavelengths, offer a unique advantage in discriminating objects and materials beyond the visible spectrum. The research also involves optimizing Machine/Deep Learning algorithms, and statistical models for effective hyperspectral data analysis. By seamlessly integrating these methodologies into the perception pipeline of autonomous vehicles, the project aims to enable real-time decision-making in diverse and challenging environments. The anticipated outcomes include advancement in autonomous vehicle technology, and fostering safer and more adaptive transportation systems in the era of intelligent mobility.

Funding:
SFI, Lero

Industry Collaborator:
Valeo

Start date:
September 2023, this project is currently active

GrassSense

Primary Researcher:
Dr. Dallan Byrne

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones

Description:
GrassSense is a networked sensor solution for farm machinery deriving statistics at harvest time that will help predict the nutritional quality of foraged grass when the animals feed months later. Data analysis will highlight the benefits or drawbacks of the chosen harvesting times and techniques, storage environments and fertilisation methods. The filtered information will, for the first time, allow the farmer to analyse their forage practices in detail while offering machinery vendors and manufacturers insight into the consumer needs and practices.

Funding:
Enterprise Ireland

Industry Collaborator:
McHale Engineering

Start date:
July 2018, this project is currently active

GrassSense — Grass Monitoring for Silage Harvesting

Primary Researcher:
Tomás Crotty

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones, Dr. Dallan Byrne

Description:
This research focuses on recording data during the grass harvesting process. From this data, machine fuel and time usage parameters can be gained. The aim of the research is to estimate the machinery's response to a variety of grass crops to further improve the efficiency of the implementation. 

Funding:
SFI, Lero

Industry Collaborator:
McHale Engineering

Start date:
July 2018, this project is currently active

GrassSense — Driver Assistance Framework in Round Bale Silage Production

Primary Researcher:
Seán Harkin

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones, Dr. Dallan Byrne

Description:
This research focuses on data capture & algorithm development with the aim of using it to assist agricultural machinery operators, improving their productivity and in-turn, improving machinery efficiency.

Funding:
SFI, Lero

Industry Collaborator:
McHale Engineering

Start date:
July 2020, this project is currently active

MODA

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones

Description:

MODA — Situational Awareness in Partially Autonomous Vehicles

Primary Researcher:
Jibran Abbassi

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones

Description:

Funding:
Irish Research Council

Industry Collaborator:
Valeo

MODA — Occupant health in autonomous vehicles

Primary Researcher:
Stephen Treacy

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones

Description:

Funding:
Irish Research Council

Industry Collaborator:
Valeo

MODA — Physiological Factors in Readiness to Drive

Primary Researcher:
Tireoin McCabe

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones

Description:

Funding:
Irish Research Council

Industry Collaborator:
Valeo

Detection of Partially Occluded Objects in the Automotive Environment

Primary Researcher:
Shane Gilroy

Supervisors:
Prof. Martin Glavin, Prof. Edward Jones

Description:

Prediction of Sudden Cardiac Death in Implantable Cardioverter Defibrillators

Primary Researcher:
Ashkan Parsi

Supervisors:
Dr. Edward Jones, Dr. Martin Glavin

Description:

Funding:
Irish Research Council

Start date:
January 2017, this project is currently active

Real Time Infrared Detection of Vulnerable Road Users for Automotive Applications

Student:
Patrick Hurney

Supervisors:
Dr. Martin Glavin, Dr. Edward Jones and Dr. Fearghal Morgan

Description:
This project aims to develop an infrared pedestrian detection algorithm for use on an Intel Atom/Reconfigurable hardware hybrid system

Start date:
October 2009, this project is currently active

Next Generation In-Vehicle Networks

Student:
Shane Tuohy

Supervisors:
Mr. Liam Kilmartin and Dr. Martin Glavin

Description:
A recent trend in automotive technology is the deployment of multiple cameras and associated vision system functionality within vehicles. Traditionally the communication infrastructure associated with this functionality has been based on dedicated proprietary communication technologies. However a recent area of interest is in the use of traditional data networking technologies (specifically Ethernet and wireless LAN) as potential replacement technologies to form the basis of the next generation "networked vehicle".

This research is focussed on the specific demands of using such technology to network the numerous digital cameras and associated back end vision and scene analysis sub-systems that will likely be deployed in the next generation of vehicles. This work will focus on the development of realistic simulation models for the visions system network based on Ethernet and wireless LAN technologies and it will investigate the impacts which network topologies and protocols have on the performance of these networks and on the performance of the vision systems themselves.

Of particular interest in this research will be investigations into the development of realistic models for the noise environment in which these data networks will operate given the relatively high EMI environment found in most modern vehicles and on developing custom Quality of Service based protocols which utilise contextual information derived from the vision systems to manage information rates and flows throughout the system. Additional issues such as security, cost and reliability need also to be investigated in this work.

Start date:
October 2010. Project is currently active.

Project Publications:
S. Tuohy, M. Glavin, C. Hughes, E. Jones, L. Kilmartin. " An ns-3 Based Simulation Testbed for In-Vehicle Communication Networks ", 27th Annual UK Performance Engineering Workshop, UKPEW 2011, University of Bradford, Bradford, July 7-8th 2011. Presentation.

Evaluation and Control of Image and Video Quality in the Automotive Environment

This project will examine the development of methodologies for automotive-specific image/video quality assessment. The project will also investigate the use of these technologies in the context of image and video compression in automotive systems, and will examine how they can be used to help make system level design decisions.

Urban Autonomous Driving : Detection of other vehicle trajectory and intention

This project will examine Urban Autonomous Driving. Investigating the detection of other vehicle trajectory and intention.

Motion-Based Determination of the Automotive Environment for Vehicular Safety

This project will examine the development of methodologies for automotive-specific image/video quality assessment. The project will also investigate the use of these technologies in the context of image and video compression in automotive systems, and will examine how they can be used to help make system level design decisions.

Distance Determination using a Monocular Camera for an Automotive Environment

Student:
Diarmaid O Cualain

Supervisors:
Dr. Edward Jones and Dr. Martin Glavin

Description:
This project aims to develop a distance determination algorithm for a monocular camera, with particular emphasis with its use in an automotive environment.

Start date: November 2006.

Related projects:
Region Of Interest Determination and Obstacle Detection For Automotive Applications
Vulnerable Road User Detection in Low-Light Conditions
Shadow Removal From Digital Imagery

Project Publications:
Conferences:
D. O Cualain, M. Glavin, E. Jones and P. Denny, “ Distance Detection Systems for the Automotive Environment: A Review”, 15th IET Irish Signals and Systems Conference, ISSC 2007, University of Ulster at Magee, Derry, September 2007. Poster.

Other:
D. O Cualain, M. Glavin, E. Jones, " Distance Determination for Colission Avoidance in an Automotive Environment", College of Engineering and Informatics Research Day 2008, Poster

D. O Cualain, M. Glavin, " Lane Departure and Obstacle Detection Algorithim for use in an Automotive Environment", B.E. Project Report, 2006.

Vulnerable Road User Detection in Low-Light Conditions

This project developed automotive video processing algorithms to detect Vulnerable Road Users (VRU) such as pedestrians and other road vehicles in dark and low-light conditions.

Shadow removal from digital imagery

Student:
Robert McFeely

Supervisors:
Dr.Martin Glavin and Dr. Edward Jones

Description:
Automated Shadow Removal from image scenes. This involves two main stages of shadow detection and shadow correction. Detecting the shadows involves colour constancy, image segmentation, and texture analysis. The shadow correction is based directional smoothing and thin plate reconstruction.

Start date: January 2005. 

Related projects:
Distance Determination using a Monocular Camera for an Automotive Environment

An investigation of objective speech and audio quality assessment techniques, with a specific interest in automotive applications

Student:
Dermot Martin Campbell

Supervisors:
Dr. Edward Jones and Dr. Martin Glavin

Description:
The overall objective of this research project is to conduct research that integrates existing research findings in two areas:
Psychoacoustic based Digital Signal Processing (DSP) algorithms for audio processing and quality evaluation
Automotive Listening Quality Evaluation.
The psychoacoustic DSP algorithms examined are algorithms that are used in general applications to assess the quality of speech and audio signals. These algorithms provide a “score” indicting the quality of the speech or audio signal under investigation. They remove the need for human subjects to manually grade the quality of speech and audio signals based on their average perception. They are typically used in the development of communication networks, the testing of audio codecs eg MP3 and the development and testing of other such communication networks and devices.

The use of speech and audio quality evaluation in the automotive industry is not new. However, the use of psychoacoustic based algorithms to perform this task is quite novel and there appears to be a lack of academic research at this level in the recent past. Speech and audio quality assessment is typically used in automotive applications to test the listening performance for vehicle users in terms of noise interference from outside the vehicle cabin and the audio listening quality of the interior of the vehicle. The evaluation of the quality of the signals is not only relevant to vehicle manufacturers but also to developers of the audio equipment used in such vehicles.

This project focuses on developing DSP algorithmic solutions for general audio quality assessment, and with a further emphasis on applications in the automotive market.

Start date: October 2005. This project has been completed.

Project Publications:
Journals:
D. Campbell, E. Jones and M. Glavin, " Audio Quality Assessment Techniques - A Review, and Recent Developments", Elsevier Signal Processing, 89(8), pp. 1489-1500, August 2009. ScienceDirect

Conferences:
D. Campbell, E. Jones and M. Glavin, " Comparison of Temporal Masking Models for Audio Quality", 15th IET Irish Signals and Systems Conference, ISSC 2007, University of Ulster at Magee, September 2007.

Other:
D. Campbell, E. Jones and M. Glavin "Applications of Psychoacoustic Signal Quality Assessment Techniques", College of Engineering and Informatics Research Day 2008. Poster

Digital Lens and Perspective Correction for the Automotive Environment

Student:
Ciaran Hughes

Supervisor:
Dr. Martin Glavin and Dr. Edward Jones

Description:
This project deals with advanced techniques in the calibration of the intrinsic and extrinsic parameters of wide-angle and fish-eye lenses, and the correction of the distortion inherent in these types of lens cameras. Additionally, with the calibration of the extrinsic parameters, perspective distortion can also be removed. There is particular emphasis on the use of such cameras for displaying blind-zones of vehicles to drivers, including Heavy Goods Vehicles and smaller private vehicles. To this end, a detailed and rigorous mathematical examination of fish-eye perspectives has been carried out. In support of our findings, data has been extensively verified using large sets of synthetic calibration images, and smaller sets of real calibration images.

Start date: September 2005. This project has been completed.

Related Projects:
Automatic Calibration of Fisheye Lenses in the Automotive Environment

Project Publications:
Journals:
C. Hughes, P. Denny, M. Glavin and E. Jones, " Equidistant Fish-Eye Calibration and Rectification by Vanishing Point Extraction", IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(12), pp. 2289-2296, December 2010. IEEEXplore

C. Hughes, P. Denny, E. Jones, M. Glavin, " Accuracy of Fisheye Lens Models", Applied Optics, 49(17), pp. 3338-3347, June 2010. OpticsInfobase

M. Friel, C. Hughes, P. Denny, E. Jones and M. Glavin, " Automatic Calibration of Fish-eye Cameras from Automotive Video Sequences", IET Intelligent Transport Systems, 4(2), pp. 136-148, June 2010. IET Digital Library IEEEXplore

C. Hughes, R. McFeely, P. Denny, M. Glavin and E. Jones, " Equidistant (fθ) Fish-Eye Perspective with Application in Distortion Centre Estimation", Elsevier Image and Vision Computing, 28(3), pp. 538-551, March 2010. ScienceDirect

C. Hughes, M. Glavin, E. Jones and P. Denny, " Wide-angle camera technology for automotive applications: a review", IET Intelligent Transport Systems, 3(1), pp. 19-31, March 2009. IET Digital Library IEEE Xplore

Book Chapters:
C. Hughes, R. O'Malley, D. O'Cualain, M. Glavin and E. Jones, " Trends towards automotive electronic vision systems for mitigation of accidents in safety critical situations", in New Trends and Developments in Automotive System Engineering, Marcello Chiaberge (Ed.), ISBN: 978-953-307-517-4, InTech, (January 2011). INTECHopen

Conferences:
C. Hughes, E. Jones, M. Glavin and P. Denny, " Validation of Polynomial-based Equidistance Fish-Eye Models", 20th IET Irish Signals and Systems Conference, ISSC 2009, University College Dublin, June 2009. Poster

C. Hughes, M. Glavin, E. Jones and P. Denny, " Automotive Blind-Zones: A Review of Legislation and the Use of Close-Range Camera Systems", 1st International Symposium on Vehicular Computing Systems, Trinity College Dublin, July 2008.

C. Hughes, M. Glavin, E. Jones and P. Denny, " Review of Geometric Distortion Compensation in Fish-Eye Cameras", 16th IET Irish Signals and Systems Conference, ISSC 2008, NUI Galway, June 2008. IET Digital Library IEEE Xplore

Other:
C. Hughes, M. Glavin and E. Jones, " Robust and Accurate Principal Point Estimation Using the Five-Point Perspective Model of Fish-Eye Radial Distortion", College of Engineering and Informatics Research Day 2008. Poster

Region Of Interest Determination and Obstacle Detection For Automotive Applications

Student:
Lorcan Browne

Supervisors:
Dr. Edward Jones and Dr. Martin Glavin

Description:
Driven by increasing concerns over automotive safety, the development of on-board automotive driver assistance systems to alert drivers about potential collisions with objects such as pedestrians or vehicles has become a huge area of research. The aim of this project is to present a novel approach for the first processing stage in many of these types of systems. An algorithm is presented which uses a single visual camera to identify Regions Of Interest (ROIs) in front of a moving vehicle, which may contain objects that represent a “threat”. These ROIs are then categorised into different priorities depending on their position in the image, which in turn gives a measure of their threat level.

Start date: November 2005. This project has been completed.

Related projects:
Distance Determination using a Monocular Camera for an Automotive Environment
Vulnerable Road User Detection in Low-Light Conditions

Project Publications:
Conferences:
L. Browne, E. Jones and M. Glavin, “ Region Of Interest Detection For Automotive Applications”, 1st International Symposium on Vehicular Computing Systems, Trinity College Dublin, July 2008. Presentation

Other:
L. Browne, E. Jones and M. Glavin, "Object Detection And Characterisation In An Automotive Environment", College of Engineering and Informatics Research Day 2008. Poster

Automatic Calibration of Fisheye Lenses in the Automotive Environment

Student:
Myles Friel

Supervisors:
Dr. Edward Jones and Dr. Martin Glavin

Description:
This project proposed a technique for calibrating a lens in the automotive environment to compensate for radial distortion introduced by wide-angle or fisheye lenses, without the need for a specific calibration set-up. At present many car manufacturers are endeavouring to provide the driver with views of the car’s surroundings that are not directly visible (“blind spots”). To achieve this, wide angle fisheye lens cameras are fitted to many modern vehicles. However fisheye lenses introduce radial distortion to the resulting images, though this distortion can be compensated for with a suitable compensation procedure. Calibration of the lens is important for compensation, since each lens has different inherent properties which may change with time. However, in situations where recalibration of the lens is necessary, the need for a specific calibration set-up is particularly undesirable as it requires the driver to return to a service centre (e.g. the garage where the car was purchased), and it requires the technical staff to be trained in the calibration procedure. It is proposed in this project that radial distortion introduced by fisheye lenses can be calibrated using only the “every-day” images captured by the camera on a vehicle. The primary focus of this project is to identify and use straight lines in a typical automotive scene as a basis for calibrating for the radial distortion, since straight lines in the scene should remain straight in an image of that scene. The information thus extracted can then be used to determine parameters necessary for image compensation.

Start date: September 2005. This project has been completed.

Related projects:
Digital Lens and Perspective Correction for the Automotive Environment

Project Publications:
Journals:
M. Friel, C. Hughes, P. Denny, E. Jones and M. Glavin, " Automatic Calibration of Fish-eye Cameras from Automotive Video Sequences", IET Intelligent Transport Systems, 4(2), pp. 136-148, June 2010. IET Digital Library IEEEXplore

Conferences:
M. Friel, E. Jones, M. Glavin and C. Hughes, " Comparison of Feature Detection Methods for an Automotive Camera System", 15th IET Irish Signals and Systems Conference, ISSC 2007, University of Ulster at Magee, Derry, September 2007

An Efficient Implementation of Bluetooth Wireless Technology for an Automotive Environment

Student:
Alan Molloy

Supervisor:
Dr. Martin Glavin

Description:
This project implements a low-cost, low-power wireless network for an automotive environment, based on Bluetooth wireless technology. It is envisaged that this network will eb implemented between a device known as a Remote Vehicle Controller (RVC), which is similar in size and aesthetic to a television remote control, and another device situated in the dashboard of a vehicle. These components will be capable of transferring information, which will be used for diagnostic, maintenance and control purposes, and will ultimately facilitate future integration of features to ehance the vehicle owners' experience.

Start date: September 2001. This project has completed.

Project Publications:
Conferences:
P. Doherty, M. Glavin, A. Molloy, F. Morgan, " A Review of Bluetooth Security in the Automotive Environment", 12th IEE Irish Signals and Systems Conference, ISSC 2004, Queen's University Belfast, July 2004. IET Digital Library

L. Kelly, A. Molloy, M. Glavin, F. Morgan, " Error Resilient Image Transmission over a Bluetooth Network", 11th IEE Irish Signals and Systems Conference, ISSC 2003, University of Limerick, July 2003.

Error resilient image transmission over a wireless Bluetooth network

Student:
Lisa Kelly

Supervisor:
Dr. Martin Glavin

Description:
The objective of this project is to achieve efficient, reliable, real-time image transmission over a Bluetooth wireless network. Bluetooth, being an ad-hoc networking standard, is used in a variet of environments, often under difficult conditions, leading to limited bandwidth and significant Bit Error Rates (BER). Image compression and transmission provisions, that avoid catastrophic failure caused by lost, delayed, or errant packets, are cruscial. This project evaluates the transmission of JPEG images through a high BER Bluetooth wireless channel with varying levels of Forward Error Correction (FEC). FEC makes images more robust to errors, however, it adds significant redundant data to an image file. The Error Resilient Entropy Code is presented as a method of modifying the JPEG compressed images to give increased error resilience while still maintaining a high compression ratio.

Start date: September 2001. This project has completed.

Related projects:
An Efficient Implementation of Bluetooth Wireless Technology for an Automotive Environment

Project Publications:
Conferences:
L. Kelly, A. Molloy, M. Glavin, F. Morgan, " Error Resilient Image Transmission over a Bluetooth Network", 11th IEE Irish Signals and Systems Conference, ISSC 2003, University of Limerick, July 2003.