Publications

Title and AuthorsDescription / AbstractPublication / Link
2011
Cloud Computing Concept for Intelligent Transportation Systems

by:
Pawel Jaworski, Tim Edwards, Jonathan Moore, Keith Burnham
Proposal of a cloud computing based urban traffic control system, aiming to increase road throughput and optimise traffic control for increased safety and reduced fuel emissionsProceedings of 14th International IEEE Conference on Intelligent Transportation Systems

doi: 10.1109/ITSC.2011.6083087
2012
Microscopic Traffic Simulation Tool for Intelligent Transportation Systems

by:
Pawel Jaworski, Tim Edwards, Keith Burnham, Olivier Haas
Describes a microscopic traffic simulation tool for assessing the performance of intelligent transportation system traffic and vehicle control systemsProceedings of the 15th International IEEE Conference on Intelligent Transportation Systems

doi: 10.1109/ITSC.2012.6338659
2015
Autonomous Vehicle Security

by:
Madeline Cheah, Siraj Shaikh
Examination of possible threats to autonomous vehicles, and a review of the state-of-the-art controls and countermeasuresIET Engineering & Technology Reference 1(1)

doi: 10.1049/etr.2014.0056
2016
Combining third party components securely in automotive systems

by:
Madeline Cheah, Siraj Shaikh, Jeremy Bryans, Hoang Nga Nguyen
Introduces a methodology for users to introduce or strengthen security of composed systems without requiring full knowledge of commercially sensitive sub-componentsProceedings of 10th IFIP International Conference on Information Security Theory and Practice

doi: 10.1007/978-3-319-45931-8_18
2016
Do we really know which vehicle attributes are important for customers?

by:
Milena Kukova, Cyriel Diels, Patrick Jordan, Maria Franco-Jorge, Jamie Anderson, Husni Kharouf
Reports preliminary findings from two studies designed to better understand vehicle attributes and how they influence customer purchase decisions and satisfactionProceedings of the 10th International Conference on Design and Emotion

doi: 10.5281/zenodo.2635727
2017
Towards a Testbed for Automotive Cybersecurity

by:
Daniel Fowler, Madeline Cheah, Siraj Shaikh, Jeremy Bryans
Proposes a testbed built over a Controller Area Network simulatorProceedings of 10th IEEE International Conference on Software Testing, Verification and Validation: Industry Track

doi: 10.1109/ICST.2017.62
2017
Threat Intelligence for Bluetooth-enabled systems with automotive applications: An empirical study

by:
Madeline Cheah, Jeremy Bryans, Daniel Fowler, Siraj Shaikh
This paper presents a survey of vehicles and vehicular aftermarket devices with Bluetooth connectivty from a threat intelligence perspective, to gain insight into conditions during real-world drivingProceedings of the 47th IEEE International Conference on Dependable Systems and Networks Workshops

doi: 10.1109/DSN-W.2017.22
2017
Towards a systematic security evaluation of the automotive Bluetooth interface

by:
Madeline Cheah, Siraj Shaikh, Olivier Haas, Alastair Ruddle
Presents a framework for systematic method of security testing for automotive Bluetooth interfaces, with implementation of a proof-of-concept tool to carry out the testingJournal of Vehicular Communications 9(July)

doi: 10.1016/j.vehcom.2017.02.008
2017
Designing for comfort in shared and automated vehicles (SAV): a conceptual framework

by:
Cyriel Diels, Tugra Erol, Milena Kukova, Joscha Wasser, Maciej Cieslak, William Payre, Abhijai Miglani, Neil Mansfield, S.G. Hodder, Jelte Bos
Discusses major comfort factors in the context of SAV and highlight both similarities and differences between transport modesProceedings of 1st International Comfort Congress

https://dspace.lboro.ac.uk/2134/25572
2017
Formalising Systematic Security Evaluations using Attack Trees for Automotive Applications

by:
Madeline Cheah, Hoang Nga Nguyen, Jeremy Bryans, Siraj Shaikh
Presents a method for systematically generating tests based on formalised attack treesProceedings of 11th IFIP International Conference on Information Security Theory and Practice

doi: 10.1007/978-3-319-93524-9_7
2017
Driverless Pods: From Technology Demonstrators to Desirable Mobility Solutions

by:
Cyriel Diels, Joscha Wasser, Anthony Baxendale, Michael Tovey
Introduces a conceptual comfort framework for the design of last mile mobility solutions, with analysis and comparison of current concepts in the context of passenger comfort experienceProceedings of 8th International Conference on Applied Human Factors and Ergonomics

doi: 10.1007/978-3-319-60441-1_53
2018
Building an automotive security assurance case using systematic security evaluations

by:
Madeline Cheah, Siraj Shaikh, Jeremy Bryans, Paul Wooderson
Builds on earlier work by using systematic security evaluations to enumerate undesirable behaviours, enabling the assignment of severity ratings in a (semi-) automated mannerComputers & Security 77(August)

doi: 10.1016/j.cose.2018.04.008
2018
Fuzz Testing for Automotive Cybersecurity

by:
Daniel Fowler, Jeremy Bryans, Siraj Shaikh, Paul Wooderson
This paper demonstrates how fuzz testing has a part to play as one of many security tests that a vehicle’s systems could undergo before being made ready for series production.Proceedings of 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops

doi: 10.1109/DSN-W.2018.00070
2018
Development and Verification of a Distributed Electro-Thermal Li-Ion Cell Model

by:
Richard Stocker, Neophytos Lophitis, Asim Mumtaz
Presents an ID distributed electro-thermal Li-ion model that gives accurate representations of cell thermal and electrical characteristics in response to current applicationProceedings of the 44th Annual Conference of the IEEE Industrial Electronics Society

doi: 10.1109/IECON.2018.8591633
2019
Human-Agent Teaming – An Evolving Interaction Paradigm: An Innovative Measure of Trust

by:
Samson Palmer, Dale Richards, Graham Shelton-Rayner, David Inch, Kurtulus Izzetoglu
Presents findings from an experiment that examines the human-autonomy interaction across different frameworks of authority (from manual to fully autonomous)Proceedings of the 20th International Symposium on Aviation Psychology

doi: 10.4233/uuid:c8e9b17b-89ea-4faf-b55c-767c1ae070ef
2019
Enabling Security Checking of Automotive ECUs with CSP models

by:
John Heneghan, Siraj Shaikh, Jeremy Bryans, Madeline Cheah, Paul Wooderson
This paper presents an approach, using CSP, to support systematic security testing of ECU components, with a case study using the mechanisms that enable Over-The-Air software updatesProceedings of 49th IEEE International Conference on Dependable Systems and Networks Workshops – Safety and Security of Intelligent Vehicles

In press
2019
A Method for Constructing Automotive Cybersecurity Tests, A CAN Fuzz Testing Example

by:
Daniel Fowler, Jeremy Bryans, Madeline Cheah, Siraj Shaikh, Paul Wooderson
Production of a method to construct useful automotive security tooling and tests, in this case a Controller Area Network fuzz testing prototypeProceedings of 2019 IEEE International Workshop on Automobile Software, Security and Safety

In press
2019
Accurate ride comfort estimation combining accelerometer measurements, anthropometric data and neural networks

by:
Maciej Cieslak, Stratis Kanarachos, Mike Blundell, Cyriel Diels, Mark Burnett, Anthony Baxendale
Explores the use of neural network for accurate estimations of ride comfort, by combining anthropometric data and acceleration measurementsNeural Computing and Applications

In press
2019
Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification

by:
Felix Batsch, Alireza Daneshkhah, Madeline Cheah, Stratis Kanarachos, Anthony Baxendale
Proposes an approach to identify the performance boundary (where corner cases are located) using Gaussian Process Classification. This is demonstrated on an exemplary traffic jam approach scenarioProceedings of the 2019 IEEE Intelligent Transportation Systems Conference Workshop

In press
2019
Applicability of Modern Correlation Tools for Ride Comfort Evaluation and Estimation

by:
Maciej Cieslak, Stratis Kanarachos, Mike Blundell, Cyriel Diels, Anthony Baxendale
A study analysing the usability of modern correlation tools, such as artificial neural networks, for objective and subject data correlation, evaluation and explores the possibility of prediction of subjective responses based on measured dataProceedings of the 2nd International Comfort Congress

In press
2019
DigiCAV project: Exploring a Test-Driven Approach in the Development of Connected and Autonomous Vehicles

by:
Ioannis Kyriakopoulos, Pawel Jaworski, Stratis Kanarachos
A concept paper introducing the Digital CAV Porving Ground project and disseminates results from the first deliverable. The aim of this project is to explore the feasibility of a simulation platform, enabling a test-driven development approach for CAV. Proceedings of the 2019 IEEE International Conference on Connected Vehicles and Expo

In press
2020
Generation of Pedestrian Crossing Scenarios Using Ped-Cross Generative Adversarial Network

by:
James Spooner, Vasile Palade, Madeline Cheah, Stratis Kanarachos, Alireza Daneshkhah
The safety of vulnerable road users is of paramount importance as transport moves towards fully automated driving. The richness of real-world data required for testing autonomous vehicles is limited and furthermore, available data do not present a fair representation of different scenarios and rare events. Before deploying autonomous vehicles publicly, their abilities must reach a safety threshold, not least with regards to vulnerable road users, such as pedestrians. In this paper, we present a novel Generative Adversarial Networks named the Ped-Cross GAN. Ped-Cross GAN is able to generate crossing sequences of pedestrians in the form of human pose sequences. The Ped-Cross GAN is trained with the Pedestrian Scenario dataset. The novel Pedestrian Scenario dataset, derived from existing datasets, enables training on richer pedestrian scenarios. We demonstrate an example of its use through training and testing the Ped-Cross GAN. The results show that the Ped-Cross GAN is able to generate new crossing scenarios that are of the same distribution from those contained in the Pedestrian Scenario dataset. Having a method with these capabilities is important for the future of transport, as it will allow for the adequate testing of Connected and Autonomous Vehicles on how they correctly perceive the intention of pedestrians crossing the street, ultimately leading to fewer pedestrian casualties on our roadshttps://www.mdpi.com/2076-3417/11/2/471
2020
Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes

by:
Felix Batsch, Alireza Daneshkhah, Vasile Palade, Madeline Cheah
Assuring the safety of automated vehicles is essential for their timely introduction and acceptance by policymakers and the public. To assess their safe design and robust decision making in response to all possible scenarios, new methods that use a scenario-based testing approach are needed, as testing on public roads in normal traffic would require driving millions of kilometres. We make use of the scenario-based testing approach and propose a method to model simulated scenarios using Gaussian Process based models to predict untested scenario outcomes. This enables us to efficiently determine the performance boundary, where the safe and unsafe scenarios can be evidently distinguished from each other. We present an iterative method that optimises the parameter space of a logical scenario towards the most critical scenarios on this performance boundary. Additionally, we conduct a novel probabilistic sensitivity analysis by efficiently computing several variance-based sensitivity indices using the Gaussian Process models and evaluate the relative importance of the scenario input parameters on the scenario outcome. We critically evaluate and investigate the usefulness of the proposed Gaussian Process based approach as a very efficient surrogate model, which can model the logical scenarios effectively in the presence of uncertainty. The proposed approach is applied on an exemplary logical scenario and shows viability in finding concrete critical scenarios. The reported results, derived from the proposed approach, could pave the way to more efficient testing of automated vehicles and instruct further physical tests on the determined critical scenarios.https://www.mdpi.com/2076-3417/11/2/775