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Intelligent Vehicles Projects

WMG is working on a number of projects within the area of Intelligent Mobility which are contributing to the understanding of the subject of the technologies and their gradual adoption.

Innovative Testing of Autonomous Control Techniques (INTACT)

Partner: RDM

Urban mobility vehicles, or driverless Pods, will reduce congestion and accidents on our roads and give more people travel independence. However, they require trust from users – they must be safe, secure and robust. This requires extensive testing and validation of the Autonomous Control System, or ACS, which is the brains of the Pod responsible for detecting objects and controlling the vehicle. Reducing the cost and optimising this ACS is essential in facilitating the large scale manufacture and sale of commercially viable Pods in the near term. However testing on public roads and in real-world driving situations would be very expensive, unrepeatable and potentially dangerous. Hence this project proposes the use of a novel simulator concept, to enable the evaluation of an optimised ACS in a safe, repeatable and scientifically rigorous environment. RDM, the UK’s only designer and manufacturer of driverless Pods, and University of Warwick will work together to enable the broader uptake of Pods, help inform the legislative framework for the UK and eventual certification of autonomous vehicles, and show the UK as a leader of research into autonomous vehicles.

Research for Advanced Concept Development: of smart and autonomous vehicles (RACeD)

Partner: Jaguar Land Rover

The project is working on advanced concept development for connected and autonomous vehicles in four strategic areas: self learning car, connected car, next generation HMI and software development. These strategic areas are closely aligned with JLR s mega research projects and will undercut the introduction of future autonomous technology to JLR.

The RACeD program with Jaguar Land Rover is a group of five Engineering Doctorates undertaking research on a collection of specific JLR challenges and which will collectively advance JLR’s pathway to building up autonomous technology capability. The individual projects are: On-Board and Off-Board data platforms, Advancing Intelligent Feature Development, Exploring the user experience of autonomous vehicles, Evaluating the self-learning car in the simulated and real-world.


UK Connected Intelligent Transport Environment (UKCITE)

Partners: Visteon Engineering Services Limited, Jaguar Land Rover, Coventry City Council, Coventry University, Highways England Company Ltd, HORIBA MIRA, Huawei Technologies (UK) Co Ltd, Siemens, Vodafone Group Services Ltd and WMG at University of Warwick.

The UKCITE project will create one of the worlds most advanced environments for testing connected and autonomous vehicles. It involves equipping over 40 miles of urban roads, dual-carriageways and motorways with combinations of three ‘talking car technologies’, and testing for a fourth, known as LTE-V. The project will establish how these technologies can improve journeys, reduce traffic congestion, and provide entertainment and safety services through better connectivity.

It will enable automotive, infrastructure and service companies to trial connected vehicle technology, infrastructure and services in real-life conditions on 40 miles of roads within Coventry and Warwickshire. The project will establish how technology can improve journeys, reduce traffic congestion and provide in-vehicle entertainment and safety services through better connectivity.

WMG are leading two major packages of work on the UKCITE projects. The first relating to the cyber-security challenges of the infrastructure implementation, and the second looking at new and evolving business models that could emerge from the new technology.


Doctoral Projects - PhD and EngD

Robert Courtney (EngD candidate)
Supervisors: Prof. Paul Jennings, Dr. Harita Joshi
Project: Advancing Intelligent Feature Development
The current push for automation and intelligent self learning features in the automotive industry has created new complex research challenges. Tackling these challenges requires research in to both the technical solutions and the user experience of these new features. In order to answer these research questions trials involving human drivers must eventually be carried out. Performing these trials on real roads often introduces issues regarding repeatability and risk. Therefore it is often preferable, at least initially, to run trials in a driving simulator. WMG’s 3xD simulator is an excellent tool for mimicking human senses and already has the ability to simulate communication signals such as GPS and LTE. It is the aim of this project to expand the capabilities of the 3xD simulator so that it can simulate signals to fool key sensors used by new vehicles to perceive the outside world. This would expand the number of features that could be safely tested in the repeatable environment of the 3xD simulator.

Researcher: Anna Gaszczak (EngD candidate)
Supervisor: Professor Paul Jennings
Project: Intelligent sensing car: driver monitoring using ComputerVision
Although a variety of problems in the automotive domain have been tackled using vision systems such as for pedestrian detection, vehicle identification and target tracking, a comprehensive understanding of the environment using visual information remains an open challenge. The aim of the proposed research is to enable the vehicle to understand the context of the environment, identifying possible hazards and obstacles, so that the vehicle dynamics / behaviour can be adjusted to improve the driver's comfort and safety.

Researcher: Robert James (EngD candidate)
Project: On-Board and Off-Board data platforms
In the automotive industry, there is a strong trend toward an increased data output from the Electronic Control Units (ECU) in order to enable more complex features. The challenges that have arisen include: the bandwidth and topology constraints of the In-vehicle Network (IVN); the bandwidth and latency of wireless communication platforms (2G/3G/4G LTE) and the inability to effectively store, process and query the available information wherever it is needed. In order to make the most effective use of this data, this project aims to research techniques that can assist with data transfer and management between the On-Board and Off-Board systems.

Researcher: Siddartha Khastgir (PhD Candidate)
Project: Development of a Drive-in Driver-in-the-Loop fully immersive driving simulator for virtual validation of automotive systems
The work utilises a 3xD Simulator platform to fulfil a gap in the validation methodology of autonomous features at various levels in the automotive environment. Lack of standard processes and legislation implies a challenge in the commercialisation of these systems in the context of customer acceptance levels. Development of autonomous features will depend on the ability to perform tests in a safe and reproducible manner and defining a methodology for this. This research aims to establish standards for autonomous features by developing scenarios which are reproducible in a test platform.

Researcher: Alexandros Mouzakitis (EngD candidate)
Supervisors: Professor Paul Jennings, Gunwant Dhadyalla
Project: A pragmatic model-based product engineering framework
The importance of embedded software systems in the automotive industry is increasingly dramatic. Automotive companies must create robust embedded software while managing the pressures of increased system complexity and reduced time to market. This research project investigates why automotive product development processes are lacking early error fault finding and use of automated testing throughout the vehicle systems development cycle. The project methodology will explore the use of model-based principles and test automation techniques.

Researcher: Brahmadevan Padmarajan (PhD candidate)
Supervisors: Professor Paul Jennings, Dr Andrew McGordon
Project: Plug-in Hybrid Electric Vehicle Energy Management for Real World Driving
Learn to design an anticipative rule based Plug-in Hybrid Vehicle (PHEV) Energy Management System (EMS) that can adapt to uncertainties of real world driving in real time. Research has shown anticipative EMS enhances hybrid vehicle performance such as fuel economy, emission and component life. However current rule based EMS used in production vehicles are non-anticipative. Existing anticipative (or predictive) EMS requires knowing the exact trip demand in advance and is computationally demanding. A full parallel PHEV model is simulated to compare the proposed EMS performance against conventional rule based EMS for various real world scenarios.

Researcher: Aimee Williams (PhD candidate)
Supervisors: Professor Paul Jennings, Dr Andrew McGordon
Project: The comparison of the fit of models to data and how this might be used to gain better understanding or categorisation of driver behaviour with respect to fuel economy
This project will develop formal methods to compare models, yielding insights by characterising the degree that the assumptions and principles utilised within the models captures the behaviour observed for the influence of driving behaviour on fuel economy. It will set out formal methods to aid in understanding the effect of different causes, classifying the degree of importance of the behaviour in relation to the effects, and the frequency with which they occur in correlation with each other for different driver behaviour and the subsequent results on fuel economy.