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Loughborough Research Initiative into the development of Autonomous Vehicle Systems – Six PhD Studentships
The increasing automation of vehicle systems is enabling many new opportunities to improve transport safety, mobility and transport efficiency. Autonomous systems enable new functionalities to become available whether on land or in the air. With vehicle or infrastructure based sensing systems and distributed computing autonomous systems are already making an impact on transport safety, efficiency and mobility. While the eventual objective is a fully autonomous transport system there are many stages of increasing automation that will take place before that is reached. Many challenges concerning sensing, communication and control systems exist but it is often said the greatest barrier to adoption will relate to human factors and legal requirements.
Loughborough University has launched a new cross-disciplinary research initiative to bring together all of the key skills and approaches that are needed to support future research studies in the field of autonomous vehicle systems. Six opportunities for PhD study are available based in four different schools within the University. Although based in separate Schools the PhD students will work as a virtual team and there will be regular liaison meetings to improve communication and promote synergies.
Students must have a first or upper second class academic qualification in a relevant subject or a relevant Masters qualification.
Applications are now invited for each of these studentships which are described in detail below. Please contact the relevant Staff member identified against each of the research topics for further information. Details about the application process can be found here: http://www.lboro.ac.uk/study/apply/research/.
Deadline for applications: 6 May 2013
1. PhD- Development of accident risk model for autonomous land vehicles Automobile automation includes information provision, navigation support, cruise control, emergency braking, automated stopping, automated steering, automated parking and coordinated highway driving (platoons). One of the major functionalities of autonomous vehicles is to fully avoid traffic collisions. However, imprecise data on positions and velocities of surrounding traffic can compromise safety of autonomous vehicles as well as other vehicles. Furthermore, possible sensor or actuator failures and ambiguous motion planning (due to imperfect measurements under the conditions of uncertainty) may also pose a potential threat to the safe operation of autonomous vehicles. Traditional accident prediction models are normally segment- or area-based that largely ignore vehicle-based factors and interactions with other vehicles and/or objects (both static and dynamic). The development of a new accident model will therefore be challenging as internal factors (e.g. sensor failures, imprecise measurements, other conditions of uncertainty in motion planning) need to be integrated with external factors such as current and projected positions and velocities of other participating road users, level of traffic flow, traffic mix and density as well as road geometry (slope/gradient and curvatures) in all built environments (e.g. metropolitan, urban, suburban, rural, dual carriageway, single carriageway, junctions, roundabouts) and weather conditions. In this PhD project, new accident risk models will be developed by integrating internal factors (e.g. sensor failures, imprecise measurements, other conditions of uncertainty in motion planning) with external factors (e.g. current and projected positions and velocities of other participating road users, level of traffic flow, traffic mix and density as well as road geometry) in all built environments (e.g. metropolitan, urban, suburban, rural, dual carriageway, single carriageway, junctions, roundabouts) and weather conditions.
School/Department: Civil and Building Engineering Engineering
Contact: Mohammed Quddus [log in to unmask] 01509
Full details: http://www.jobs.ac.uk/job/AGD191/phd-studentship/
2.PhD - Hazard analysis for Improved Unmanned Autonomous Vehicles (UV) Situational Awareness An autonomous system is fundamentally different to a process plant or many other systems as it does not operate in a single environment but can move around in, and between, different environments, carrying out its assigned tasks and at the same time be prepared to respond to unexpected events or changes. For successful operation in this changing environment, two challenges exist: one is how to identify hazards in the operational environment and the other is to identify when the UV itself becomes a hazard to the environment (e.g. the UV loses communication with its operator, or performance degradation of on-board sensors). It is expected that the exploratory and knowledge-based approach of HAZOP will provide a sound basis for creating a novel hazard identification framework for such autonomous systems. Hazard analysis includes constructing suitable representations for different operational scenarios, and producing dynamic models that characterise the behaviour of UVs within an environment, the operation constraints dictated by regulatory requirements, and algorithms for generating hazardous scenarios or situations that present operational difficulties. Tools such as accident risk models and failure modes, effects and critical analysis will be used to build up a library for hazard analysis (e.g. in the UML format), which will be linked with real-time situational awareness factors. The project will propose and evaluate a systematic framework that addresses these UV challenges, and develop tools for automating the hazard analysis process.
School/Department: Aeronautical and Automotive Engineering
Contact: Dr Lisa Jackson, [log in to unmask] 01509 227276
Full Details: http://www.jobs.ac.uk/job/AGI314/fully-funded-phd-studentship/
3. Geo-spatial mapping using Unmanned Aircraft Systems (UAS) This project will focus on the use of UAS for the collection of geospatial data and their use in physical geography research. We encourage potential students to approach us with project ideas in any area of physical geography, or who wish to work on technical aspects of UAS development in the context of geospatial imaging/mapping. Potential projects might lie in the area of glaciology/glacial geomorphology (e.g. the distribution dust on glacier surfaces and the effect on albedo/ablation), ecology (e.g. monitoring of forest health in the light of ash dieback), fluvial geomorphology (e.g. reach-scale sediment budgeting), or other areas in which Loughborough has expertise (see http://www.lboro.ac.uk/geography/research/).The project will utilise the Department’s autonomous Unmanned Aircraft System (UAS). This is a GPS-controlled fixed wing surveying platform with integrated camera system, developed in association with colleagues in Aeronautical and Civil Engineering. Data processing will utilise the Department’s high-performance PC (equipped with dual Xeon E5 8-core processors, each with 64Gb memory).They will also benefit from close collaboration with colleagues in the ‘Earth and Planetary Observation and Monitoring’ team at the British Geological Survey (including access to their rotary wing UAS).
School/Department: Geography
Contact: Dr David Graham ([log in to unmask]) 01509 222763
Full details: http://www.jobs.ac.uk/job/AGI306/fully-funded-phd-studentship/
4. Relationship between accident behaviour and naturalistic driving studies New safety technologies are rapidly entering the vehicle fleet, based on autonomous systems there is a considerable expectation that they will make a substantial contribution to casualty reduction. Systems such as stability control, autonomous emergency braking and lane keeping support address specific behaviours and traffic conditions and are expected to save lives. However although the driving situations addressed by these technologies do occur on the road the link between normal driving behaviour and accident causation factors has not been established. A quantified knowledge of this relationship is essential if future safety technologies are able to fulfil their expectations. The objectives of this PhD are to integrate the results from detailed analyses of crashes with normal driving data gathered using instrumented vehicles to improve the understanding of the link between pre-crash and normal driving behaviour and to identify key driving situations to be addressed by future technologies. The PhD will be of interest to students with a background in safety, behaviour studies, accident analysis, vehicle engineering, systems engineering or other similar background.
School/Department: Transport Safety Research Centre, Loughborough Design School
Contact: Prof. Pete Thomas, [log in to unmask] 01509 226931
Full details: http://www.jobs.ac.uk/job/AGI302/fully-funded-phd-studentship/
5. PhD - Railway vehicle automation requirements for ‘driver-in-the-loop’ and fully autonomous operation on ATO network systems
The Rail Technical Strategy (RTS) document has outlined a vision for the transformation of the railways in the UK over the next 30 years. A major part of this vision is increasing capacity: a key enabler for which will be Automatic Train Operation (ATO). ATO refers to automatic control and scheduling of services, which can still mean a ‘driver-in-the-loop’. The academic response strategy document to the RTS will refer to the longer term concept of the transition from driver commanded vehicles to fully autonomous ones. In this context this project will look at the contrasting requirements for keeping a ‘driver-in-the-loop’ and those of fully autonomous running for different rail systems (light rail to very high speed lines) inside ATO (both in the technology required and the human systems that need development), drawing on research from other domains where similar issues have been tackled. Consideration will be given to developing a training system for drivers/train managers to enable them to combine, reason with and interpret data from multiple sources, grasping higher cognitive skills, such as remote situational awareness of the rail network in which they operate and high consequence decision-making under extreme uncertainty, including issues such as compliance with appropriate guidelines, legislation and control hand over. This will incorporate prior work on the modelling of decision making systems (in this situation, modelling the driver(s) and vehicle systems as a single decision making system, a combination of human and autonomous agents).
School/Department: Electronic, Electrical and Systems Engineering
Contact: Dr Chris Ward [log in to unmask] (Via the Apply button below) 01509 227026 or Dr Ella-Mae Hubbard [log in to unmask] 01509 227092
Full details: http://www.jobs.ac.uk/job/AGI312/fully-funded-phd-studentship/
6. Field operational trial of Autonomous Emergency Braking Systems Autonomous Emergency Braking systems detect hazards in the road and automatically brake the car if the driver takes no action. The system typically uses a combination of sensors and on-board processing to determine the response of the vehicle to changes in the traffic environment. Vehicles with these systems are becoming available to the public and experimentally the systems appear to operate successfully. However use of the systems on the road has not been evaluated and there is a need to assess the complete real-world experience and the safety benefits.
The objectives of this PhD are to conduct an experimental study with vehicles equipped with AEBS and evaluate the safety benefits together with any changes in driving behaviour.The PhD will be of interest to students with a background in behaviour studies, vehicle performance and evaluations, safety systems design or other similar backgrounds.
School/Department: Transport Safety Research Centre, Loughborough Design School
Contact: Prof. Pete Thomas, [log in to unmask] 01509 226931
Full details: http://www.jobs.ac.uk/job/AGI307/fully-funded-phd-studentship/
Kind Regards,
Dr Mohammed Quddus
Senior Lecturer
Transport Studies Group
School of Civil and Building Engineering, Loughborough University, Leicestershire, UK
Telephone: +44 (0)1509 228545;
Associate Editor: Transportation Research Part C: Emerging Technologies; Associate Editor: Journal of Intelligent Transportation Systems
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