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PhD studentship sponsored by Nissan: Cognitive models of human traffic interaction for real-time behaviour interpretation


Key facts

Type of research degree
Application deadline
Ongoing deadline
Country eligibility
UK and EU
Source of funding
External organisation
Dr Gustav Markkula and Professor Natasha Merat
Additional supervisors
Dr Matteo Leonetti
Institute for Transport Studies
Research groups/institutes
Human Factors and Safety
<h2 class="heading hide-accessible">Summary</h2>

There is a strong push towards the development of driverless, automated vehicles (AVs). However, to enable full self-driving in complex, urban environments, AVs will need to participate in the subtleties of on-road interactions, appropriately interpreting and responding to the goals and intentions of human road users while at the same time communicating and pursuing the AVs&rsquo; own goals. In the Human Factors &amp; Safety research group at the Institute for Transport Studies, University of Leeds, we are actively addressing this open research challenge in a number of ways, including the development of cognitively plausible mathematical models which quantitatively describe human interactive behaviours in traffic, and application of these models to understand and improve human-AV interactions. This PhD studentship, sponsored by Nissan Motor Manufacturing (UK) Limited, will allow the successful candidate to build further on the cutting edge models from our research group and elsewhere, and to connect it to state of the art methods for real-time AV perception and decision-making. The overarching goal is to implement models and algorithms that can estimate, from processed AV sensor data, what a given human road user perceives the AV&rsquo;s near-term intentions to be. A preliminary list of intended intermediate objectives and activities includes: - Identifying interaction scenarios where an interaction model are likely to be most beneficial to real-time AV algorithms. - Analysing processed sensor data provided by Nissan. - Designing and carrying out controlled virtual reality studies of the targeted interaction scenarios, and analysing the collected data. - Applying, extending, and/or developing mathematical models of road user interactions to the targeted scenarios. - Investigating how to best integrate the mathematical models within the real-time perception and decision-making algorithms of an AV. These plans are flexible and will be agreed in collaboration between the PhD student, supervisors, and sponsor as the project unfolds.

<h2 class="heading hide-accessible">Full description</h2>

<p><strong>Hosting research group and institution</strong></p> <p>The <a href="">Human Factors &amp; Safety research group</a> is internationally renowned as a leader in transportation safety research in general, and in human factors of automated vehicles in particular. We are a vibrant and caring cross-disciplinary group of researchers with backgrounds from engineering, human factors, psychology, computer science, and make use of a variety of methods and tools, not least our <a href="">state of the art virtual reality facilities</a>. <a href="">Dr. Gustav Markkula</a>, the lead supervisor for this studentship, is among the world leaders in mathematical modelling of safety-critical and interactive road user behaviour, and you will also be co-supervised by <a href="">Prof. Natasha Merat</a>, Chair of Human Factors of Transport Systems, and <a href="">Dr. Matteo Leonetti</a> of the School of Computing, with research expertise in real-time algorithms and human-robot interaction.</p> <p>The <a href="">Institute for Transport Studies</a> at the University of Leeds is a leading department for transport teaching and research, ranked <a href="">top 15 globally for Transportation Science and Technology</a> and 2nd in the UK for research power (<a href="">Research Excellence Framework (REF) 2014</a>).</p> <p><strong>Industry sponsor</strong></p> <p>Nissan Motor Manufacturing (UK) Limited (&ldquo;NMUK&rdquo;) has a proven track record of innovation and &pound;4bn investment in the UK over the past 30 years. NMUK&rsquo;s Sunderland plant is the largest car production site in the UK (since 1998), making almost 500,000 cars per year over the past 5 years, representing 1 in 3 of all new cars produced in the UK. Nissan in the UK directly employs 7,000 people and supports total of 40,000 jobs including the supply chain and dealer network. In addition to a standard grant for conference travel, the studentship also includes a generous travel budget for visits to Nissan Japan, to allow close on-site interaction with AV development teams, enabling strong applicability and impact for the research findings.</p>

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="">University&#39;s website</a>. Please state clearly in the research information section&nbsp;that the research degree you wish to be considered for is&nbsp;&ldquo;PhD studentship sponsored by Nissan: Cognitive models of human traffic interaction for real-time behaviour interpretation&quot; as well as&nbsp;<a href=""><font color="#0066cc">Dr. Gustav Markkula</font></a> as your proposed supervisor. This studentship will remain open for applications until position is filled. Please indicate in the application your earliest available PhD start date.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. For this project candidates should have a Masters degree in an engineering discipline, computer science, or in the psychological/cognitive/behavioural sciences, and: Strong quantitative (mathematical) skills and programming skills; Some understanding (or ideally experience) of real-time signal processing; Keen interest (or ideally formal training) in psychology, cognitive science, or behavioural science; Interest in understanding and improving human interaction with automated vehicles; Excellent written and verbal communication skills including presentation skills; Good time management and planning skills; Proven ability to manage competing demands effectively, responsibly and without close support; Proven ability to work well both individually and in a team; Strong commitment to the own continuous professional development. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<h2 class="heading">Funding on offer</h2>

<p>Full UK/EU fees, a maintenance award of &pound;15,009* per year for 3.5 years, and a Research Training Support Grant of &pound;2,250 for other research costs such as conference visits. There is also a budget for visits to Nissan in Japan. (*2019/20 academic year)</p>

<h2 class="heading">Contact details</h2>

<p>Any enquiries about the application procedure can be sent to the Graduate School Office at <a href=""></a>.</p> <p>Enquiries about the research project can be sent to the lead supervisor at <a href=""></a>.</p>

<h3 class="heading heading--sm">Linked research areas</h3>