- UK/EU/International: Worldwide (International, UK and EU)
- Value: This project is open to self-financing students and may be eligible for funding through University or external research bodies. Browse through our funding schemes listings to find a suitable scholarship for this project.
- Deadline: Applications accepted all year round
Contact Dr Gustav Markkula to discuss this project further informally.
As vehicle systems for automation and driver support become increasingly complex, so do also their verification and evaluation. To ensure that vehicle automation features are robust and error-free requires test driving over such large distances that it becomes impossible to carry it all out in physical reality for every hardware or software update. Likewise, to estimate the actual impact of in-development systems on very rare externally arising safety-critical situations is also more or less infeasible without taking recourse to virtual methods. This project will aim to improve methods for virtual testing of safety support and automation functions, with an emphasis on (1) completely computational, model-in-the-loop testing, with (2) faithful representations of human driver behaviour, when applicable.
The first step of the project will be to agree the exact scope in terms of addressed systems and traffic scenarios. This will be determined with up-to-date input from vehicle industry, to which the research group has strong ties. Based on the agreed scope, industry co-supervision might be possible. The researched methods for virtual testing could include so called "what-if" simulations based on naturalistic crash data, and/or large-scale Monte Carlo simulations. Furthermore, situations of varying criticality should ideally be addressed, as well as situations arising both externally from surrounding traffic or internally from e.g. functional failure.
A specific current strength of the research group in this domain is the understanding and modelling of human behaviour in safety critical situations, and this should be leveraged and ideally developed further, if appropriate supported by additional data collection with human participants.
The suitable candidate will have a background in Engineering or similar quantitative discipline, with an interest in and prior working knowledge of at least some of the following: programming (esp. MATLAB), data analysis, signal processing, control theory, vehicle dynamics, complex systems, human behaviour and psychology, neuroscience.
Markkula, G. (2015). Driver behavior models for evaluating automotive active safety: From neural dynamics to vehicle dynamics. PhD thesis, Chalmers University of Technology.
Markkula, G. Engström, J., Lodin, J., Bärgman, J., Victor, T. (2016). A farewell to brake reaction time? Kinematic-dependent brake response in naturalistic rear-end emergencies. Accident Analysis & Prevention, 95A, 209-226.
Zhao, D., Huang, X., Peng, H., Lam, H., LeBlanc, D. J. (2016). Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers. Submitted for publication. arXiv:1607.02687
Applications are invited from candidates with or expecting a minimum of a UK upper second class honours degree (2:1), and/or a Master's degree in the relevant subject area.
If English is not your first language, you must provide evidence that you meet the University’s minimum English Language requirements.
How to apply
Formal applications for research degree study should be made online through the university's website. Please state clearly in the research information section that the PhD you wish to be considered for is the ‘Driver-oriented virtual testing of vehicle safety and automation' as well as Dr Gustav Markkula as your proposed supervisor.
We welcome scholarship 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.
If you require any further information please contact the Graduate School Office e: email@example.com, t: +44 (0)113 34 35326.