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Driving simulator studies of human factors and traffic safety using statistical data modelling, AI and machine learning


School of Engineering and the Built Environment (SEBE)

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Prof P Langdon , Dr G Fountas No more applications being accepted Self-Funded PhD Students Only

About the Project

Over the last few years, the need for testing and evaluating new safety countermeasures or novel driver assistance systems, in advance of their implementation or launch, has given rise to advanced simulation methods. The primary advantage of the simuation approaches is that they can capture critical nuances of human factors and user’s behaviour without requiring actual exposure to the tested technology or system.

For this reason, driving simulators are extensively used for education and training of users in almost all modes of surface and air transportation.

This PhD program aims at developing a novel, yet standardized analysis framework of behavioural response, which can be promptly adjusted for the evaluation of systems or technologies targeted at enhancing transportation safety. This framework is expected to include the following components:

• Design and programming of appropriate, context-driven scenarios

• Identification of cognitive functions interacting with the system/technology in question

• Induction of external or internal stimuli that may interact with user’s cognitive state and critical

features of the tested system/technology

• Collection of highly disaggregate data on perceived and observed response of the user

• Extraction of inferences by integration of advanced statistical modeling and artificial intelligence techniques for the analysis of the simulation data

For the development and validation of the framework, various case studies in transportation safety can be investigated including (but not limited to): testing of various human-machine interface configurations for Automated Vehicles (AV) and Urban Air Mobility (UAM), emergency takeover of control in automated vehicles, effectiveness of Advanced Driver Assistance Systems (ADAS) in conflicts between motorized and non-motorized users.

To carry out this research program, the newly obtained driving simulator of the Transport Research Institute at Edinburgh Napier University will be leveraged. Apart from the development of the framework, the successful candidate is expected to actively contribute in programming the simulator and carrying out the experiments. The program is planned to include a number of interlocking PhDs and ongoing research projects creating a supportive environment for research.

Academic qualifications

A first degree (at least a 2.1) ideally in computer science/data science/applied psychology/transportation, electrical or industrial engineering or any relevant academic discipline with a good fundamental knowledge of transportation safety and its links with human factors.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online. Essential attributes: · Experience of fundamental knowledge in transportation safety modelling and simulation techniques · Competent in computer programming · Knowledge of artificial intelligence and/or statistical and econometric methods for data analysis · Good written and oral communication skills · Strong motivation, with evidence of independent research skills relevant to the project · Good time management Desirable attributes: Previous experience in designing and conducting driving simulation experiments Previous experience in using Artificial Intelligence methods for transport safety analysis Familiarity with Python, C++, Matlab, R Familiarity with STISIM simulation system Familiarity with CAD programming for architectural or industrial engineering


References

Politis, I., Langdon, P., Adebayo, D., Bradley, M., Clarkson, P.J., Skrypchuk, L., Mouzakitis, A., Eriksson, A., Brown, J.W., Revell, K. and Stanton, N., 2018, March. An evaluation of inclusive dialogue-based interfaces for the takeover of control in autonomous cars. In 23rd International Conference on Intelligent User Interfaces (pp. 601-606). Revell, K.M., Richardson, J., Langdon, P., Bradley, M., Politis, I., Thompson, S., Skrypchuck, L., O'Donoghue, J., Mouzakitis, A. and Stanton, N.A., 2020. Breaking the cycle of frustration: Applying Neisser's Perceptual Cycle Model to drivers of semi-autonomous vehicles. Applied ergonomics, 85, p.103037. Fountas, G., Pantangi, S.S., Hulme, K.F. and Anastasopoulos, P.C., 2019. The effects of driver fatigue, gender, and distracted driving on perceived and observed aggressive driving behavior: A correlated grouped random parameters bivariate probit approach. Analytic methods in accident research, 22, p.100091. Boyle, L.N. and Lee, J.D., 2010. Using driving simulators to assess driving safety. Accident; analysis and prevention, 42(3), p.785.
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