Human Interaction with Low-Speed Autonomous Vehicles

About the Project

The University of York is embarking on ground-breaking research that focuses on data-centric engineering, digital twins, and AI, revolutionizing the way systems are designed and optimized through data. As part of this transformative initiative, we are inviting applications for Ph.D. degrees in the field of low-speed autonomous vehicles (LSAVs) and human interaction, with the goal of creating pedestrian-safe and human-friendly postures in dense and assistive environments.

The research will delve into the intricate dynamics of human behaviour, which can often be unpredictable and irrational, and explore how it can be modelled and integrated into LSAVs. By enhancing the cognitive abilities of LSAVs, including road and sidewalk walkers, delivery robots, and tug/luggage robots, we aim to cultivate a pedestrian-safe and human-friendly environment in congested areas. This endeavour will effectively transform LSAVs into autonomous vehicles that are attuned to human needs and interactions.

Currently, a significant challenge to the widespread acceptance of autonomous vehicles is the lack of trust. The public demands absolute safety before entrusting the well-being of their family members to fully autonomous vehicles. This concern extends to LSAVs, which are being tested and deployed as first/last mile transport shuttles/pods in urban centres, sidewalk delivery robots on campuses and streets, and tug/luggage robots in airports, railway stations, and hotels. Unlike high-speed autonomous vehicles that primarily interact with their drivers, LSAVs are constantly engaged with pedestrians, cyclists, segway scooters, and hoverboards. While LSAVs possess basic functionalities like collision-avoidance, collaborative area-building, and pattern recognition, they are far from attaining the cognitive capabilities required to mimic human behaviour, accounting for unpredictability and irrationality, and adhere to social, moral, and legal norms.

This research project aims to quantify, classify, and model a comprehensive range of human behaviours within the intended operating environment of LSAVs, considering various pedestrian contexts, including challenging edge cases. It will involve the development of machine learning algorithms to differentiate and identify different patterns of human behaviour, as well as methods to verify the accuracy and efficacy of such algorithms.

At the University of York, we take immense pride in our position among the top ten UK universities in the REF, a testament to our dedication to research excellence and its societal impact. Our vision aligns with being a University for the Public Good, fostering strong partnerships to expand and disseminate knowledge for the benefit of local and global communities. This research project perfectly embodies our principles of inclusion, internationalism, and collaboration, as it holds the potential to make a profound impact on the future of Autonomous Vehicles.

Entry requirements:

Candidates should have (or expect to obtain) a minimum of a UK upper second class honours degree (2.1) or equivalent in Engineering, Physics, Computer Science, Mathematics or a closely-related subject.

How to apply:

Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process.

Funding Notes

This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the School of Physics, Engineering and Technology website for details about funding opportunities at York. View Website