Connected and Autonomous Vehicles (CAVs) offer a range of future of exciting opportunities, not just as a new form of transport modality but also a potentially transformative one that may change how we work and live, whilst also enhancing safety and supporting more sustainable travel. In particular, CAVs may offer mobility to groups that struggle to access it and become a key part of a mobility-as-a-service ecosystem, either offering the bulk of a journey or else addressing the difficult ‘last mile’ problem connecting public transport modes to homes and workplaces. This, in turn, maybe most powerful in a model of use that approximates taxi use and may also offer shared use to maximise efficiency.
Much research and development to-date has understandably tended to focus upon how the CAV senses and navigates its external environment and how individual vehicles might be organised on a logistical and control basis (e.g. through platooning or swarming). However, to build a trustworthy, attractive, and sustainable mode this project takes a slightly different line in that it considers what could be sensed within the cabin of the vehicle to offer reassurance and safety to passengers (especially in a scenario of shared use) while also protecting the asset itself and ideally generating data that can be used to further enhance future design and service operation.
In order to address this issue, work has been carried out at a range of levels but all broadly from a human factors/socio-technical systems perspective which includes understanding both technical trajectories in development (CAVs, sensors) as well as human needs and concerns (safety, activity while travelling). One approach to modelling such a system considering both technological and human elements is Cognitive Work Analysis, a multi-phase modelling activity that integrates both the technical constraints but also purposive and ethical/values-based constraints in the same analysis.
This method has previously been used for the formative design of new systems, in this work we have extended it to work as a principled method for road mapping future service and technological models based on the idea of identify irreducible features of a mobility service and how they might be allocated to humans, algorithms and physical objects. Other work has included trying to understand what activities are likely to be undertaken on the move and what activities may cause concern based on analysis of existing journey behaviour on public modes, aspirations around this and also an expert description from CCTV operators of behaviours that might cause concern and the kinds of information needed to identify them.
Pilot work has also been undertaken to demonstrate how robust and extremely low-cost sensors embedded in seating can reliably detect the transition between a range of postures, information that can be used to indicate occupancy, comfort/discomfort and even health and engagement. More playfully, but as a demonstration of the interactive possibilities of sensor-rich environments, it was also possible to develop a fun seat-based interactive exercise game.
It is to be noted that surveillance (both in the sense of using camera and in a broader sense of collecting data about people in private and public settings) is a sensitive issue and the work is very much guided by concern for this. In part, this is addressed through exploring the quid pro quo between data donor and data collector where both parties can identify value from the exercise, and a wider argument is also made that understanding the difference between what you can collect and what you should collect should guide these activities from an informed and considered perspective.
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