Is it possible to shift public opinion on automated cars?

Lessons from DeepSafe

DeepSafe aimed to advance the commercialisation and deployment of self-driving vehicles by bringing to market ‘sensor real’ edge case data – a simulation of what an actual sensor would detect – together with AV training tools for industry, and innovative behavioural research to understand how to develop public buy-in and interest in self-driving technologies. 

Working with the DeepSafe consortium, DG Cities led public and industry engagement, undertaking research that can be used to inform developers, policy makers, and civil society to support the design and development of policies and approaches that support the safe roll-out of automated vehicle technologies . Our work included:

  • Engaging with emergency services to gather and validate edge-cases that can be used to simulate and train self-driving AI. 

  • Exploring how Automated Driver Assistance Systems are marketed to consumers and the ways in which vehicle manufacturers explain automated technologies to consumers.

  • Assessing online discourse on automated vehicle incidents to understand how the public engages with automated vehicles online.

  • Undertaking online experiments to explore public attitudes and to test different approaches to driving interest and acceptance of automated vehicles. 

Safety first? Understanding acceptance of automated vehicles

This public engagement research takes a closer look at public attitudes towards automated vehicles and expectations regarding their safety. We use approaches from behavioural economics to assess the impact of safety messaging on increasing acceptance.

Our staircase model assessed how safety statistics can be used to support acceptance of automated vehicles onto UK roads, and uncovered how different demographics engage with information on automated vehicle safety. 

Partners

Deepsafe was delivered by transport and technology specialists, including DG Cities, dRisk.ai, Imperial College London, Claytex and rFpro. The research was funded by the Centre for Connected and Autonomous Vehicles (CCAV) via Innovate UK.

““There are currently bottlenecks in the technology sector that are inhibiting deep-learning using simulation. These are what the DeepSafe consortium set out to resolve.””
— Rav Babbra, dRISK.ai

DeepSafe is part of CCAV’s Commercialising CAM Supply Chain Competition (CCAMSC). The Commercialising CAM programme is funded by the Centre for Connected and Autonomous Vehicles, a joint unit between the Department for Business and Trade (DBT) and the Department for Transport (DfT) and delivered in partnership with Innovate UK and Zenzic. The CCAM Supply Chain competition was launched in October 2022 to support the delivery of early commercialisable Connected and Automated Mobility technologies, products and services and is part of the Government’s vision for self-driving vehicles. https://www.gov.uk/government/publications/connected-and-automated-mobility-2025-realising-the-benefits-of-self-driving-vehicles