Context
Mobility is vital for transitioning to sustainability, and AI-powered smart mobility solutions are key to achieving the goal of more sustainable and accessible transportation. Applications such as Mobility-as-a-Service (MaaS) or the deployment of connected and autonomous vehicles can benefit the environment by lowering pollutant emissions, and can benefit citizens by allowing a fairer access to mobility for all and by improving safety in urban environments.
Many policy initiatives target this nexus: for instance, smart mobility solutions represent a core part of the Sustainable and Smart Mobility Strategy. Furthermore, the positive potential of smart mobility was also established in the Strategic Transport Research and Innovation Agenda (STRIA) roadmap, adopted by the European Commission as part of the ‘Europe on the Move’ package.
Approach
AI, by feeding on large amounts of data to extract patterns and insights, can undeniably be a transversal force of change for several sectors.
However, technology adoption does not restrict to the potential held by the technology per se: it rather expands to the context surrounding it. From this more holistic perspective, each sector displays its own set of peculiarities (and challenges) with respect to AI uptake.
In this task, we outline such sectoral specificities in the adoption of AI both quantitatively, by blending different streams of data, and qualitatively, through expert consultations and literature reviews.
Publications
This paper addresses the often overlooked issue of fairness in the autonomous driving domain, particularly in vision-based perception and prediction systems, which play a pivotal role in the overall functioning of Autonomous Vehicles (AVs).
We identify the maturity level of the different requirements for artificial intelligence (AI) in autonomous driving and outline the main challenges to be addressed in the future to ensure that automotive AI systems are developed in a trustworthy way.
All our publications on Trustworthy Artificial Intelligence in Automated/Autonomous Driving
Addressing by design the AI safety and cybersecurity challenges is key to securing the many benefits that automated driving can bring to society.
Trustworthy requirements for AVs have a heterogeneous level of maturity, and bring new research and development challenges in different areas.
The report puts forward a set of challenges and recommendations to improve AI security in autonomous vehicles and mitigate the risks.
This AI Watch report presents the sectoral analysis of AI in smart mobility. Its main aim is to act as a baseline for future editions to be able to assess the changes in uptake and impact of AI in mobility over time.
This report provides an analysis of digital transformation (DT) in a selection of policy areas covering transport, construction, energy, and digital government and public administration