Autonomy, electrification, and connectivity are reshaping the automotive landscape. To meet global demand, the automotive industry must shift from selling cars to delivering flexible, sustainable systems built for everyone, writes Dr Christopher Borroni-Bird
‘Beyond the Car’, FISITA’s podcast series, kicks off with a leadership special featuring Mike Anderson and ChangHwan Kim’s reflections on the FISITA Presidency and the evolution of the organisation. By James Scoltock
The shift to software-defined vehicles is more than a technical upgrade, it’s a cultural revolution. Explore how legacy manufacturers need to evolve to stay relevant in a market driven by intelligence, integration, and speed. By Dr Partha Goswami
‘Beyond the Car’, FISITA’s podcast series, kicks off with a leadership special featuring Mike Anderson and ChangHwan Kim’s reflections on the FISITA Presidency and the evolution of the organisation. By James Scoltock
Save the date for the FISITA World Mobility Summit 2026, hosted by FISITA President ChangHwan Kim on 4–5 November in Seoul. Supported by KSAE, the biennial summit brings together leaders of FISITA member organisations for two days of thought leadership, knowledge sharing, networking and the Academy of Technical Leadership Awards. Alongside this, preparations for EuroBrake 2026 are underway, with sponsorship opportunities available and the Call for Papers now open. FISITA also highlights recent digitalisation insights from its latest webinar and invites the community to tune into the new ‘Beyond the Car’ podcast series.
AI-driven simulation technologies are increasingly promoted as a potential “holy grail” for the automotive engineering community, promising accelerated vehicle development and breakthrough design capabilities. Nonetheless, important questions remain around topics such as:
– The extent to which machine learning solvers can already deliver reliable, swift, and well-reasoned design decisions.
– The inherent drawbacks and risks of data-driven simulation methods.
– The challenges which must be addressed to enable large-scale implementation of machine learning solvers in safety-critical automotive engineering.
– How geometry-aware machine learning methods can transform design and simulation, and which obstacles still limit their practical adoption.
