iseAuto as a Testbed for Safe Autonomous Driving: Bridging Formal Verification and Artificial Intelligence

Autonomous driving has reached a stage where technical feasibility is no longer in question, yet large-scale deployment is made difficult by safety concerns. 
Traditional approaches to system assurance, based on extensive testing or scenario catalogues, are insufficient to capture the open-endedness of real-world environments. 
At the same time, machine learning (ML) methods, particularly in perception, offer remarkable capabilities but remain opaque and difficult to verify formally.
This tension calls for a new paradigm in which formal verification methods and artificial intelligence approaches are not seen as incompatible but rather as complementary. 
This position paper argues that bridging these domains is both necessary and feasible, and that \textit{iseAuto}, an open-source autonomous shuttle developed in two generations (v1 and v2), provides a well-suited testbed to demonstrate how theoretical methods can translate into real-world applications.