  Combining machine learning and formal methods (FMs) provides a possible
solution to overcome the safety issue of autonomous driving (AD) vehicles.
However, there are gaps to be bridged before this combination becomes
practically applicable and useful. In an attempt to facilitate researchers in
both FMs and AD areas, this paper proposes a framework that combines two
well-known tools, namely CommonRoad and UPPAAL. On the one hand, CommonRoad can
be enhanced by the rigorous semantics of models in UPPAAL, which enables a
systematic and comprehensive understanding of the AD system's behaviour and
thus strengthens the safety of the system. On the other hand, controllers
synthesised by UPPAAL can be visualised by CommonRoad in real-world road
networks, which facilitates AD vehicle designers greatly adopting formal models
in system design. In this framework, we provide automatic model conversions
between CommonRoad and UPPAAL. Therefore, users only need to program in Python
and the framework takes care of the formal models, learning, and verification
in the backend. We perform experiments to demonstrate the applicability of our
framework in various AD scenarios, discuss the advantages of solving motion
planning in our framework, and show the scalability limit and possible
solutions.
