  Machine learning can provide efficient solutions to the complex problems
encountered in autonomous driving, but ensuring their safety remains a
challenge. A number of authors have attempted to address this issue, but there
are few publicly-available tools to adequately explore the trade-offs between
functionality, scalability, and safety.
  We thus present WiseMove, a software framework to investigate safe deep
reinforcement learning in the context of motion planning for autonomous
driving. WiseMove adopts a modular learning architecture that suits our current
research questions and can be adapted to new technologies and new questions. We
present the details of WiseMove, demonstrate its use on a common traffic
scenario, and describe how we use it in our ongoing safe learning research.
