  Providing safety guarantees for Autonomous Vehicle (AV) systems with
machine-learning-based controllers remains a challenging issue. In this work,
we propose Simplex-Drive, a framework that can achieve runtime safety assurance
for machine-learning enabled controllers of AVs. The proposed Simplex-Drive
consists of an unverified Deep Reinforcement Learning (DRL)-based advanced
controller (AC) that achieves desirable performance in complex scenarios, a
Velocity-Obstacle (VO) based baseline safe controller (BC) with provably safety
guarantees, and a verified mode management unit that monitors the operation
status and switches the control authority between AC and BC based on
safety-related conditions. We provide a formal correctness proof of
Simplex-Drive and conduct a lane-changing case study in dense traffic
scenarios. The simulation experiment results demonstrate that Simplex-Drive can
always ensure operation safety without sacrificing control performance, even if
the DRL policy may lead to deviations from the safe status.
