  We present a novel adaptive cruise control (ACC) system namely SAINT-ACC:
{S}afety-{A}ware {Int}elligent {ACC} system (SAINT-ACC) that is designed to
achieve simultaneous optimization of traffic efficiency, driving safety, and
driving comfort through dynamic adaptation of the inter-vehicle gap based on
deep reinforcement learning (RL). A novel dual RL agent-based approach is
developed to seek and adapt the optimal balance between traffic efficiency and
driving safety/comfort by effectively controlling the driving safety model
parameters and inter-vehicle gap based on macroscopic and microscopic traffic
information collected from dynamically changing and complex traffic
environments. Results obtained through over 12,000 simulation runs with varying
traffic scenarios and penetration rates demonstrate that SAINT-ACC
significantly enhances traffic flow, driving safety and comfort compared with a
state-of-the-art approach.
