  We present an autonomous 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 comport compared with
the state-of-the-art approach.
