  Autonomous driving has gained significant advancements in recent years.
However, obtaining a robust control policy for driving remains challenging as
it requires training data from a variety of scenarios, including rare
situations (e.g., accidents), an effective policy architecture, and an
efficient learning mechanism. We propose ADAPS for producing robust control
policies for autonomous vehicles. ADAPS consists of two simulation platforms in
generating and analyzing accidents to automatically produce labeled training
data, and a memory-enabled hierarchical control policy. Additionally, ADAPS
offers a more efficient online learning mechanism that reduces the number of
iterations required in learning compared to existing methods such as DAGGER. We
present both theoretical and experimental results. The latter are produced in
simulated environments, where qualitative and quantitative results are
generated to demonstrate the benefits of ADAPS.
