ADAPS: Autonomous Driving Via Principled Simulations
This work addresses the problem of robust autonomous driving control for vehicle systems, presenting an incremental improvement through a novel simulation-based training approach.
The paper tackles the challenge of obtaining robust control policies for autonomous vehicles by proposing ADAPS, which uses principled simulations to generate accident scenarios for training data and a memory-enabled hierarchical policy architecture. Experimental results in simulated environments demonstrate that ADAPS reduces the number of learning iterations compared to existing methods like DAGGER.
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.