ADAPS: Autonomous Driving Via Principled Simulations

TL;DR

ADAPS introduces a simulation-based framework with accident analysis and hierarchical control for robust autonomous driving policies, improving learning efficiency and policy robustness in diverse scenarios.

Contribution

It combines accident simulation and hierarchical control with an efficient online learning mechanism, advancing autonomous driving policy training methods.

Findings

ADAPS reduces training iterations compared to DAGGER.

It produces robust control policies in simulated environments.

Theoretical analysis supports improved learning efficiency.

Abstract

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.