1 research outputs found
Learning Driving Decisions by Imitating Drivers' Control Behaviors
Classical autonomous driving systems are modularized as a pipeline of
perception, decision, planning, and control. The driving decision plays a
central role in processing the observation from the perception as well as
directing the execution of downstream planning and control modules. Commonly
the decision module is designed to be rule-based and is difficult to learn from
data. Recently end-to-end neural control policy has been proposed to replace
this pipeline, given its generalization ability. However, it remains
challenging to enforce physical or logical constraints on the decision to
ensure driving safety and stability. In this work, we propose a hybrid
framework for learning a decision module, which is agnostic to the mechanisms
of perception, planning, and control modules. By imitating the low-level
control behavior, it learns the high-level driving decisions while bypasses the
ambiguous annotation of high-level driving decisions. We demonstrate that the
simulation agents with a learned decision module can be generalized to various
complex driving scenarios where the rule-based approach fails. Furthermore, it
can generate driving behaviors that are smoother and safer than end-to-end
neural policies