Sampling-based Bi-level Optimization aided by Behaviour Cloning for Autonomous Driving
Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
Autonomous driving has a natural bi-level structure. The upper behavioural layer aims to
provide appropriate lane change, speeding up, and braking decisions to optimize a given
driving task. The upper layer can only indirectly influence the driving efficiency through
the lower-level trajectory planner, which takes in the behavioural inputs to produce
motion commands for the controller. Existing sampling-based approaches do not fully
exploit the strong coupling between the behavioural and planning layer. On the other
hand, Reinforcement Learning (RL) can learn a behavioural layer while incorporating
feedback from the lower-level planner. However, purely data-driven approaches often
fail regarding safety metrics in dense and rash traffic environments. This thesis presents a
novel alternative; a parameterized bi-level optimization that jointly computes the optimal
behavioural decisions and the resulting downstream trajectory. The proposed approach
runs in real-time using a custom Graphics Processing Unit (GPU)-accelerated batch
optimizer and a Conditional Variational Autoencoder (CVAE) learnt warm-start strategy
and extensive experiments on challenging traffic scenarios show that it outperforms
state-of-the-art Model Predictive Control (MPC) and RL approaches regarding collision
rate while being competitive in driving efficiency.
Description
Keywords
Autonomous Driving, Bi-level Optimization, Behavioural Cloning, Differentiable Optimization, Conditional Variational Autoencoder