19 research outputs found
Learning to drive via Apprenticeship Learning and Deep Reinforcement Learning
With the implementation of reinforcement learning (RL) algorithms, current
state-of-art autonomous vehicle technology have the potential to get closer to
full automation. However, most of the applications have been limited to game
domains or discrete action space which are far from the real world driving.
Moreover, it is very tough to tune the parameters of reward mechanism since the
driving styles vary a lot among the different users. For instance, an
aggressive driver may prefer driving with high acceleration whereas some
conservative drivers prefer a safer driving style. Therefore, we propose an
apprenticeship learning in combination with deep reinforcement learning
approach that allows the agent to learn the driving and stopping behaviors with
continuous actions. We use gradient inverse reinforcement learning (GIRL)
algorithm to recover the unknown reward function and employ REINFORCE as well
as Deep Deterministic Policy Gradient algorithm (DDPG) to learn the optimal
policy. The performance of our method is evaluated in simulation-based scenario
and the results demonstrate that the agent performs human like driving and even
better in some aspects after training.Comment: 7 pages, 11 figures, conferenc
Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Experts
The goal of autonomous vehicles is to navigate public roads safely and
comfortably. To enforce safety, traditional planning approaches rely on
handcrafted rules to generate trajectories. Machine learning-based systems, on
the other hand, scale with data and are able to learn more complex behaviors.
However, they often ignore that agents and self-driving vehicle trajectory
distributions can be leveraged to improve safety. In this paper, we propose
modeling a distribution over multiple future trajectories for both the
self-driving vehicle and other road agents, using a unified neural network
architecture for prediction and planning. During inference, we select the
planning trajectory that minimizes a cost taking into account safety and the
predicted probabilities. Our approach does not depend on any rule-based
planners for trajectory generation or optimization, improves with more training
data and is simple to implement. We extensively evaluate our method through a
realistic simulator and show that the predicted trajectory distribution
corresponds to different driving profiles. We also successfully deploy it on a
self-driving vehicle on urban public roads, confirming that it drives safely
without compromising comfort. The code for training and testing our model on a
public prediction dataset and the video of the road test are available at
https://woven.mobi/safepathne
Control Strategies for Autonomous Vehicles
This chapter focuses on the self-driving technology from a control
perspective and investigates the control strategies used in autonomous vehicles
and advanced driver-assistance systems from both theoretical and practical
viewpoints. First, we introduce the self-driving technology as a whole,
including perception, planning and control techniques required for
accomplishing the challenging task of autonomous driving. We then dwell upon
each of these operations to explain their role in the autonomous system
architecture, with a prime focus on control strategies. The core portion of
this chapter commences with detailed mathematical modeling of autonomous
vehicles followed by a comprehensive discussion on control strategies. The
chapter covers longitudinal as well as lateral control strategies for
autonomous vehicles with coupled and de-coupled control schemes. We as well
discuss some of the machine learning techniques applied to autonomous vehicle
control task. Finally, we briefly summarize some of the research works that our
team has carried out at the Autonomous Systems Lab and conclude the chapter
with a few thoughtful remarks