6 research outputs found
Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving
Reinforcement learning has received high research interest for developing
planning approaches in automated driving. Most prior works consider the
end-to-end planning task that yields direct control commands and rarely deploy
their algorithm to real vehicles. In this work, we propose a method to employ a
trained deep reinforcement learning policy for dedicated high-level behavior
planning. By populating an abstract objective interface, established motion
planning algorithms can be leveraged, which derive smooth and drivable
trajectories. Given the current environment model, we propose to use a built-in
simulator to predict the traffic scene for a given horizon into the future. The
behavior of automated vehicles in mixed traffic is determined by querying the
learned policy. To the best of our knowledge, this work is the first to apply
deep reinforcement learning in this manner, and as such lacks a
state-of-the-art benchmark. Thus, we validate the proposed approach by
comparing an idealistic single-shot plan with cyclic replanning through the
learned policy. Experiments with a real testing vehicle on proving grounds
demonstrate the potential of our approach to shrink the simulation to real
world gap of deep reinforcement learning based planning approaches. Additional
simulative analyses reveal that more complex multi-agent maneuvers can be
managed by employing the cycling replanning approach.Comment: 8 pages, 10 figures, to be published in 34th IEEE Intelligent
Vehicles Symposium (IV
Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks
Connected automated driving has the potential to significantly improve urban
traffic efficiency, e.g., by alleviating issues due to occlusion. Cooperative
behavior planning can be employed to jointly optimize the motion of multiple
vehicles. Most existing approaches to automatic intersection management,
however, only consider fully automated traffic. In practice, mixed traffic,
i.e., the simultaneous road usage by automated and human-driven vehicles, will
be prevalent. The present work proposes to leverage reinforcement learning and
a graph-based scene representation for cooperative multi-agent planning. We
build upon our previous works that showed the applicability of such machine
learning methods to fully automated traffic. The scene representation is
extended for mixed traffic and considers uncertainty in the human drivers'
intentions. In the simulation-based evaluation, we model measurement
uncertainties through noise processes that are tuned using real-world data. The
paper evaluates the proposed method against an enhanced first in - first out
scheme, our baseline for mixed traffic management. With increasing share of
automated vehicles, the learned planner significantly increases the vehicle
throughput and reduces the delay due to interaction. Non-automated vehicles
benefit virtually alike.Comment: 8 pages, 7 figures, 34th IEEE Intelligent Vehicles Symposium (IV),
updated to accepted versio
A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions
Already today, driver assistance systems help to make daily traffic more
comfortable and safer. However, there are still situations that are quite rare
but are hard to handle at the same time. In order to cope with these situations
and to bridge the gap towards fully automated driving, it becomes necessary to
not only collect enormous amounts of data but rather the right ones. This data
can be used to develop and validate the systems through machine learning and
simulation pipelines. Along this line this paper presents a fleet
learning-based architecture that enables continuous improvements of systems
predicting the movement of surrounding traffic participants. Moreover, the
presented architecture is applied to a testing vehicle in order to prove the
fundamental feasibility of the system. Finally, it is shown that the system
collects meaningful data which are helpful to improve the underlying prediction
systems.Comment: the article has been accepted for publication during the 2020 IEEE
Symposium Series on Computational Intelligence (SSCI) within the IEEE
Symposium on Computational Intelligence in Vehicles and Transportation
Systems (CIVTS), 7 pages, 6 figure
Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based Recurrent Neural Networks
To plan safe and comfortable trajectories for automated vehicles on highways,
accurate predictions of traffic situations are needed. So far, a lot of
research effort has been spent on detecting lane change maneuvers rather than
on estimating the point in time a lane change actually happens. In practice,
however, this temporal information might be even more useful. This paper deals
with the development of a system that accurately predicts the time to the next
lane change of surrounding vehicles on highways using long short-term
memory-based recurrent neural networks. An extensive evaluation based on a
large real-world data set shows that our approach is able to make reliable
predictions, even in the most challenging situations, with a root mean squared
error around 0.7 seconds. Already 3.5 seconds prior to lane changes the
predictions become highly accurate, showing a median error of less than 0.25
seconds. In summary, this article forms a fundamental step towards downstreamed
highly accurate position predictions.Comment: the article has been accepted for publication in IEEE Robotics and
Automation Letters (RA-L); the article has been submitted to RA-L with IEEE
ICRA conference option; if the article will be presented during the
conference will be decided independently; 8 pages, 5 figures, 6 table