26,395 research outputs found
Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving
Behavior and motion planning play an important role in automated driving.
Traditionally, behavior planners instruct local motion planners with predefined
behaviors. Due to the high scene complexity in urban environments,
unpredictable situations may occur in which behavior planners fail to match
predefined behavior templates. Recently, general-purpose planners have been
introduced, combining behavior and local motion planning. These general-purpose
planners allow behavior-aware motion planning given a single reward function.
However, two challenges arise: First, this function has to map a complex
feature space into rewards. Second, the reward function has to be manually
tuned by an expert. Manually tuning this reward function becomes a tedious
task. In this paper, we propose an approach that relies on human driving
demonstrations to automatically tune reward functions. This study offers
important insights into the driving style optimization of general-purpose
planners with maximum entropy inverse reinforcement learning. We evaluate our
approach based on the expected value difference between learned and
demonstrated policies. Furthermore, we compare the similarity of human driven
trajectories with optimal policies of our planner under learned and
expert-tuned reward functions. Our experiments show that we are able to learn
reward functions exceeding the level of manual expert tuning without prior
domain knowledge.Comment: Appeared at IROS 2019. Accepted version. Added/updated footnote,
minor correction in preliminarie
A Distributed Model Predictive Control Framework for Road-Following Formation Control of Car-like Vehicles (Extended Version)
This work presents a novel framework for the formation control of multiple
autonomous ground vehicles in an on-road environment. Unique challenges of this
problem lie in 1) the design of collision avoidance strategies with obstacles
and with other vehicles in a highly structured environment, 2) dynamic
reconfiguration of the formation to handle different task specifications. In
this paper, we design a local MPC-based tracking controller for each individual
vehicle to follow a reference trajectory while satisfying various constraints
(kinematics and dynamics, collision avoidance, \textit{etc.}). The reference
trajectory of a vehicle is computed from its leader's trajectory, based on a
pre-defined formation tree. We use logic rules to organize the collision
avoidance behaviors of member vehicles. Moreover, we propose a methodology to
safely reconfigure the formation on-the-fly. The proposed framework has been
validated using high-fidelity simulations.Comment: Extended version of the conference paper submission on ICARCV'1
Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?
Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations
Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning
Providing an efficient strategy to navigate safely through unsignaled
intersections is a difficult task that requires determining the intent of other
drivers. We explore the effectiveness of Deep Reinforcement Learning to handle
intersection problems. Using recent advances in Deep RL, we are able to learn
policies that surpass the performance of a commonly-used heuristic approach in
several metrics including task completion time and goal success rate and have
limited ability to generalize. We then explore a system's ability to learn
active sensing behaviors to enable navigating safely in the case of occlusions.
Our analysis, provides insight into the intersection handling problem, the
solutions learned by the network point out several shortcomings of current
rule-based methods, and the failures of our current deep reinforcement learning
system point to future research directions.Comment: IEEE International Conference on Robotics and Automation (ICRA 2018
Advances in the Hierarchical Emergent Behaviors (HEB) approach to autonomous vehicles
Widespread deployment of autonomous vehicles (AVs) presents formidable challenges in terms on handling scalability and complexity, particularly regarding vehicular reaction in the face of unforeseen corner cases. Hierarchical Emergent Behaviors (HEB) is a scalable architecture based on the concepts of emergent behaviors and hierarchical decomposition. It relies on a few simple but powerful rules to govern local vehicular interactions. Rather than requiring prescriptive programming of every possible scenario, HEB’s approach relies on global behaviors induced by the application of these local, well-understood rules. Our first two papers on HEB focused on a primal set of rules applied at the first hierarchical level. On the path to systematize a solid design methodology, this paper proposes additional rules for the second level, studies through simulations the resultant richer set of emergent behaviors, and discusses the communica-tion mechanisms between the different levels.Peer ReviewedPostprint (author's final draft
Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach
We present a hierarchical control approach for maneuvering an autonomous
vehicle (AV) in tightly-constrained environments where other moving AVs and/or
human driven vehicles are present. A two-level hierarchy is proposed: a
high-level data-driven strategy predictor and a lower-level model-based
feedback controller. The strategy predictor maps an encoding of a dynamic
environment to a set of high-level strategies via a neural network. Depending
on the selected strategy, a set of time-varying hyperplanes in the AV's
position space is generated online and the corresponding halfspace constraints
are included in a lower-level model-based receding horizon controller. These
strategy-dependent constraints drive the vehicle towards areas where it is
likely to remain feasible. Moreover, the predicted strategy also informs
switching between a discrete set of policies, which allows for more
conservative behavior when prediction confidence is low. We demonstrate the
effectiveness of the proposed data-driven hierarchical control framework in a
two-car collision avoidance scenario through simulations and experiments on a
1/10 scale autonomous car platform where the strategy-guided approach
outperforms a model predictive control baseline in both cases.Comment: 7 pages, 7 figures, accepted at ICRA 202
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