2,028 research outputs found
From Specifications to Behavior: Maneuver Verification in a Semantic State Space
To realize a market entry of autonomous vehicles in the foreseeable future,
the behavior planning system will need to abide by the same rules that humans
follow. Product liability cannot be enforced without a proper solution to the
approval trap. In this paper, we define a semantic abstraction of the
continuous space and formalize traffic rules in linear temporal logic (LTL).
Sequences in the semantic state space represent maneuvers a high-level planner
could choose to execute. We check these maneuvers against the formalized
traffic rules using runtime verification. By using the standard model checker
NuSMV, we demonstrate the effectiveness of our approach and provide runtime
properties for the maneuver verification. We show that high-level behavior can
be verified in a semantic state space to fulfill a set of formalized rules,
which could serve as a step towards safety of the intended functionality.Comment: Published at IEEE Intelligent Vehicles Symposium (IV), 201
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Generating Long-term Trajectories Using Deep Hierarchical Networks
We study the problem of modeling spatiotemporal trajectories over long time
horizons using expert demonstrations. For instance, in sports, agents often
choose action sequences with long-term goals in mind, such as achieving a
certain strategic position. Conventional policy learning approaches, such as
those based on Markov decision processes, generally fail at learning cohesive
long-term behavior in such high-dimensional state spaces, and are only
effective when myopic modeling lead to the desired behavior. The key difficulty
is that conventional approaches are "shallow" models that only learn a single
state-action policy. We instead propose a hierarchical policy class that
automatically reasons about both long-term and short-term goals, which we
instantiate as a hierarchical neural network. We showcase our approach in a
case study on learning to imitate demonstrated basketball trajectories, and
show that it generates significantly more realistic trajectories compared to
non-hierarchical baselines as judged by professional sports analysts.Comment: Published in NIPS 201
Plan recognition for space telerobotics
Current research on space telerobots has largely focused on two problem areas: executing remotely controlled actions (the tele part of telerobotics) or planning to execute them (the robot part). This work has largely ignored one of the key aspects of telerobots: the interaction between the machine and its operator. For this interaction to be felicitous, the machine must successfully understand what the operator is trying to accomplish with particular remote-controlled actions. Only with the understanding of the operator's purpose for performing these actions can the robot intelligently assist the operator, perhaps by warning of possible errors or taking over part of the task. There is a need for such an understanding in the telerobotics domain and an intelligent interface being developed in the chemical process design domain addresses the same issues
Intelligent Roundabout Insertion using Deep Reinforcement Learning
An important topic in the autonomous driving research is the development of
maneuver planning systems. Vehicles have to interact and negotiate with each
other so that optimal choices, in terms of time and safety, are taken. For this
purpose, we present a maneuver planning module able to negotiate the entering
in busy roundabouts. The proposed module is based on a neural network trained
to predict when and how entering the roundabout throughout the whole duration
of the maneuver. Our model is trained with a novel implementation of A3C, which
we will call Delayed A3C (D-A3C), in a synthetic environment where vehicles
move in a realistic manner with interaction capabilities. In addition, the
system is trained such that agents feature a unique tunable behavior, emulating
real world scenarios where drivers have their own driving styles. Similarly,
the maneuver can be performed using different aggressiveness levels, which is
particularly useful to manage busy scenarios where conservative rule-based
policies would result in undefined waits
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