1,558 research outputs found
Encoding natural movement as an agent-based system: an investigation into human pedestrian behaviour in the built environment
Gibson's ecological theory of perception has received considerable attention within psychology literature, as well as in computer vision and robotics. However, few have applied Gibson's approach to agent-based models of human movement, because the ecological theory requires that individuals have a vision-based mental model of the world, and for large numbers of agents this becomes extremely expensive computationally. Thus, within current pedestrian models, path evaluation is based on calibration from observed data or on sophisticated but deterministic route-choice mechanisms; there is little open-ended behavioural modelling of human-movement patterns. One solution which allows individuals rapid concurrent access to the visual information within an environment is an 'exosomatic visual architecture" where the connections between mutually visible locations within a configuration are prestored in a lookup table. Here we demonstrate that, with the aid of an exosomatic visual architecture, it is possible to develop behavioural models in which movement rules originating from Gibson's principle of affordance are utilised. We apply large numbers of agents programmed with these rules to a built-environment example and show that, by varying parameters such as destination selection, field of view, and steps taken between decision points, it is possible to generate aggregate movement levels very similar to those found in an actual building context
Conditional Affordance Learning for Driving in Urban Environments
Most existing approaches to autonomous driving fall into one of two
categories: modular pipelines, that build an extensive model of the
environment, and imitation learning approaches, that map images directly to
control outputs. A recently proposed third paradigm, direct perception, aims to
combine the advantages of both by using a neural network to learn appropriate
low-dimensional intermediate representations. However, existing direct
perception approaches are restricted to simple highway situations, lacking the
ability to navigate intersections, stop at traffic lights or respect speed
limits. In this work, we propose a direct perception approach which maps video
input to intermediate representations suitable for autonomous navigation in
complex urban environments given high-level directional inputs. Compared to
state-of-the-art reinforcement and conditional imitation learning approaches,
we achieve an improvement of up to 68 % in goal-directed navigation on the
challenging CARLA simulation benchmark. In addition, our approach is the first
to handle traffic lights and speed signs by using image-level labels only, as
well as smooth car-following, resulting in a significant reduction of traffic
accidents in simulation.Comment: Accepted for Conference on Robot Learning (CoRL) 201
Exploring the Limitations of Behavior Cloning for Autonomous Driving
Driving requires reacting to a wide variety of complex environment conditions
and agent behaviors. Explicitly modeling each possible scenario is unrealistic.
In contrast, imitation learning can, in theory, leverage data from large fleets
of human-driven cars. Behavior cloning in particular has been successfully used
to learn simple visuomotor policies end-to-end, but scaling to the full
spectrum of driving behaviors remains an unsolved problem. In this paper, we
propose a new benchmark to experimentally investigate the scalability and
limitations of behavior cloning. We show that behavior cloning leads to
state-of-the-art results, including in unseen environments, executing complex
lateral and longitudinal maneuvers without these reactions being explicitly
programmed. However, we confirm well-known limitations (due to dataset bias and
overfitting), new generalization issues (due to dynamic objects and the lack of
a causal model), and training instability requiring further research before
behavior cloning can graduate to real-world driving. The code of the studied
behavior cloning approaches can be found at
https://github.com/felipecode/coiltraine
Agent architecture for adaptive behaviours in autonomous driving
Evolution has endowed animals with outstanding adaptive behaviours which are grounded in the organization of their sensorimotor system. This paper uses inspiration from these principles of organization in the design of an artificial agent for autonomous driving. After distilling the relevant principles from biology, their functional role in the implementation of an artificial system are explained. The resulting Agent, developed in an EU H2020 Research and Innovation Action, is used to concretely demonstrate the emergence of adaptive behaviour with a significant level of autonomy. Guidelines to adapt the same principled organization of the sensorimotor system to other agents for driving are also obtained. The demonstration of the system abilities is given with example scenarios and open access simulation tools. Prospective developments concerning learning via mental imagery are finally discussed
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