25,337 research outputs found
DRIVE: A Digital Network Oracle for Cooperative Intelligent Transportation Systems
In a world where Artificial Intelligence revolutionizes inference, prediction
and decision-making tasks, Digital Twins emerge as game-changing tools. A case
in point is the development and optimization of Cooperative Intelligent
Transportation Systems (C-ITSs): a confluence of cyber-physical digital
infrastructure and (semi)automated mobility. Herein we introduce Digital Twin
for self-dRiving Intelligent VEhicles (DRIVE). The developed framework tackles
shortcomings of traditional vehicular and network simulators. It provides a
flexible, modular, and scalable implementation to ensure large-scale, city-wide
experimentation with a moderate computational cost. The defining feature of our
Digital Twin is a unique architecture allowing for submission of sequential
queries, to which the Digital Twin provides instantaneous responses with the
"state of the world", and hence is an Oracle. With such bidirectional
interaction with external intelligent agents and realistic mobility traces,
DRIVE provides the environment for development, training and optimization of
Machine Learning based C-ITS solutions.Comment: Accepted for publication at IEEE ISCC 202
Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search
Urban traffic scenarios often require a high degree of cooperation between
traffic participants to ensure safety and efficiency. Observing the behavior of
others, humans infer whether or not others are cooperating. This work aims to
extend the capabilities of automated vehicles, enabling them to cooperate
implicitly in heterogeneous environments. Continuous actions allow for
arbitrary trajectories and hence are applicable to a much wider class of
problems than existing cooperative approaches with discrete action spaces.
Based on cooperative modeling of other agents, Monte Carlo Tree Search (MCTS)
in conjunction with Decoupled-UCT evaluates the action-values of each agent in
a cooperative and decentralized way, respecting the interdependence of actions
among traffic participants. The extension to continuous action spaces is
addressed by incorporating novel MCTS-specific enhancements for efficient
search space exploration. The proposed algorithm is evaluated under different
scenarios, showing that the algorithm is able to achieve effective cooperative
planning and generate solutions egocentric planning fails to identify
A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks
Situational awareness in vehicular networks could be substantially improved
utilizing reliable trajectory prediction methods. More precise situational
awareness, in turn, results in notably better performance of critical safety
applications, such as Forward Collision Warning (FCW), as well as comfort
applications like Cooperative Adaptive Cruise Control (CACC). Therefore,
vehicle trajectory prediction problem needs to be deeply investigated in order
to come up with an end to end framework with enough precision required by the
safety applications' controllers. This problem has been tackled in the
literature using different methods. However, machine learning, which is a
promising and emerging field with remarkable potential for time series
prediction, has not been explored enough for this purpose. In this paper, a
two-layer neural network-based system is developed which predicts the future
values of vehicle parameters, such as velocity, acceleration, and yaw rate, in
the first layer and then predicts the two-dimensional, i.e. longitudinal and
lateral, trajectory points based on the first layer's outputs. The performance
of the proposed framework has been evaluated in realistic cut-in scenarios from
Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable
improvement in the prediction accuracy in comparison with the kinematics model
which is the dominant employed model by the automotive industry. Both ideal and
nonideal communication circumstances have been investigated for our system
evaluation. For non-ideal case, an estimation step is included in the framework
before the parameter prediction block to handle the drawbacks of packet drops
or sensor failures and reconstruct the time series of vehicle parameters at a
desirable frequency
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