689 research outputs found
Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing
Within the context of autonomous driving a model-based reinforcement learning
algorithm is proposed for the design of neural network-parameterized
controllers. Classical model-based control methods, which include sampling- and
lattice-based algorithms and model predictive control, suffer from the
trade-off between model complexity and computational burden required for the
online solution of expensive optimization or search problems at every short
sampling time. To circumvent this trade-off, a 2-step procedure is motivated:
first learning of a controller during offline training based on an arbitrarily
complicated mathematical system model, before online fast feedforward
evaluation of the trained controller. The contribution of this paper is the
proposition of a simple gradient-free and model-based algorithm for deep
reinforcement learning using task separation with hill climbing (TSHC). In
particular, (i) simultaneous training on separate deterministic tasks with the
purpose of encoding many motion primitives in a neural network, and (ii) the
employment of maximally sparse rewards in combination with virtual velocity
constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl
Trajectory planning based on adaptive model predictive control: Study of the performance of an autonomous vehicle in critical highway scenarios
Increasing automation in automotive industry is an important contribution to
overcome many of the major societal challenges. However, testing and validating a highly
autonomous vehicle is one of the biggest obstacles to the deployment of such vehicles,
since they rely on data-driven and real-time sensors, actuators, complex algorithms,
machine learning systems, and powerful processors to execute software, and they must
be proven to be reliable and safe.
For this reason, the verification, validation and testing (VVT) of autonomous
vehicles is gaining interest and attention among the scientific community and there has
been a number of significant efforts in this field. VVT helps developers and testers to
determine any hidden faults, increasing systems confidence in safety, security, functional
analysis, and in the ability to integrate autonomous prototypes into existing road
networks. Other stakeholders like higher-management, public authorities and the public
are also crucial to complete the VTT process.
As autonomous vehicles require hundreds of millions of kilometers of testing
driven on public roads before vehicle certification, simulations are playing a key role as
they allow the simulation tools to virtually test millions of real-life scenarios, increasing
safety and reducing costs, time and the need for physical road tests.
In this study, a literature review is conducted to classify approaches for the VVT
and an existing simulation tool is used to implement an autonomous driving system. The
system will be characterized from the point of view of its performance in some critical
highway scenarios.O aumento da automação na indústria automotiva é uma importante
contribuição para superar muitos dos principais desafios da sociedade. No entanto,
testar e validar um veículo altamente autónomo é um dos maiores obstáculos para a
implantação de tais veículos, uma vez que eles contam com sensores, atuadores,
algoritmos complexos, sistemas de aprendizagem de máquina e processadores potentes
para executar softwares em tempo real, e devem ser comprovadamente confiáveis e
seguros.
Por esta razão, a verificação, validação e teste (VVT) de veículos autónomos está
a ganhar interesse e atenção entre a comunidade científica e tem havido uma série de
esforços significativos neste campo. A VVT ajuda os desenvolvedores e testadores a
determinar quaisquer falhas ocultas, aumentando a confiança dos sistemas na
segurança, proteção, análise funcional e na capacidade de integrar protótipos autónomos
em redes rodoviárias existentes. Outras partes interessadas, como a alta administração,
autoridades públicas e o público também são cruciais para concluir o processo de VTT.
Como os veículos autónomos exigem centenas de milhões de quilómetros de
testes conduzidos em vias públicas antes da certificação do veículo, as simulações estão
a desempenhar cada vez mais um papel fundamental, pois permitem que as ferramentas
de simulação testem virtualmente milhões de cenários da vida real, aumentando a
segurança e reduzindo custos, tempo e necessidade de testes físicos em estrada.
Neste estudo, é realizada uma revisão da literatura para classificar abordagens
para a VVT e uma ferramenta de simulação existente é usada para implementar um
sistema de direção autónoma. O sistema é caracterizado do ponto de vista do seu
desempenho em alguns cenários críticos de autoestrad
An Overview about Emerging Technologies of Autonomous Driving
Since DARPA started Grand Challenges in 2004 and Urban Challenges in 2007,
autonomous driving has been the most active field of AI applications. This
paper gives an overview about technical aspects of autonomous driving
technologies and open problems. We investigate the major fields of self-driving
systems, such as perception, mapping and localization, prediction, planning and
control, simulation, V2X and safety etc. Especially we elaborate on all these
issues in a framework of data closed loop, a popular platform to solve the long
tailed autonomous driving problems
Conception of control paradigms for teleoperated driving tasks in urban environments
Development of concepts and computationally efficient motion planning methods for teleoperated drivingEntwicklung von Konzepten und recheneffizienten Bewegungsplanungsmethoden für teleoperiertes Fahre
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