2,042 research outputs found
Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior
Abstract—Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, inter- active motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behaviour, from prediction of individual pedestrians’ likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behaviour, but much work is still needed to translate them into quantitative algorithms for practical AV control
Vulnerable road users and connected autonomous vehicles interaction: a survey
There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.This work was partially funded by the Ministry of Economy, Industry, and Competitiveness
of Spain under Grant: Supervision of drone fleet and optimization of commercial operations flight
plans, PID2020-116377RB-C21.Peer ReviewedPostprint (published version
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 veÃculo altamente autónomo é um dos maiores obstáculos para a
implantação de tais veÃculos, 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 veÃculos autónomos está
a ganhar interesse e atenção entre a comunidade cientÃfica 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 veÃculos autónomos exigem centenas de milhões de quilómetros de
testes conduzidos em vias públicas antes da certificação do veÃculo, 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 fÃsicos 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 crÃticos de autoestrad
Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehicles
Interaction-aware Autonomous Driving (IAAD) is a rapidly growing field of
research that focuses on the development of autonomous vehicles (AVs) that are
capable of interacting safely and efficiently with human road users. This is a
challenging task, as it requires the autonomous vehicle to be able to
understand and predict the behaviour of human road users. In this literature
review, the current state of IAAD research is surveyed in this work. Commencing
with an examination of terminology, attention is drawn to challenges and
existing models employed for modelling the behaviour of drivers and
pedestrians. Next, a comprehensive review is conducted on various techniques
proposed for interaction modelling, encompassing cognitive methods, machine
learning approaches, and game-theoretic methods. The conclusion is reached
through a discussion of potential advantages and risks associated with IAAD,
along with the illumination of pivotal research inquiries necessitating future
exploration
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