4,900 research outputs found
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
An approach to simulation of autonomous vehicles
Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major de Telecomunicações). Faculdade de Engenharia. Universidade do Porto. 200
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
Thanks to the augmented convenience, safety advantages, and potential
commercial value, Intelligent vehicles (IVs) have attracted wide attention
throughout the world. Although a few autonomous driving unicorns assert that
IVs will be commercially deployable by 2025, their implementation is still
restricted to small-scale validation due to various issues, among which precise
computation of control commands or trajectories by planning methods remains a
prerequisite for IVs. This paper aims to review state-of-the-art planning
methods, including pipeline planning and end-to-end planning methods. In terms
of pipeline methods, a survey of selecting algorithms is provided along with a
discussion of the expansion and optimization mechanisms, whereas in end-to-end
methods, the training approaches and verification scenarios of driving tasks
are points of concern. Experimental platforms are reviewed to facilitate
readers in selecting suitable training and validation methods. Finally, the
current challenges and future directions are discussed. The side-by-side
comparison presented in this survey not only helps to gain insights into the
strengths and limitations of the reviewed methods but also assists with
system-level design choices.Comment: 20 pages, 14 figures and 5 table
An Agent-Based Framework for Urban Mobility Simulation
International audienceMobility study is composed of many research areas which one interests us: urban mobility. In the literature, urban mobilities are represented by analytical techniques like stochastic laws or they are defined by simulation tools like Multi-Agents Systems (MAS). The goal of our work is to define citizen behaviour in order to observe population dynamics by a simulation. This strategy is facilitated by a meta-model and a toolkit which are used with a particular method. The latter begins by a conceptual representation of each mobile and finishes by a mobility simulator. This paper aims at describing the mobility simulation toolkit. Thanks to this framework, mobility simulator development is sim- plified. It allows us to create distributed applications which are based on MAS
Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation
Optimizing traffic dynamics in an evolving transportation landscape is
crucial, particularly in scenarios where autonomous vehicles (AVs) with varying
levels of autonomy coexist with human-driven cars. While optimizing
Reinforcement Learning (RL) policies for such scenarios is becoming more and
more common, little has been said about realistic evaluations of such trained
policies. This paper presents an evaluation of the effects of AVs penetration
among human drivers in a roundabout scenario, considering both quantitative and
qualitative aspects. In particular, we learn a policy to minimize traffic jams
(i.e., minimize the time to cross the scenario) and to minimize pollution in a
roundabout in Milan, Italy. Through empirical analysis, we demonstrate that the
presence of AVs} can reduce time and pollution levels. Furthermore, we
qualitatively evaluate the learned policy using a cutting-edge cockpit to
assess its performance in near-real-world conditions. To gauge the practicality
and acceptability of the policy, we conduct evaluations with human participants
using the simulator, focusing on a range of metrics like traffic smoothness and
safety perception. In general, our findings show that human-driven vehicles
benefit from optimizing AVs dynamics. Also, participants in the study highlight
that the scenario with 80% AVs is perceived as safer than the scenario with
20%. The same result is obtained for traffic smoothness perception
Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation
Optimizing traffic dynamics in an evolving transportation landscape is crucial, particularly in scenarios where autonomous vehicles (AVs) with varying levels of autonomy coexist with human-driven cars. While optimizing Reinforcement Learning (RL) policies for such scenarios is becoming more and more common, little has been said about realistic evaluations of such trained policies. This paper presents an evaluation of the effects of AVs penetration among human drivers in a roundabout scenario, considering both quantitative and qualitative aspects. In particular, we learn a policy to minimize traffic jams (i.e., minimize the time to cross the scenario) and to minimize pollution in a roundabout in Milan, Italy. Through empirical analysis, we demonstrate that the presence of AVs can reduce time and pollution levels. Furthermore, we qualitatively evaluate the learned policy using a cutting-edge cockpit to assess its performance in near-real-world conditions. To gauge the practicality and acceptability of the policy, we conduct evaluations with human participants using the simulator, focusing on a range of metrics like traffic smoothness and safety perception. In general, our findings show that human-driven vehicles benefit from optimizing AVs dynamics. Also, participants in the study highlight that the scenario with 80% AVs is perceived as safer than the scenario with 20%. The same result is obtained for traffic smoothness perception
Software architectural design for safety in Automated Parking System
The automotive industry has seen a revolution brought about by self-driving cars. However, one of the main challenges facing autonomous driving systems is ensuring safety in the absence of a supervising driver and verifying safe vehicle behaviour under various circumstances. Autonomous Driving Systems (ADS), due to their complexity, cannot be solved straightforwardly without proper structure. Thus, they need a well-defined architecture to guide their development with requirements that involve modularity, scalability, and maintainability among other properties. To help overcome some of the challenges, this master thesis defines and implements in a simulated environment an automated parking system that complies with industrial and safety standards. The work has been divided into four parts. Firstly, the safety rules for the development of an autonomous function have been analysed. Secondly, the use cases and system requirements have been defined following the needs of the automated parking system. Thirdly, the system has been implemented in the simulation environment with a structure based on a widely adopted automotive standard. The final result is the software architecture of an autonomous vehicle with automated parking functionality. This concept has been validated within the virtual environment together with the integration of the AUTOSAR runtime environment, which the communication between components and mode switching functionality in the CARLA simulation environment. The result of this project shows the benefit of integrating architecture and simulation, thus easing the development and testing of future autonomous systems
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
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