103,824 research outputs found
An Agent-based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles
Due to the complexity of the natural world, a programmer cannot foresee all
possible situations, a connected and autonomous vehicle (CAV) will face during
its operation, and hence, CAVs will need to learn to make decisions
autonomously. Due to the sensing of its surroundings and information exchanged
with other vehicles and road infrastructure, a CAV will have access to large
amounts of useful data. While different control algorithms have been proposed
for CAVs, the benefits brought about by connectedness of autonomous vehicles to
other vehicles and to the infrastructure, and its implications on policy
learning has not been investigated in literature. This paper investigates a
data driven driving policy learning framework through an agent-based modelling
approaches. The contributions of the paper are two-fold. A dynamic programming
framework is proposed for in-vehicle policy learning with and without
connectivity to neighboring vehicles. The simulation results indicate that
while a CAV can learn to make autonomous decisions, vehicle-to-vehicle (V2V)
communication of information improves this capability. Furthermore, to overcome
the limitations of sensing in a CAV, the paper proposes a novel concept for
infrastructure-led policy learning and communication with autonomous vehicles.
In infrastructure-led policy learning, road-side infrastructure senses and
captures successful vehicle maneuvers and learns an optimal policy from those
temporal sequences, and when a vehicle approaches the road-side unit, the
policy is communicated to the CAV. Deep-imitation learning methodology is
proposed to develop such an infrastructure-led policy learning framework
An Investigation of Autonomous Vehicle Roundabout Situation
The aim of this paper is to evaluate the impact of connected autonomous behavior in real vehicles on vehicle fuel consumption and emission reductions. Authors provide a preliminary theoretical summary to assess the driving conditions of autonomous vehicles in roundabout, which attempts exploring the impact of driving behavior patterns on fuel consumption and emissions, and including other key factors of autonomous vehicles to reduce fuel consumption and emissions. After summarizing, driving behavior, effective in-vehicle systems, both roundabout physical parameters and vehicle type are all play an important role in energy using. ZalaZONE’s roundabout is selected for preliminary test scenario establishment, which lays a design foundation for further in-depth testing
Emerging privacy challenges and approaches in CAV systems
The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions
Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses
In the vehicular mixed reality (MR) Metaverse, the distance between physical
and virtual entities can be overcome by fusing the physical and virtual
environments with multi-dimensional communications in autonomous driving
systems. Assisted by digital twin (DT) technologies, connected autonomous
vehicles (AVs), roadside units (RSU), and virtual simulators can maintain the
vehicular MR Metaverse via digital simulations for sharing data and making
driving decisions collaboratively. However, large-scale traffic and driving
simulation via realistic data collection and fusion from the physical world for
online prediction and offline training in autonomous driving systems are
difficult and costly. In this paper, we propose an autonomous driving
architecture, where generative AI is leveraged to synthesize unlimited
conditioned traffic and driving data in simulations for improving driving
safety and traffic efficiency. First, we propose a multi-task DT offloading
model for the reliable execution of heterogeneous DT tasks with different
requirements at RSUs. Then, based on the preferences of AV's DTs and collected
realistic data, virtual simulators can synthesize unlimited conditioned driving
and traffic datasets to further improve robustness. Finally, we propose a
multi-task enhanced auction-based mechanism to provide fine-grained incentives
for RSUs in providing resources for autonomous driving. The property analysis
and experimental results demonstrate that the proposed mechanism and
architecture are strategy-proof and effective, respectively
Automatizuotų transporto priemonių valdymas: civilinės atsakomybės reglamentavimas Europos Sąjungoje
The aim of this article is to provide with the option of civil liability regulation of connected autonomous vehicles (CAVs) and autonomous vehicles (AVs) at the European Union level in the light of introduction of Connected Automated Driving (CAD) on the common market.Šio straipsnio tikslas – pasiūlyti automatizuotų transporto priemonių (ATP) civilinės atsakomybės reglamentavimą Europos Sąjungos dimensijoje atsižvelgiant į sukurtą automatizuotų transporto priemonių valdymą ES vidaus rinkoje
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