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Exploring data protection challenges of automated driving
بررسی چالش های حفاظت از داده ها در رانندگی خودکار-2020 With the increase in automation of vehicles and the rise of driver monitoring systems in
those vehicles, data protection becomes more relevant for the automotive sector. Monitoring
systems could contribute to road safety by, for instance, warning the driver if he is dozing off.
However, keeping such a close eye on the user of the vehicle has legal implications. Within
the European Union, the data gathered through the monitoring system, and the automated
vehicle as a whole, will have to be collected and processed in conformity with the General
Data Protection Regulation. By means of a use case, the different types of data collected by
the automated vehicle, including health data, and the different requirements applicable to
the collecting and processing of those types of data are explored. A three-step approach to
ensuring data protection in automated vehicles is discussed. In addition, the possibilities
to ensure data protection at a European level via the (type-) approval requirements will be
explored.
Keywords: Data protection | GDPR | Automated driving |
مقاله انگلیسی |
2 |
Digital evidence in fog computing systems
شواهد دیجیتال در سیستم های محاسباتی مه-2020 Fog Computing provides a myriad of potential societal benefits: personalized healthcare, smart cities, automated vehicles, Industry 4.0, to name just a few. The highly dynamic and complex nature of Fog Computing with its low latency communication networks connecting sensors, devices and actuators facilitates ambient computing at scales previously unimaginable. The combination of Machine Learning, Data Mining, and the Internet of Things, sup- ports endless innovation in our data driven society. Fog computing incurs new threats to security and privacy since these become more difficult when there are an increased number of connected devices, and such devices (for example sensors) typically have limited capacity for in-built security. For law enforcement agencies, the existing models for digital forensic investigations are ill suited to the emerging fog paradigm. In this paper we examine the procedural, technical, legal, and geopolitical challenges associated with digital forensic investigations in Fog Computing. We highlight areas that require further development, and posit a framework to stimulate further consideration and discussion around the challenges associated with extracting digital evidence from Fog Computing systems.© 2021 R. Hegarty and M. Taylor. Published by Elsevier Ltd. All rights reserved. Keywords: Digital evidence | Fog computing | Cyber crime |
مقاله انگلیسی |
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Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach
کاهش مشترک نوسانات ترافیک و بهبود مصرف انرژی با وسایل نقلیه الکتریکی ، متصل و خودکار: یک رویکرد مبتنی بر یادگیری تقویتی-2020 It has been well recognized that human driver’s limits, heterogeneity, and selfishness substantially compromise
the performance of our urban transport systems. In recent years, in order to deal with these deficiencies, our
urban transport systems have been transforming with the blossom of key vehicle technology innovations, most
notably, connected and automated vehicles. In this paper, we develop a car following model for electric, connected
and automated vehicles based on reinforcement learning with the aim to dampen traffic oscillations
(stop-and-go traffic waves) caused by human drivers and improve electric energy consumption. Compared to
classical modelling approaches, the proposed reinforcement learning based model significantly reduces the
modelling constraints and has the capability of self-learning and self-correction. Experiment results demonstrate
that the proposed model is able to improve travel efficiency by reducing the negative impact of traffic oscillations,
and it can also reduce the average electric energy consumption. Keywords: Electric vehicles | Connected and automated vehicles | Car following | Machine learning | Reinforcement learning | Deep Deterministic Policy Gradient | Traffic oscillations | Energy consumption |
مقاله انگلیسی |
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Automated vehicle’s behavior decision making using deep reinforcement learning and high-fidelity simulation environment
تصمیم گیری خودکار وسیله نقلیه با استفاده از یادگیری تقویتی عمیق و محیط شبیه سازی با وفاداری بالا-2019 Automated vehicles (AVs) are deemed to be the key element for the intelligent transportation
system in the future. Many studies have been made to improve AVs’ ability of environment
recognition and vehicle control, while the attention paid to decision making is not enough and
the existing decision algorithms are very preliminary. Therefore, a framework of the decisionmaking
training and learning is put forward in this paper. It consists of two parts: the deep
reinforcement learning (DRL) training program and the high-fidelity virtual simulation environment.
Then the basic microscopic behavior, car-following (CF), is trained within this framework.
In addition, theoretical analysis and experiments were conducted to evaluate the proposed
reward functions for accelerating training using DRL. The results show that on the premise
of driving comfort, the efficiency of the trained AV increases 7.9% and 3.8% respectively compared
to the classical adaptive cruise control models, intelligent driver model and constant-time
headway policy. Moreover, on a more complex three-lane section, we trained an integrated
model combining both CF and lane-changing behavior, with the average speed further growing
2.4%. It indicates that our framework is effective for AV’s decision-making learning. Keywords: Automated vehicle | Decision making | Deep reinforcement learning | Reward function |
مقاله انگلیسی |
5 |
Parsimonious shooting heuristic for trajectory design of connected automated traffic part II: Computational issues and optimization
تیراندازی پارسییمونیک مکاشفه ای برای طراحی مسیر ترافیک خودکار متصل شده بخش دوم: مسائل محاسباتی و بهینه سازی-2017 Advanced connected and automated vehicle technologies enable us to modify driving be
havior and control vehicle trajectories, which have been greatly constrained by human lim
its in existing manually-driven highway traffic. In order to maximize benefits from these
technologies on highway traffic management, vehicle trajectories need to be not only con
trolled at the individual level but also coordinated collectively for a stream of traffic. As
one of the pioneering attempts to highway traffic trajectory control, Part I of this study
(Zhou et al., 2016) proposed a parsimonious shooting heuristic (SH) algorithm for con
structing feasible trajectories for a stream of vehicles considering realistic constraints in
cluding vehicle kinematic limits, traffic arrival patterns, car-following safety, and signal op
erations. Based on the algorithmic and theoretical developments in the preceding paper,
this paper proposes a holistic optimization framework for identifying a stream of vehicle
trajectories that yield the optimum traffic performance measures on mobility, environment
and safety. The computational complexity and mobility optimality of SH is theoretically an
alyzed, and verifies superior computational performance and high solution quality of SH.
A numerical sub-gradient-based algorithm with SH as a subroutine (NG-SH) is proposed to
simultaneously optimize travel time, a surrogate safety measure, and fuel consumption for
a stream of vehicles on a signalized highway section. Numerical examples are conducted to
illustrate computational and theoretical findings. They show that vehicle trajectories gen
erated from NG-SH significantly outperform the benchmark case with all human drivers at
all measures for all experimental scenarios. This study reveals a great potential of trans
formative trajectory optimization approaches in transportation engineering applications. It
lays a solid foundation for developing holistic cooperative control strategies on a general
transportation network with emerging technologies.
Keywords: Connected vehicles | Automated vehicles | Traffic smoothing | Trajectory optimization | Traffic signal | Shooting heuristic | Customized numerical-gradient heuristic | Expedited objective evaluation |
مقاله انگلیسی |
6 |
Distributed MPC for cooperative highway driving and energy-economy validation via microscopic simulations
MPC توزیع شده برای رانندگی بزرگراه های تعاونی و اعتبار سنجی اقتصاد انرژی با استفاده از شبیه سازی های میکروسکوپی-2017 Traffic congestion and energy issues have set a high bar for current ground transportation
systems. With advances in vehicular communication technologies, collaborations of con
nected vehicles have becoming a fundamental block to build automated highway trans
portation systems of high efficiency. This paper presents a distributed optimal control
scheme that takes into account macroscopic traffic management and microscopic vehicle
dynamics to achieve efficiently cooperative highway driving. Critical traffic information
beyond the scope of human perception is obtained from connected vehicles downstream
to establish necessary traffic management mitigating congestion. With backpropagating
traffic management advice, a connected vehicle having an adjustment intention exchanges
control-oriented information with immediately connected neighbors to establish potential
cooperation consensus, and to generate cooperative control actions. To achieve this goal, a
distributed model predictive control (DMPC) scheme is developed accounting for driving
safety and efficiency. By coupling the states of collaborators in the optimization index, con
nected vehicles achieve fundamental highway maneuvers cooperatively and optimally. The
performance of the distributed control scheme and the energy-saving potential of conduct
ing such cooperation are tested in a mixed highway traffic environment by the means of
microscopic simulations.
Keywords: Distributed optimal control | Connected and automated vehicles | Energy saving | Microscopic traffic simulation |
مقاله انگلیسی |
7 |
Auction-based tolling systems in a connected and automated vehicles environment: Public opinion and implications for toll revenue and capacity utilization
سیستم های تلفنی مبتنی بر مزایده در محیط وسایل نقلیه متصل و خودکار: افکار عمومی و پیامدهای درآمد عوارض و استفاده از ظرفیت-2017 Autonomous and connected vehicles are expected to enable new tolling mechanisms, such
as auction-based tolls, for allocating the limited roadway capacity. This research examines
the public perception of futuristic auction-based tolling systems, with a focus on the public
acceptance of such systems over current tolling practices on highways (e.g., dynamic and
fixed tolling methodologies). Through a stated-preference survey, responses from 159
road-users residing in Virginia are elicited to understand route choice behavior under a
descending price auction implemented on a hypothetical two-route network. Analysis of
the survey data shows that there is no outright rejection of the presented auction-based
tolling among those who are familiar with the current tolling methods. While males
strongly support the new method, no clear pattern emerges among other demographic
variables such as income and education level, and age. While high income respondents
and regular commuters are more likely to pay higher tolls, no statistical significance
between different genders, age groups, household sizes, and education levels is found.
Based on the modeling results and the hypothetical road network, it is found that descend
ing price tolling method yields higher average toll rates, and generates at least 70% more
revenue when travel time saving is 30 min, and improves capacity utilization of the toll
road significantly compared to fixed tolls.
Keywords: Auction-based tolling | Connected vehicles | Autonomous vehicles | Online survey | Public attitudes | Choice models |
مقاله انگلیسی |
8 |
Dynamic adaptive policymaking for the sustainable city: The case of automated taxis
سیاست تطبیقی پویا برای شهر پایدار: مورد تاکسی خودکار-2017 By 2050, about two-thirds of the world’s people are expected to live in urban areas. But, the
economic viability and sustainability of city centers is threatened by problems related to
transport, such as pollution, congestion, and parking. Much has been written about automated vehicles and demand responsive transport. The combination of these potentially disruptive developments could reduce these problems. However, implementation is held
back by uncertainties, including public acceptance, liability, and privacy. So, their potential
to reduce urban transport problems may not be fully realized. We propose an adaptive
approach to implementation that takes some actions right away and creates a framework
for future actions that allows for adaptations over time as knowledge about performance
and acceptance of the new system (called ‘automated taxis’) accumulates and critical
events for implementation take place. The adaptive approach is illustrated in the context
of a hypothetical large city
Keywords: Deep uncertainty | Dynamic adaptive policymaking | Demand responsive transport | Automated taxis |
مقاله انگلیسی |
9 |
Reducing drivers behavioural uncertainties using an interdisciplinary approach: Convergence of Quantified Self, Automated Vehicles, Internet Of Things and Artificial Intelligence:
کاهش ریسک های ناشی از رفتار رانندگان با استفاده از یک رویکرد بین رشته ای: همگرایی از میزان خود، وسایل نقلیه خودکار، اینترنت اشیا و هوش مصنوعی-2016 Growing research progress in Internet of Things (IoT), automated/connected cars, Artificial
Intelligence and person’s data acquisition (Quantified Self) will help to reduce behavioral uncertainties in
transport and unequivocally influence future transport landscapes. This vision paper argues that by
capitalizing advances in data collection and methodologies from emerging research disciplines, we could
make the driver amenable to a knowable and monitorable entity, which will improve road safety. We
present an interdisciplinary framework, inspired by the Safe system, to extract knowledge from the large
present an interdisciplinary framework, inspired by the Safe system, to extract knowl
amount of available data during driving. The limitation of our approach is discussed.
Keywords: automated driving | quantified self | internet of things | artificial intelligence |
مقاله انگلیسی |