Integration Of Autonomous Vehicles Research Articles

Through the eyes of Autonomous Vehicles – using laser scanning technology to engage the public via the analysis of journeys seen from a different perspective.

It is likely that Autonomous Vehicles (AVs) will have significant social, cultural, spatial and environmental implications and the interaction between humans, automated vehicles and physical environment will provide an array of challenges. This paper aims to explore use of innovative visualisation approaches, to communicate and foster discussion to anticipate possible scenarios involving AVs. It is argued that such an approach might be used to help conceptualise human experiences with the potential to enhance public engagement and understanding of the complex human-machine associations and open a dialogue with potential end users.Presenting the journeys from different perspectives and reconceptualising the context through the eyes of AVs emphasised the nuances of experience between the machines, urban space and human bodies. Unexpected user-technology interactions and experiences will emerge as humans are not always sensible and passive followers and can be apprehensive when it comes to accepting such a novel technology as self-driving vehicles on the roads.The focus applied in the methodology and data capture was on inclusivity of data, and an aspiration to capture not only movement but also noise and human experience of a space. The integration of AVs on public roads will rely on technical innovation to ensure that vehicles can safely operate in a practical sense yet, the study of the perceptual and ethical effects of new technology and potential influences on society via engaging the general public in the process will help to manage expectations and create platforms for mutual learning.

Regulatory approaches to managing artificial intelligence systems in autonomous vehicles in Australia

Abstract This article explores the emergence of Automated Vehicles (AVs) in Australia. It will investigate the legal and regulatory terrain. International and domestic approaches are examined to determine potential responses. The regulatory issues emerge partly due to the varied nature of artificial intelligence systems and processes that enable AVs to function. The variations may be due to the chosen domain model, software development processes, or the development of biases that may occur during code development for the underlying artificial intelligence system. Such variations can create difficulties in the application of road rules, safety requirements, and the legal and regulatory requirements. They may give rise to significant issues relating to driver classification and licensing for AVs, due to the varied levels of control and involvement in the driving process. For this reason, legislative reform at specific jurisdictional levels is suggested together with clearer international standards as a pathway to ensure the safe and effective integration of autonomous vehicles into society.

A INFLUÊNCIA DA INDÚSTRIA 4.0 NA LOGÍSTICA AUTOMOTIVA

Industry 4.0 signifies a major revolution in automotive logistics, driven by the digitalization and automation of industrial processes. This article analyzes the definition and evolution of Industry 4.0, highlighting its core principles and the technologies that underpin it, such as the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI). The application of these technologies offers numerous benefits in automotive logistics, including real-time traceability, predictive analytics for supply chain optimization, and automation of operations through collaborative robotics. Additionally, the article explores innovative trends shaping the future of automotive logistics, such as the integration of autonomous vehicles into the supply chain, the use of 3D printing for parts manufacturing, and the development of smart warehouses. These advancements not only enhance efficiency and reduce costs but also create new business opportunities and operational models. However, the implementation of Industry 4.0 in automotive logistics faces significant challenges, including data security issues and the need for workforce training. The article includes case studies, such as that of Tesla, which exemplify success in the practical integration of these technologies. In conclusion, the article reflects on the future prospects of automotive logistics, emphasizing that the ongoing adoption of Industry 4.0 technologies will be crucial for competitiveness and innovation within the sector.

Agent-Based Framework for Self-Organization of Collective and Autonomous Shuttle Fleets

The mobility of people is at the center of transportation planning and decision-making of the cities of the future. In order to accelerate the transition to zero-emissions and to maximize air quality benefits, smart cities are prioritizing walking, cycling, shared mobility services and public transport over the use of private cars. Extensive progress has been made in autonomous and electric cars. Autonomous Vehicles (AV) are increasingly capable of moving without full control of humans, automating some aspects of driving, such as steering or braking. For these reasons, cities are investing in the infrastructure and technology needed to support connected, multi-modal transit networks that include shared electric Autonomous Vehicles (AV). The relationship between traditional public transport and new mobility services is in the spotlight and need to be rethought. This paper proposes an agent-based simulation framework that allows for the creation and simulation of mobility scenarios to investigate the impact of new mobility modes on a city daily life. It lets traffic planners explore the cooperative integration of AV using a decentralized control approach. A prototype has been implemented and validated with data of the city of Trento.

Travel Behavior Modeling: Taxonomy, Challenges, and Opportunities

Personal daily movement patterns have a longitu-dinal impact on the individual’s decision-making in traveling. Recent observation on human travel raises concerns on the impact of travel behavior changes on many aspects. Many travel-related aspects like traffic congestion management and effective land-use were significantly affected by travel behavior changes. Existing travel behavior modeling (TBM) were focusing on assessing traffic trends and generate improvement insights for urban planning, infrastructure investment, and policymaking. However, literature indicates limited discussions on recent TBM adaptation towards future technological advances like the integration of autonomous vehicles and intelligent traveling. This survey paper aims to provide overview insights on recent advances of TBM including notable classifications, emerging challenges, and rising opportunities. In this survey, we reviewed and analyzed recently published works on TBM from high-quality publication sources. A taxonomy was devised based on notable characteristics of TBM to guide the classification and analysis of these works. The taxonomy classifies recent advances in TBM based on type of algorithms, applications, data sources, technologies, behavior analysis, and datasets. Furthermore, emerging research chal-lenges and limitations encountered by recent TBM studies were characterized and discussed. Subsequently, this survey identified and highlights open issues and research opportunities arise from recent TBM advances for the future undertaking.

Autonomous and conventional bus fleet optimization for fixed-route operations considering demand uncertainty

The emerging technology of autonomous vehicles has been widely recognized as a promising urban mobility solution in the future. This paper considers the integration of autonomous vehicles into bus transit systems and proposes a modeling framework to determine the optimal bus fleet size and its assignment onto multiple bus lines in a bus service network considering uncertain demand. The mixed-integer stochastic programming approach is applied to formulate the problem. We apply the sample average approximation (SAA) method to solve the formulated stochastic programming problem. To tackle the nonconvexity of the SAA problem, we first present a reformulation method that transforms the problem into a mixed-integer conic quadratic program (MICQP), which can be solved to its global optimal solution by using some existing solution methods. However, this MICQP based approach can only handle the small-size problems. For the cases with large problem size, we apply the approach of quadratic transform with linear alternating algorithm, which allows for efficient solution to large-scale instances with up to thousands of scenarios in a reasonable computational time. Numerical results demonstrate the benefits of introducing autonomous buses as they are flexible to be assigned across different bus service lines, especially when demand uncertainty is more significant. The introduction of autonomous buses would enable further reduction of the required fleets and total cost. The model formulation and solution methods proposed in this study can be used to provide bus transit operators with operational guidance on including autonomous buses into bus services, especially on the autonomous and conventional bus fleets composition and allocation.

Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle

Autonomous vehicles (AVs) can potentially compensate for human error whilst driving. Thus, the number of accidents and the amount of congestion, emissions and fuel consumption could be reduced. For such reduction to occur, insights into public acceptance of AVs must be considered because they are a crucial factor for the successful implementation of AVs in existing traffic systems. PurposeThis study aims to explore (1) the mobility behaviour of current passengers, (2) the perception of the public of how AVs can be applied, (3) the perceived challenges of AVs in terms of integration into existing traffic systems, (4) the general attitude of the public towards AVs and (5) the perceived safety of passengers after riding an autonomous shuttle with a maximum SAE level of 3 in the setting of mixed traffic. MethodThis qualitative study was conducted in Carinthia (Austria) in September 2018. Interviews were conducted with 19 participants aged between 20 and 75 years. The participants were recruited through purposeful sampling, and data were collected through in-depth, semistructured interviews which were recorded, transcribed, organised and analysed using systematic, qualitative content analysis. ResultsResults show that transport mobility plays an important role in social life. In rural areas, AVs can shift transportation modes from private cars to public transportation. The respondents perceive AVs as an alternative more than as a substitute for existing means of transportation. With the integration of AVs into existing traffic systems, the transportation of goods and people could be improved despite related uncertainties. This study explores different aspects of societal, technical, legal and economic challenges. Overall, the respondents feel positive about AVs. Responses regarding safety show that experience with AVs and speed are key factors. Originality/ValueThis study is one of the first to explain qualitatively public perceptions of AVs after an actual ride in an AV under real-life conditions. The results, which help understand AVs from the public’s perspective, may also provide guidelines for the successful integration of AVs in Austria.

Joint optimization of customer location clustering and drone-based routing for last-mile deliveries

With growing consumer demand and expectations, companies are attempting to achieve cost-efficient and faster delivery operations. The integration of autonomous vehicles, such as drones, in the last-mile network design can curtail many operational challenges and provide a competitive advantage. This paper deals with the problem of delivering orders to a set of customer locations using multiple drones that operate in conjunction with a single truck. To take advantage of the drone fleet, the delivery tasks are parallelized by concurrently dispatching the drones from a truck parked at a focal point (ideal drone launch location) to the nearby customer locations. Hence, the key decisions to be optimized are the partitioning of delivery locations into small clusters, identifying a focal point per cluster, and routing the truck through all focal points such that the customer orders in each cluster are fulfilled either by a drone or truck. In contrast to prior studies that tackle this problem using multi-phase sequential procedures, this paper presents mathematical programming models to jointly optimize all the decisions involved. We also consider two polices for choosing a cluster focal point - (i) restricting it to one of the customer locations, and (ii) allowing it to be anywhere in the delivery area (i.e., a customer or non-customer location). Since the models considering unrestricted focal points are computationally expensive, an unsupervised machine learning-based heuristic algorithm is proposed to accelerate the solution time. Initially, we treat the problem as a single objective by independently minimizing either the total cost or delivery completion time. Subsequently, the two conflicting objectives are considered together for obtaining the set of best trade-off solutions. An extensive computational study is conducted to investigate the impacts of restricting the focal points, and the influence of adopting a joint optimization method instead of a sequential approach. Finally, several key insights are obtained to aid the logistics practitioners in decision making.

Perceptions of safety on a shared road: Driving, cycling, or walking near an autonomous vehicle

Introduction: While improved safety is a highly cited potential benefit of autonomous vehicles (AVs), at the same time a frequently cited concern is the new safety challenges that AVs introduce. The literature lacks a rigorous exploration of the safety perceptions of road users who will interact with AVs, including vulnerable road users. Addressing this gap is essential because the successful integration of AVs into transportation systems hinges on an understanding of how all road users will react to their presence. Methods: A stated preference survey of the Phoenix, Arizona, metropolitan statistical area (Phoenix MSA) was conducted in July 2018. A series of ordered probit models was estimated to analyze the survey responses and identify differences between various population groups with respect to the perceived safety of driving, cycling, and walking near AVs. Results: Greater exposure to and awareness of AVs are not uniformly associated with increases in perceived safety. Various attitudinal factors, level of AV automation, and other intrinsic and extrinsic factors are related to safety perceptions of driving, walking, and cycling near AVs. Socioeconomic and demographic characteristics, such as gender, age, income, employment, and automobile usage and ownership, have various relationships with perceived safety. Conclusions: Cycling near an AV was perceived as the least safe activity, followed by walking and then driving near an AV. Both similarities and differences were observed among the factors associated with the perceived safety of different travel alternatives. Practical Applications: Public perception will guide the development and adoption of AVs directly and indirectly. To help maintain control of public perception, transportation planners, decision makers, and other stakeholders should consider more deliberate and targeted messaging to address the concerns of different road users. In addition, more careful pilot testing and more direct attention to vulnerable road users may help avoid a backlash that could delay the rollout of this technology.

Integration of autonomous vehicles and Industry 4.0

Integration of autonomous vehicles and Industry 4.0

The Impacts of Autonomous Vehicles and E-Commerce on Local Government Budgeting and Finance

Autonomous vehicles (AVs) are already being used and their proliferation is inevitable. AVs have the potential to fundamentally alter transportation systems by averting deadly crashes, providing critical mobility to the elderly and disabled, increasing road capacity, saving fuel, and lowering emissions (Fagnant and Kockelman 2015). Eighteen states and the District of Columbia have enacted legislation regarding AVs1, and the governors of four other states have signed executive orders about AVs2 (National Conference of State Legislatures 2017). In 2017 there were 33 states that had introduced AV legislation, up from 20 in 2016 (National Conference of State Legislatures 2017). As of June 2, 2017, there were 31 companies that had received permits from the California DMV to test autonomous vehicles, and the list is getting longer each month (California Department of Motor Vehicles 2017). In Berlin, Deutsche Bahn, Germany’s largest train and bus operator, is testing a driverless twelve-passenger shuttle bus (Scott 2017). Over 20 pilot or existing AV public transport programs have taken place in Europe. And the most recent AV testing permit recipient in California is a private shuttle bus operator — Bauer’s Intelligent Transportation (California Department of Motor Vehicles 2017). Much has been written about the technical challenges of integrating autonomous vehicles into traffic patterns, but to date, there has been little consideration of the significant secondary impacts that AVs present. This project aims to fill that gap. AVs have the potential to transform cities — but whether the impact is positive or not depends on how the AVs are used. If AVs use clean fuels, are used for shared rides, and become an on-demand service rather than an owned product, cities and society may benefit. Consumer-owned cars are inefficient and underused assets — most are used for less than one hour per day (OECD 2015), sitting idle for about 95 of their life, and about 10 percent of the average American’s budget goes to the cost of purchasing and fueling private vehicles (Bureau of Labor Statistics 2016). AVs will impact land use planning, transit use, government revenues, and may exacerbate societal inequality by reducing the viability of existing public transportation services. The goal of this white paper is to consider the impact of AVs on municipal budgets. AVs create a “potential rat’s nest of a budgeting challenge” (Fung 2016). This paper seeks to begin the process of untangling that rat’s nest, and provide the foundation for future phases of the project that will consider potential additional revenue sources to fund the infrastructure changes that may come from the integration of AVs as well as land use planning implications.

Data-enabled public preferences inform integration of autonomous vehicles with transit-oriented development in Atlanta

Autonomous vehicles (AVs) have emerged as a transformative technology with the potential to both fundamentally improve lives in cities but also to exacerbate suburban sprawl, vehicle miles traveled and the associated greenhouse gas emissions. Are communities willing to adopt best practices that can lead to early adoption of more sustainable outcomes? This paper presents innovative means to analyze social preferences, demand for AVs, and the potential to resolve community concerns with integrated solutions. We discuss our comprehensive analysis of unstructured and structured data from a survey on AVs that was conducted by the Atlanta Regional Commission in 2015. We used topic modeling to synthesize the “topics” from 1540 comments. The topics captured Atlanta residents' concerns and suggestions about implementing AVs. Further, sentiment analysis revealed people's attitudes on the topics. Accordingly, we proposed an integration of AVs and transit-oriented development (TOD: the development of compact and mixed-use communities around high quality mass transit services within a 10-min walking distance). The second type of data is people's responses to multiple-choice questions about AVs and TOD, which we call structured data. Using latent-class analysis, we identified heterogeneity in preferences for AVs and TOD. More Atlanta residents are willing to live in transit-oriented communities than traditional automobile-dependent ones if AVs save time and improve productivity. This finding portends the future success of combining AVs with TOD and reaping the sustainable benefits of this transformative technology.