Future Of Autonomous Vehicles Research Articles

Modeling the operational and labor costs of autonomous robotaxi services

Autonomous vehicle (AV) companies are rolling out driverless taxi-type services in cities around the world, promising to enhance road safety, promote equity, and foster environmental sustainability while simultaneously threatening the jobs of current taxi and ride-hailing drivers. The potential impacts of these “robotaxi” services hinge upon not only whether AV technology becomes sufficiently performant, but also whether AV services become economically competitive. In this study, we develop ground-up cost models for a traditional taxi and a robotaxi service to compare their relative competitiveness. We draw on direct observations of commercially available robotaxi services, semi-structured interviews with AV operational and regulatory experts (N = 27), and archival documents to include the most detailed accounting of frontline robotaxi labor roles to date. We find that labor remains a significant cost for existing robotaxi services but that robotaxi operating costs are still lower than those of traditional taxi services. Ultimately, utilization rates and annual mileage will serve as the most influential factors for robotaxi competitiveness. Finally, if jobs shift from traditional taxi to robotaxi services, the total number of frontline jobs could decrease by between 57% and 76%, but the distribution of worker wages would shift higher. Transportation planners, researchers, and policymakers should continue accounting for labor costs in an AV future, as these costs will influence where AV deployment and job losses are (and are not) likely to occur, and should proactively investigate alternative career paths for potentially displaced frontline workers. Robotaxi costs are highly sensitive to lower labor ratios (i.e., fewer vehicles per worker), but firms reach economies of scale relatively quickly. Robotaxi firms should weigh marginal labor cost reductions against impacts to passengers and workers.

Multidimensional Analysis of Willingness to Share Rides in a Future of Autonomous Vehicles

A sustainable transportation future is one in which people eschew personal car ownership in favor of using autonomous vehicle (AV)-based ridehailing services in a shared mode. However, the traveling public has historically shown a disinclination toward sharing rides and carpooling with strangers. In a future of AV-based ridehailing services, it will be necessary for people to embrace both AVs as well as true ridesharing to fully realize the benefits of automated and shared mobility technologies. This study investigated the factors influencing willingness to use AV-based ridehailing services in the future in a shared mode (i.e., with strangers). This was done through the estimation of a behavioral model system on a comprehensive survey data set that included rich information about attitudes, perceptions, and preferences pertaining to the adoption of AVs and shared mobility modes. The model results showed that current ridehailing experiences strongly influenced the likelihood of being willing to ride AV-based services in a shared mode. Campaigns that provide opportunities for individuals to experience such services firsthand would potentially go a long way to enabling a shared mobility future at scale. In addition, several attitudinal variables were found to strongly influence the adoption of future mobility services; these findings provide insights on the likely early adopters of shared autonomous mobility services and the types of educational awareness campaigns that may effect change in the prospects of such services.

A Review Paper on Development of e-Vehicles

Abstract: In this paper, we present a detailed chronological analysis of the achievements in autonomous automation, which is an area of interest for researchers. This paper can be useful for understanding the trends in autonomous vehicletechnology in the past, present, and future. The technology of autonomous vehicles has drastically changed since the 1920s, when radio controlled vehicles first appeared. Electric cars powered by embedded circuits on the road became fairly autonomous in the next few decades. By the 1960s, autonomous cars had similar electronic guide systems. The 1980s saw the introduction of vision guided autonomous vehicles, a major milestone in technology that we continue to use today. In modern cars, semi-autonomous features such as lane keeping,automatic braking, and adaptive cruise control are based on these systems. An extensive network of visionguided systems is the future of autonomous vehicles. It is predicted that the majority of companieswill launch fully autonomous vehicles by the turn of the century. We are entering an era of safe, comfortable, and efficient transportation with autonomous vehicles.

Traffic Prediction for Intelligent Transportation System using Machine Learning

This paper aims to develop a tool for predicting accurate and timely traffic flow Information. Traffic Environment involves everything that can affect the traffic flowing on the road, whether it’s traffic signals, accidents, rallies, even repairing of roads that can cause a jam. If we have prior information which is very near approximate about all the above and many more daily life situations which can affect traffic then, a driver or rider can make an informed decision. Also, it helps in the future of autonomous vehicles. In the current decades, traffic data have been generating exponentially, and we have moved towards the big data concepts for transportation. Available prediction methods for traffic flow use some traffic prediction models and are still unsatisfactory to handle real-world applications. This fact inspired us to work on the traffic flow forecast problem build on the traffic data and models. It is cumbersome to forecast the traffic flow accurately because the data available for the transportation system is insanely huge. In this work, we planned to use machine learning, genetic, soft computing, and deep learning algorithms to analyse the big-data for the transportation system with much-reduced complexity. Also, Image Processing algorithms are involved in traffic sign recognition, which eventually helps for the right training of autonomous vehicles.

AIM5LA: A Latency-Aware Deep Reinforcement Learning-Based Autonomous Intersection Management System for 5G Communication Networks.

The future of Autonomous Vehicles (AVs) will experience a breakthrough when collective intelligence is employed through decentralized cooperative systems. A system capable of controlling all AVs crossing urban intersections, considering the state of all vehicles and users, will be able to improve vehicular flow and end accidents. This type of system is known as Autonomous Intersection Management (AIM). AIM has been discussed in different articles, but most of them have not considered the communication latency between the AV and the Intersection Manager (IM). Due to the lack of works studying the impact that the communication network can have on the decentralized control of AVs by AIMs, this paper presents a novel latency-aware deep reinforcement learning-based AIM for the 5G communication network, called AIM5LA. AIM5LA is the first AIM that considers the inherent latency of the 5G communication network to adapt the control of AVs using Multi-Agent Deep Reinforcement Learning (MADRL), thus obtaining a robust and resilient multi-agent control policy. Beyond considering the latency history experienced, AIM5LA predicts future latency behavior to provide enhanced security and improve traffic flow. The results demonstrate huge safety improvements compared to other AIMs, eliminating collisions (on average from 27 to 0). Further, AIM5LA provides comparable results in other metrics, such as travel time and intersection waiting time, while guaranteeing to be collision-free, unlike the other AIMs. Finally, compared to other traffic light-based control systems, AIM5LA can reduce waiting time by more than 99% and time loss by more than 95%.

Testing Emergent Technologies in the Arctic

There are great expectations around the future of autonomous vehicles (AVs). Such visions often picture vehicles that work everywhere without human interference. In this article we use empirical data from a pilot project taking place in the Norwegian Arctic to explore the place-specificity of such technologies. The case study is used to demonstrate how new configurations of emergent technologies are shaped by the places where the trial unfolds; and how insights produced through working on and with this site contribute to changing visions of AV technologies into questioning issues of transferability and scalability. In this way, the paper contributes to discussions of how pilot projects and testing of emergent technologies in the real world relates to the re-configuring of visions and expectations. The paper highlights how emerging technologies might transform societies, infrastructures, and vehicles towards more computerized configurations in ways that are not anticipated, discussed in public and therefore seldom governed.

The Future of Autonomous Vehicles in the Opinion of Automotive Market Users

Contemporary trends are focused on the development of the so-called smart, connected and multimedia cars as well as actions in the field of driving autonomy, and these trends may lead to changes in the structure of the industry through the emergence and growth of the importance of new entities. The article presents the concept of autonomous vehicles (AVs) and the way it is perceived by users of traditional cars. Surveys were carried out in various age groups on the possibilities of developing AVs in Poland. The group of respondents were inhabitants of a rural area, small towns and cities with over 300,000 inhabitants. Based on our own research, it can be concluded that, due to many different factors, including costs, legal regulations and conviction, among others, AVs will not appear so soon in common use on Polish roads. The results of the research showed that the majority of respondents consider hybrid vehicles (HVs) and then electric vehicles (EVs) to be the dominant type of vehicles in the near future in Poland, at the same time pointing at the long process of adopting AV technology.

Assessing the Spatial Implications of Autonomous Vehicles as Feeders to Railway Stations in Suburban Melbourne

ABSTRACT Using results from a design-research studio analysing rail station access in Melbourne, this article considers how differing Autonomous Vehicle (AV) deployment models might fit into a suburban landscape. The results challenge assumptions about AV deployment. If AVs are low occupancy, peak demands may require station carparks to be used entirely as pick-up/drop off zones. This suggests little opportunity to re-purpose car parks for non-transport uses. Further, with AVs uncoupling station demand from existing feeder bus and park-and-ride provision, a re-distribution of demand along rail corridors may occur. The work has broad applicability to the policy challenges of an AV future.

The Future of Autonomous Vehicles: Lessons from the Literature on Technology Adoption

AbstractThe introduction and adoption of autonomous vehicles (AVs) will likely reshape the transportation system and many economic activities. The economic literature on technology adoption, based on studies in agriculture and other sectors, provides lessons on the diffusion of AVs and its social and economic impacts. We rely on the threshold model of diffusion, where heterogeneous agents make decisions pursuing their self‐interests. Applications of the threshold model point to case studies of other technologies where one can gain information and make predictions about the future of AVs. We find that private ownership of AVs may prevail after a transition period, as was the case in other technologies like computers, tractors, and conventional vehicles. With technological progress, the cost of privately owning AVs may decline. Further, there will be an increase in vehicle miles traveled (VMT) per capita, there may be more vehicles on the road, and perhaps the transportation user‐base will expand to include those currently facing limited mobility. Congestion is likely to depend on the tradeoff between the expansion of VMT and increased efficiency of AVs to communicate and help regulate traffic. Furthermore, differentiation of vehicles will increase as driving time becomes freed for other activities. These trends may lead to increased greenhouse gas emissions and expansion of the transportation sector. Finally, the technology will evolve and may result in complementary innovations needing to be addressed, including the “last 10 feet” problem. It is evident that the future of the transportation system governed by AVs is most likely not going to be sustainable. This necessitates the importance of developing and enforcing rigorous policies at the metropolitan level and TNC levels to ensure a sustainable evolution of the future of transportation mobility.

On investigating the potential effects of private autonomous vehicle use on home/work relocations and commute times

The current study is motivated by the need to better understand the potential impacts that vehicular automation may have on individual decisions of residential and work relocation in a future autonomous vehicle (AV) scenario. The study employs a multivariate approach to model five behavioral dimensions simultaneously: (1) technology-savviness (TS) propensity, (2) interest in productive use of travel time (IPTT) propensity, (3) interest in work relocation, (4) interest in residential relocation, and (5) tolerance to an increase in commute travel time. Data from a web-based survey of commuters in 2017 in the Dallas-Fort Worth Metropolitan Area (DFW) is employed. The results show that both TS and IPTT, as well as demographic variables, impact relocation decisions when individuals have a private AV available for their commute. Importantly, there is considerable heterogeneity across individuals in the willingness to relocate and/or accept longer commute times in an AV future. As such, our model results may be used to inform inputs to land use and travel demand models in an AV future. Also, our results suggest that the magnitude of value of travel time savings (VTTS) decrease considered in many earlier AV impact simulation studies may be much higher than reality. Relative to 50% and even 100% VTTS decreases assumed in many studies, our results suggest a much more modest 30% or so overall decrease in VTTS because of the ability to commute in a privately-owned AV. Finally, our results do predict a rather substantial extent of urban sprawl due to AVs, potentially up to a 68% increase in the horizontal spread of cities such as Dallas-Fort Worth, unless proactive planning and policies are implemented to avert such consequences of AVs.

Planning for walking and cycling in an autonomous-vehicle future

Over the last few decades, walking and cycling have increased in the United States, especially in large cities. Future efforts to promote active travel will occur during a time when automated vehicles will increasingly perform driving tasks without human input. Little is known about impacts of an automated vehicle fleet on pedestrians and cyclists.This study uses semi-structured interviews with experts from academia as well as the public and private sectors in the United States to (1) explore potential synergies and conflicts between increasingly automated motorized vehicles and active travel; and (2) highlight planning and policy priorities for active travel in a time of emerging connected and automated vehicles (C/AVs).Our interviews indicate that while C/AVs promise to make roadways safer for motorists, cyclists, and pedestrians, some potential hazards exist related to communication, behavior, and technical capabilities in the near term. In the long-term, C/AVs may have drastic impacts on infrastructure, the built environment, and land use, but these impacts are likely to vary by locality. Federal and state governments will play a role in ensuring that connected and automated vehicles operate safely, but local governments will ultimately determine how automated vehicles are integrated into the transportation network.

Modeling individuals’ willingness to share trips with strangers in an autonomous vehicle future

With the era of fully automated vehicles (AVs) quickly approaching, ridesharing services could have an important role in increasing vehicle occupancy, reducing vehicle miles traveled, and improving traffic conditions. However, the extent to which these potentials can be achieved depends on consumers’ disposition to sharing rides. From a travel behavior perspective, two essential elements to the adoption of shared rides are individuals’ acceptance of increased travel times associated with pick-up/drop-off of other passengers and their approval of strangers sharing the same vehicle. The current study develops the notion of willingness to share (WTS), which represents the money value attributed by an individual to traveling alone compared to riding with strangers, to investigate the adoption of shared rides. Using a multivariate integrated choice and latent variable approach, we examine current choices and future intentions regarding the use of shared rides and estimate individuals’ WTS as well as their values of travel time for two distinct trip purposes. Results show that users are less sensitive to the presence of strangers when in a commute trip compared to a leisure-activity trip. We also observe that the travel time added to the trip to serve other passengers may be a greater barrier to the use of shared services compared to the presence of a stranger. However, the potential to use travel time productively may help overcome this barrier especially for high-income individuals.

The Optimal Agent: The Future of Autonomous Vehicles & Liability Theory

Autonomous Vehicles (“AVs”) are rapidly disrupting the $4 trillion auto industry. Indeed, questions surrounding AV regulation are some of the most important to be answered in the Twenty-First Century. Yet, legislators have yet to address or even identify some of the most critical issues relating to AV regulation. This paper explains the unique issues that deep reinforcement learning systems pose for AV technology, policy, and law. Additionally, this paper identifies two important regulatory problems that legislators and scholars need to address in the context of AV development. Legal scholars have made clear that there is a demanding need for some sort of regulatory system for AVs. However, those arguments focus on short term regulation and generally misunderstand the evolutionary rate of AV technology. This paper takes an informatics based approach to analyzing issues in AV regulation with a specific emphasis on the technical aspects of AV systems. Further, this paper discusses and explains the formal models that are currently being used as a foundation for AV development. Ultimately, AV technology will change the way humans move throughout the world and legislators must prepare immediately for the endeavor ahead in regulating AVs.

Impact of Autonomous Vehicles on Traffic Management: Case of Dynamic Lane Reversal

As the future of autonomous vehicles (AVs) becomes more certain, transport network managers may seek ways to reinvent elements of the traffic network to improve efficiency. One possibility is dynamic lane reversal, in which the network operator makes use of AV communications and behavior to change the direction of flow on a road link at smaller time intervals than would be possible with human drivers. Although there is much research into the mechanical details of AVs, this study motivates the need for future research by focusing on a planning application in which AVs are already present. A novel extension to an established system optimal dynamic traffic assignment model based on the cell transmission model was examined. The model determined the optimal lane configuration at small space–time intervals. Results demonstrate the model on a single link and a grid network and explore the dynamic demand scenarios that are most conducive to increasing system efficiency with dynamic lane reversal.