Emergence Of Autonomous Vehicles Research Articles

Deep Q‐network learning‐based active speed management under autonomous driving environments

AbstractEfficient traffic safety management necessitates real‐time crash risk prediction using expressway characteristics. With the emergence of autonomous vehicles (AVs), the development and evaluation of variable speed limit (VSL) strategies, a key active traffic management technique, become crucial for enhancing safety and mobility in mixed traffic flows. This underscores the need for optimized VSL strategies to accommodate both conventional and AVs. This paper presents a study on the development of VSL control algorithms using deep reinforcement learning in a microscopic traffic simulation. As the rewards function, time‐to‐collision and speed were considered. To enhance traffic safety, VSL strategies were refined across various market penetration of connected AVs. Analysis revealed that safety and traffic density are improved by 53% and 59%, respectively, in market penetration rate (MPR) 50, marking significant safety improvements in congested and low MPR scenarios. These findings present the importance of developing and evaluating VSL strategies for mixed traffic flow, particularly in the context of increasing the prevalence of connected and AVs.

Autonomous Vehicles Perception, Acceptance, and Future Prospects in the GCC: An Analysis Using the UTAUT-Based Model

The emergence of Autonomous Vehicles (AVs) marks a significant advancement in the automotive industry, transitioning from driver-assistance technologies to fully autonomous systems. This change is particularly impactful in the Gulf Cooperation Council (GCC) region, which is a significant automotive market and technological hub. However, the adoption of AVs in the GCC faces unique challenges due to the influence of cultural norms and geographical characteristics. Our research utilizes a customized framework of the Unified Theory of Acceptance and Use of Technology (UTAUT), which is adapted to include cultural and geographical factors. This approach fills a gap in the existing literature by identifying and analyzing the key factors affecting the adoption of AVs in the GCC. Our findings indicate a difference in the receptiveness towards AVs among different demographics. Younger participants displayed a more favorable attitude towards AVs compared to older individuals. Additionally, gender and educational attainment play significant roles in the acceptance of AVs. Specifically, our results suggest that there are variations in acceptance rates among genders and individuals with varying levels of education. The United Arab Emirates (UAE) has a relatively high acceptance rate of AVs due to its advanced infrastructure and openness to technological innovations. Our study identifies facilitating conditions and performance expectancy as crucial determinants of intention to use AVs in the GCC. It emphasizes the importance of infrastructure readiness and the perceived advantages of AVs in promoting their adoption.

Early Perspectives: Exploring the Potential Impacts of Autonomous Vehicles Through the Lens of Urban Mobility and Urban Form

The emergence of autonomous vehicles (AVs) is expected to significantly reshape urban mobility and travel behavior patterns. AVs have the potential to offer higher levels of convenience, safety, and accessibility while enabling users to spend their trip time on more efficient and productive tasks such as working or even relaxing. This transformation in the short- to mid-term could result in changes in the public's sensitivity and perception toward enduring longer travel times and consequently, in mid- to long-term, it could influence the willingness to reconsider their residential locations. Therefore, the objective of this study is to enhance the understanding of the potential effects of AVs on travel behavior and land use through the examination of stated preference queries. To achieve this objective, various multinomial logit models toward AVs adoption and residential relocation were estimated by employing a dataset from Istanbul, Türkiye, as an example of megacity in a developing country. While the study findings revealed a set of potential adoption barriers for AVs, they also indicated a notable propensity for adopting these vehicles. Furthermore, concerning individuals' willingness to reconsider their residential locations due to the emergence of AVs, a considerable positive attitude was evident, albeit guarded. This attitude suggests that individuals' decisions are still evolving and can be guided toward the desired future through well-timed and well-suited policies. The outcomes of the study can serve as valuable input for policymakers as well as transportation and urban planners, offering insights into the potential impacts of AVs on urban mobility and form.

An improved eco-driving strategy for mixed platoons of autonomous and human-driven vehicles

The emergence of autonomous vehicles (AVs) could transform traffic flow characteristics and affect energy consumption subsequently. The primary objective of this study is to propose an improved eco-driving strategy for the mixed platoons by exploring the impacts of AVs on energy consumption characteristics. Initially, leveraging precise AV control, this study utilizes a Model Predictive Control (MPC) system to optimize AV trajectories, aiming to minimize energy consumption within mixed platoons of AVs and human-driven vehicles (HDVs). Given the differing collaborative capabilities of HDVs and AVs in mixed platoons, exceptional situations may arise at intersections where not all platoon vehicles cross the stop line during the same green light cycle (GLC). To address these challenges, we then propose a splitting approach to optimize the platoon operation mode. This approach allows for flexible splitting decisions based on AVs market penetration and current traffic conditions. The simulation results indicate that the energy consumption reduction ranges from 15.35% to 35.39% and 2.75–3.64% compared to the traditional mode and the eco-driving mode, respectively. In addition, through an analysis of the energy consumption of various vehicle orders within a mixed platoon, we determined that positioning the second AV in the middle platoon maximizes stability, resulting in a more consistent energy consumption pattern. These findings highlight the advantages of the strategy in terms of energy consumption, providing theoretical support management of mixed platoons.

Calculation of the Level of Death Risk in Traffic Accidents Based On Aggregate Loss Costs

The transportation sector is undergoing transformative changes, driven by the emergence of autonomous vehicles and advancements in smart transportation systems. However, these innovations pose novel challenges, particularly in mitigating the risk of fatalities in traffic accidents. This study focuses on estimating death risk in traffic accidents by analyzing aggregate loss costs within the context of traffic safety. Employing a quantitative approach, the research utilizes the Poisson distribution to model the frequency of fatal incidents and the exponential distribution function to depict the distribution of associated losses. The study's objective is to calculate aggregate losses, offering insights into the potential severity of risks. Through a comprehensive analysis, the results affirm the efficacy of the Poisson and exponential distributions in assessing death risk in traffic accidents, with the highest estimated aggregate loss cost reaching Rp3.734.832,09.

Persona-PhysioSync AV: Personalized Interaction through Personality and Physiology Monitoring in Autonomous Vehicles.

The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional "one size fits all" approach to AVs may limit their broader societal, economic, and cultural impact. Here, we introduce the Persona-PhysioSync AV (PPS-AV). It adopts a comprehensive approach by combining personality traits with physiological and emotional indicators to personalize the AV experience to enhance trust and comfort. A significant aspect of the PPS-AV framework is its real-time monitoring of passenger engagement and comfort levels within AVs. It considers a passenger's personality traits and their interaction with physiological and emotional responses. The framework can alert passengers when their engagement drops to critical levels or when they exhibit low situational awareness, ensuring they regain attentiveness promptly, especially during Take-Over Request (TOR) events. This approach fosters a heightened sense of Human-Vehicle Interaction (HVI), thereby building trust in AV technology. While the PPS-AV framework currently provides a foundational level of state diagnosis, future developments are expected to include interaction protocols that utilize interfaces like haptic alerts, visual cues, and auditory signals. In summary, the PPS-AV framework is a pivotal tool for the future of autonomous transportation. By prioritizing safety, comfort, and trust, it aims to make AVs not just a mode of transport but a personalized and trusted experience for passengers, accelerating the adoption and societal integration of autonomous vehicles.

Operations and regulations for a ride-sourcing market with a mixed fleet of human drivers and autonomous vehicles

The rise of autonomous driving may sharply reshape ride-sourcing markets by influencing drivers’ income and participation, customers’ choices, ride-sourcing platforms’ operations, and governments’ regulations. We introduce a model that characterizes a ride-sourcing market with a mixed fleet of non-atomic autonomous vehicles (AVs) and atomic human-driven vehicles (HVs). Human drivers decide whether and where to offer ride-sourcing services independently, while AVs are fully controlled and dispatched by the platform to serve on-demand ride requests that are distributed heterogeneously. We solve for the optimal platform operating strategy and examine the implications of different government regulations on the platform’s optimal decisions and the resulting market equilibrium. Although the emergence of AV might undermine human drivers’ welfare, it can simultaneously increase platform profit, customer surplus, and social welfare. Moreover, by looking into the regulatory outcomes of a few prevailing regulations (i.e., price cap, minimum wage, minimum fleet size, and maximum fleet size), we find that the policymakers should determine the regulations according to the conditions of labor supply and the development stage of AV technology.

Deciphering Autonomous Vehicle Regulations with Machine Learning

The emergence of autonomous vehicles (AVs) presents a transformative shift in transportation, promising enhanced safety and economic efficiency. However, a fragmented legislative landscape across the United States hampers AV deployment. This fragmentation creates significant challenges for AV manufacturers and stakeholders. This research contributes by employing advanced machine learning (ML) techniques to analyze state data, aiming to identify factors associated with the likelihood of passing AV-friendly legislation, particularly regarding the requirement for human backup drivers. The findings reveal a nuanced interplay of socio-economic, political, demographic, and safety-related factors influencing the nature of AV legislation. Key variables such as democratic electoral college votes per capita, port tons per capita, population density, road fatalities per capita, and transit agency needs significantly impact legislative outcomes. These insights suggest that a combination of political, economic, and safety considerations shape AV legislation, transcending traditional partisan divides. These findings offer a strategic perspective for developing a harmonized regulatory approach, potentially at the federal level, to foster a conducive environment for AV development and deployment.

Algorithmic Conscience: An In-Depth Inquiry into Ethical Dilemmas in Artificial Intelligence

Artificial Intelligence (AI) stands at the forefront of technological innovation, promising to reshape industries and improve human lives. However, as AI technologies proliferate, so do the ethical dilemmas they pose. This paper presents a comprehensive review of the ethical considerations surrounding AI, delving into nuanced discussions on privacy, bias, job displacement, autonomous decision-making, and accountability. Drawing on an extensive body of literature, industry reports, and real-world examples, this review elucidates the intricate interplay between technological advancement and societal values. Privacy concerns loom large in the era of AI, as algorithms increasingly rely on vast troves of personal data to fuel their decision-making processes. From social media platforms to healthcare systems, the collection and analysis of sensitive information raise profound questions about consent, data ownership, and individual autonomy. For instance, a study by Acquisti and Grossklags (2006) found that individuals are often unaware of the extent to which their personal data is being used and shared, highlighting the need for robust privacy protections in AI systems. Bias and fairness represent another ethical minefield in the realm of AI, with algorithms often reflecting and amplifying societal prejudices present in training data. Research by Obermeyer et al. (2019) revealed racial bias in a widely used healthcare algorithm, leading to disparities in patient care. Such instances underscore the urgency of addressing bias in AI systems through careful data curation, algorithmic transparency, and community engagement. The specter of job displacement looms large as AI-driven automation threatens to reshape labor markets worldwide. According to a report by the World Economic Forum (2020), an estimated 85 million jobs may be displaced by AI by 2025, with significant implications for income inequality and social stability. Mitigating the adverse effects of automation requires proactive measures, such as investment in education and training programs, as well as policies to ensure a just transition for displaced workers. Autonomous decision-making by AI systems raises complex ethical questions regarding accountability and liability. The opacity of many AI algorithms complicates efforts to attribute responsibility for algorithmic outcomes, particularly in cases of harm or discrimination. For instance, the emergence of autonomous vehicles has sparked debates about moral decision-making in life-or- death scenarios. Ethical frameworks for AI accountability emphasize the importance of transparency, explainability, and human oversight in algorithmic decision-making processes.

Stabilizing traffic flow by autonomous vehicles: Stability analysis and implementation considerations

The emergence of autonomous vehicles (AVs) is expected to revolutionize road transportation and it has been demonstrated that AVs can be employed as mobile actuators to effectively dissipate stop and go waves. This paper proposes a comprehensive theoretical framework including stability and implementation considerations for analyzing traffic platoon control via sparse AVs and discussing the number of HDVs that can be stabilized by one AV. Considering multiple disturbance sources, we first discuss the output error composition and analyze traffic dynamics in a platoon system. Then we discuss the Lyapunov asymptotic stability (LAS) of the system based on eigenvalue analysis and propose a novel ring string stability (RSS), which ensures the boundedness of disturbance error and guarantees that the effect of disturbance will not be amplified over a lap. The proposed RSS is flexible and applicable to mixed traffic, since the string instability for predecessor-follower pairs are allowed, provided they can be compensated by AVs. In addition, the practical constraints including driving safety and vehicle physical limitations are modeled. The worst-case evaluation of constraints is conducted to reproduce the critical scenarios in real life. Finally, we conduct numerical experiments and analyze the traffic platoon control from the aspects of both stability and practical implementations. New findings include: (1) The introduction of AVs is beneficial to dampen the waves and stabilize the HDV flow. (2) Considering the implementation issues, there is an upper bound of HDVs that can be stabilized by one single AV. (3) As the number and amplitudes of exogenous disturbances increase, it will be more challenging for sparse AVs to ensure stability and meanwhile satisfy the practical constraints.

A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management

The emergence of autonomous vehicles and the advancement of technology over the past several decades has increased the demand for intelligent intersection management systems. Since there has been increased interest in researching how autonomous vehicles manage traffic at junctions, a thorough literature analysis is urgently needed. This study discovered peer-reviewed publications published between 2012 and 2022 in the most prestigious libraries to address this problem. After that, 100 primary studies were identified, and the chosen literature was subjected to systematic analysis. According to the findings, there are four primary categories of approaches, i.e., rule-based, optimization, hybrid, and machine learning procedures, which are used to achieve diverse driving objectives, including efficacy, safety, ecological, and passenger ease. The analyses illustrate the many attributes, limits, and views of the current solutions. This analysis enables the provision of potential future difficulties and directions in this study area.

Telemetry System For The Steering System

Telemetry System For The Steering System

Study on mixed traffic of autonomous vehicles and human-driven vehicles with different cyber interaction approaches

The emergence of autonomous vehicles will significantly improve traffic efficiency and safety. Before the fully autonomous driving of traffic system, the mixed traffic with autonomous and human-driven vehicles will be a typical feature for the future road traffic. To study the properties of a mixed traffic mode combining autonomous and human-driven vehicles, a cellular automata model based traffic system considering autonomous and human-driven vehicles is proposed. In the mixed traffic model, human-driven vehicles can use multiple preceding vehicles' information within drivers' visual range for vehicle motion. For autonomous vehicles, the interaction mechanism of information is iterative and the communication range for vehicle-to-vehicle is considered. The results from this study show that the average traffic flow can be enhanced with the increase of communication range for autonomous vehicles. But when the communication range is big enough, its role on traffic flow is little. Also, it is found that human-driven vehicles' behavior of obtaining multiple preceding vehicles' information can significantly impact the meta-stable state of traffic flow in the average flow density phase. Further, the relationship between the average flow and vehicle density of the mixed traffic flow with random distribution, uniform distribution and separate distribution is discussed. And it reveals that the traffic capacity of the mixed traffic flow with separate distribution is more impressive comparing to the traffic capacity with random distribution and uniform distribution. Finally, the traffic congestion rate under different values of penetration rate is verified and it uncovers that the congestion rate is reduced with the increase of penetration rate.

Expert stakeholders' views on the potential nature and impacts of autonomous alcohol home delivery.

The emergence of autonomous vehicles (AV) heralds new methods of alcohol delivery that are likely to have substantial implications for home drinking. The aim of this study was to explore the factors that have the potential to hasten or restrict widescale increases in alcohol availability resulting from AVs. Thirty-six interviews were conducted with 44 stakeholders across diverse sectors. Interviewees discussed their perceptions of how AVs will evolve and the implications for alcohol access. The interview transcripts were analysed to develop a typology of factors that need to be addressed to minimise harms associated with greater home alcohol consumption due to AVs. The interviewees identified a range of demand-side (e.g., enhanced convenience for consumers) and supply-side (e.g., increased efficiency for providers) factors associated with AV home deliveries that could increase the prevalence and volume of home drinking. A third category of factors related to potential forms of regulation: suggested harm-minimisation options related to appropriate enhancements of licensing restrictions and responsible service requirements and the use of various fiscal levers to introduce price barriers. The suggested strategies will require collaborative efforts across government and non-government organisations to address the potential issues arising from the advent of AV alcohol deliveries. Technological and policy solutions will be needed to ensure responsible service of alcohol. This study provides policy makers with insights into forms of intervention that may be required to minimise the harms associated with the use of AVs in alcohol home delivery systems.

Implications of the Emergence of Autonomous Vehicles and Shared Autonomous Vehicles: A Budapest Perspective

The introduction of autonomous vehicles (AVs) and shared autonomous vehicles (SAVs) is projected to enhance network performance and accessibility. The future share distribution of AV and SAV is not yet apparent, nor is which of these two future transport modes will become dominant. Therefore, this research deploys a simulation-based dynamic traffic assignment using Visum software to investigate the impact of varying the share distribution of AVs and SAVs on Budapest’s network performance and consumer surplus in three projected future traffic scenarios for the years 2030 and 2050 compared to the Base scenario for 2020. The three future scenarios are presented and characterized by different penetration rates of AVs and SAVs to reflect the uncertainty in the market share of these future cars as follows: Mix-Traffic scenario for 2030, and AV-Focused and SAV-Focused scenarios for 2050. The results revealed that the emergence of AVs and SAVs would improve the overall network performance, and better performance was observed with increasing the share distribution of SAVs. Similarly, the consumer surplus increased in all future scenarios, especially with increasing the share distribution of AVs. Consequently, the advent of AVs and SAVs will improve traffic performance and increase consumer surplus, benefiting road users and authorities.

Walking in the Era of Autonomous Vehicles

(1) Background: The emergence of autonomous vehicles (AVs) is likely to have substantial implications for walking behaviours due to the availability of new transport options and altered physical environments within cities. The aim of this exploratory study was to identify AV-related factors that could affect walking at the population level and strategies to ameliorate any negative effects. (2) Methods: A total of 46 Australian expert stakeholders were interviewed about their perceptions of the potential impacts of AVs on walking behaviours. The interviewees represented government departments (state and federal), non-government organisations (NGOs), private sector companies, peak bodies, and academia. (3) Results: Interviewees expected AVs to have different effects on individuals’ ability and motivation to engage in planned versus incidental walking. While those with innate motivation to walk as a form of exercise or leisure may experience enhanced participation opportunities, it appears that incidental walking could be adversely impacted through the availability of convenient AV door-to-door transport options and automated home delivery services that reduce walking related to commuting and shopping. (4) Conclusions: Proactive policy actions are needed to optimize the potential positive impacts of AVs on walking and circumvent the potential negative impacts on valuable incidental walking that constitutes a key component of many people’s total physical activity.

Defensive or competitive Autonomous Vehicles: Which one interacts safely and efficiently with pedestrians?

The emergence of Autonomous Vehicles (AVs) could provoke unexpected challenges in urban traffic environments. One such crucial challenge is the conflicts between pedestrians and AVs, particularly on unsignalized mid-block crosswalks (UMC), where pedestrians are exposed to the AV flow. This study investigates the efficiency and safety performance of a UMC in the presence of both AVs and pedestrians considering the diversities in their behaviors. Through empirical analyses, two pedestrians’ crossing decision models are built and four groups of speed profiles are classified. Meanwhile, based on previous literature, defensive and competitive driving strategies are assumed for AVs. The simulation is implemented on an agent-based framework that can dynamically reproduce the kinematic interactions between pedestrians and vehicles. Results indicated that with a reasonable safety margin (2.5 s), percentages of low post encroachment time events for competitive AVs with different pedestrian types are smaller than defensive AVs with differences of 0.2% to 2.9%. The average delays of competitive AVs for all pedestrian types are smaller than defensive AVs with a maximum estimated difference of 39 s. Moreover, the analysis showed that lowering the speed limit may reduce the crash rate of competitive AV up to 0%. It is also found that the pedestrians who make reckless crossing decisions and change their speed drastically during the crossing process are more likely to incur crashes with competitive AVs. Therefore, if pedestrian behaviors can be regulated reasonably, competitive AVs with appropriate parameter settings are most suitable for UMC in the future.

Driverless Industry: Current State, Business Model, Challenges and Prospects

Digital transformation in the automotive industry promotes the emergence of autonomous vehicles, having a significant impact on people’s daily life and microeconomic environment. This paper presents the current situation of the self-driving industry, its prospects, and the potential value chain in the future. Being the bellwether in the self-driving industry, Waymo has had the largest driverless taxi service in the world. Although there are many obstacles in the process of autonomous vehicles being the mainstream, its future is still promising. We predict the changes in the value chain mainly come from driverless technology and its convenience. This research could enable autonomous vehicle manufacturers to know which aspects should be developed into core competencies.

Consideration on Contemporary Tasks about the Realization of Autonomous Vehicles Public Road Driving - Focusing on the revision of the Road Traffic Act and driver's license regulations -

Autonomous Vehicles are one of the representative products of the 4th industrial revolution from which we can expect benefits such as the reduction of traffic accidents and the decrease of casualties and loosening of traffic congestion by that. It can be said that interest in and movement about the development and commercialization of Autonomous Vehicles are considerable globally. Autonomous Vehicles can be described as the ones that perceive surroundings and do things dynamically on their own through recognition, judgment, and control. Also, to realize the driving and operation of Autonomous Vehicles on public roads, basically, it is needed to examine and revise the Road Traffic Act that has been institutionalized to keep cars moving smoothly by preventing and eliminating all the dangerous and hindrance factors of traffic on roads. We cannot deny the fact that the safe and desirable commercialization of those cars will be never obtained without it. Therefore, it is necessary not only to reflect technological perspectives to the revision of the Road Traffic Act but also to understand Autonomous Vehicles from the standpoint of the Road Traffic Act. From the viewpoint of the Road Traffic Act, Autonomous Vehicles can be regarded as the cars that support driving, but it is somewhat different in the perspective of technology. We cannot say that operating capacity directly means driving ability. Accordingly, in the situation to develop Autonomous Vehicles, we carry out recognition based on the technological concept of Autonomous Vehicles. To realize the reflection of it diversely into legislation, however, it is needed to perform consideration more precisely. In other words, not only technological development but efforts and attempts have been made multilaterally to commercialize and generalize Autonomous Vehicles from which we can expect various advantages associated with the decrease of traffic accidents. Yet, what is related to autonomous driving is not just from today or yesterday but has been a part of technological advancement and development that have been done continuously from long before. To wit, the emergence of Autonomous Vehicles did not take place in a day along with the 4th industrial revolution, and it can also be considered as part of the acceptance of technological development. Moreover, it seems to be necessary to establish definite criteria or such regarding what should be the focus in the realization of Autonomous Vehicles. In other words, on which should we put the emphasis, the acceptance of developed and advanced technology or the enhancement of safety? In conclusion, basically, it is needed to consider and arouse opinions about two aspects; one is to advocate technology as much as possible immediately, and the other is to accept it stepwise through discussion and examination. Therefore, it is necessary to consider all those things generally and revise the law practically and realistically in order to realize the public road driving of Autonomous Vehicles.

Beyond the Driverless Car: A Typology of Forms and Functions for Autonomous Mobility

ABSTRACT The emergence of autonomous vehicles is attracting intense scrutiny for the transformative effects it may have on urban infrastructures and planning systems. Much of this scrutiny, though, has arguably focused on driverless passenger cars designed to operate on conventional carriageways. In this paper, we argue the car-centric vision of autonomous mobility limits our ability to identify and respond to the challenges posed by a suite of alternative vehicles found in hallways, footpaths, and (sub)urban precincts. We address this problem by presenting a typological description of autonomous vehicles beyond the driverless car. The paper describes their configuration and performance, identifies the spaces and use-cases they are associated with, and gestures to the challenges they raise for urban environments. The paper synthesises the observations generated through the typology into a set of complex questions demonstrated through the scenarioof a self-driving pod providing passenger transport at a university campus. It shows that, with a broad range of autonomous vehicles already operating in our cities, we need finer-grained analyses of how they are integrated into shared pedestrian spaces. The typology it offers provides a platform for this research by enhancing our comprehension of autonomous mobility forms, functions, and spaces. This will support further mobilities research on the proccesses and outcomes of urban experiments with autonomous systems.