Introduction Of Autonomous Vehicles Research Articles

Backcasting Analysis of Autonomous Vehicle Implementation: A Systematic Review

The introduction of autonomous vehicles (AVs) has the potential to drastically change society, planning, design, and development strategies. This study uses the PRISMA protocol to carry out a systematic literature review, focusing on the backcasting method as an analytic tool. By examining. 21 studies published between 2003 and 2024, this paper highlights the phases of backcasting: visioning, policy packaging, and appraisal, and identifies critical factors necessary for the successful integration of AVs. Visioning for future driverless cities includes high-quality urban areas, active mobility, and innovative developments. Policies and Packaging suggested a focus on restricting vehicular access, transit-oriented development, and encouraging public transportation. Appraisal reveals skepticism about the positive impacts of AVs, urging policies that limit access to urban areas and promote sustainable modes of transportation. The main contribution of this study lies in its comprehensive application of backcasting to AV implementation, offering a structured approach to envisioning future urban scenarios, formulating supportive policies, and evaluating their impact. This analysis provides a solid foundation for future research, urging us to explore the intersection between AVs, citizen participation, and environmental sustainability to achieve more efficient and sustainable cities.

Hypercongestion, Autonomous Vehicles, and Urban Spatial Structure

This paper examines the effects of hypercongestion mitigation by perimeter control and the introduction of autonomous vehicles on the spatial structures of cities. By incorporating a bathtub model, we develop a land use model where hypercongestion occurs in the downtown area and interacts with land use. We show that hypercongestion mitigation by perimeter control decreases the commuting cost in the short run and results in a less dense urban spatial structure in the long run. Furthermore, we reveal that the impact of autonomous vehicles depends on the presence of hypercongestion. The introduction of autonomous vehicles may increase the commuting cost in the presence of hypercongestion and may cause a decrease in the suburban population; however, it may make cities spatially expand outward. This result contradicts that of the standard bottleneck model. When perimeter control is implemented, the introduction of autonomous vehicles decreases the commuting cost and results in a less dense urban spatial structure. These results show that hypercongestion is a key factor that can change urban spatial structures. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: This work was supported by ACT-X [Grant JPMJAX21AE], Fusion Oriented REsearch for disruptive Science and Technology [Grant JPMJFR215M], the Japan Society for the Promotion of Science [Grants 23K13422 and 22H01610], and the Council for Science, Technology and Innovation [Grant JPJ012187]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0519 .

Theory-data dual driven car following model in traffic flow mixed of AVs and HDVs

To model the car following (CF) behavior of mixed traffic flow composed of autonomous vehicles (AVs) and human-driving vehicles (HDVs) in the future, this paper calibrated the lower control algorithm of AVs and proposed a model named theory-data dual driven stochastically generative adversarial networks (TDS-GAN) to describe the CF behavior of HDVs based on experimental data from mixed traffic flow. Firstly, the experimental scenario and collected data were introduced. Secondly, two transfer functions for the lower control algorithm of AVs were compared. Then, to account for the stochasticity of HDVs, the idea of physics-informed deep learning (PIDL) was used to improve generative adversarial networks (GAN) and integrate it with two-dimensional intelligent driver model (2D-IDM). Finally, the effectiveness of the model was verified from both a micro prediction and a macro simulation perspective. The influence of AVs on the stability of mixed traffic flow at different Market Penetration Rate (MPR) was also observed. The results show that TDS-GAN can effectively describe the following behavior and stochasticity of HDVs. When combined with CF model of AVs, it can depict the evolution of mixed traffic flow more accurately. Additionally, AVs can improve traffic flow stability under different penetration rates, and the effect is more significant with higher MPR. However, the introduction of AVs may not necessarily be positive in terms of road capacity.

Assessing the Impacts of Autonomous Vehicles on Urban Sprawl

Background: Urban sprawl adversely effects the sustainability of urban environments by promoting private vehicle use, decreasing the viability of active/public transport, and increasing the cost of public service provision. Autonomous vehicles could change the desirability of different residential locations due to resulting changes to urban design and decreased value of travel time. Methods: Adult Australians (n = 1078) completed an online survey that included a description of a future where autonomous vehicles are widely available. The respondents reported anticipated changes in residential location in this autonomous future. Frequency analyses were conducted, and three logistic generalised linear models were run to identify factors associated with staying in the same area or moving to higher- or lower-density locations. Results: Autonomous vehicles are likely to have mixed effects on people’s desired residential locations. Most respondents (84%) elected not to move location, 11% intended to move to lower-density locations, and 6% to higher density locations. Reasons for moving included a desire for more space, the ease of travelling in urban areas, and the reduced value of travel time. Conclusion: The introduction of autonomous vehicles will need to be managed to avoid fostering increased urban sprawl and the associated negative consequences. Strategies that increase the liveability of higher density urban environments are likely to discourage urban sprawl in a future characterised by autonomous transport options.

Impact of autonomous vehicles on discretionary activities: an agent-based model with space–time accessibility constraints

AbstractThe extensive development of autonomous vehicles (AVs) is set to revolutionise the way of travelling. Research suggests that the introduction of AVs may affect travel behaviour and choices, resulting in long-term changes in land use. Accessibility is an important concept that connects transportation and land use, providing a holistic performance measure for the transport-land use system. However, this concept has not been adequately capitalised in studies that attempt to understand the impact of AVs on location choice decisions. To explore this knowledge gap, we proposed an agent-based simulation framework that integrates with accessibility constraints to study how AVs influence behavioural and location choices. The framework consists of an activity-based travel demand model with accessibility constraints and a dynamic transport assignment model. The accessibility constraints are derived from individuals’ travel time budgets based on activity-travel survey data. We applied the agent-based simulation framework to Clayton, Australia, and focused on discretionary activity location choices. Various values of travel time and vehicle running costs underpinned by the use of AVs were examined. While most studies have concluded that AVs can significantly increase trip lengths for daily activities, our results demonstrate that even when AVs are used, the movement of individuals is still limited by spatio-temporal constraints of accessibility. As a result, we predict that the increase in discretionary trip lengths and their impact on traffic congestion is modest.

The value of scenario discovery in land-use modeling: An automated vehicle test case

Long-range planning is an uncertain endeavor. This is especially true for urban regions, small ships in a global urban storm that are too small to influence macro policies and without the land-use powers of local governments. Exploratory scenarios, the established practice for planning under deep uncertainty, have inspired stakeholders to consider multiple futures but have fallen short of identifying robust and contingent policies. We need new tools to plan under conditions of deep uncertainty. Scenario discovery is a technique for using simulation models to explore the performance of policy options across uncertain scenarios. This paper presents an application of scenario discovery in land-use modeling and asks what this computationally intensive approach offers relative to a more circumscribed exploration of uncertainty space. The introduction of autonomous vehicles (AVs) and their associated impacts on land use provide a test case demonstrating this method, as well as a topic of substantive concern. This research concludes that scenario discovery is particularly valuable for identifying the conditions under which contingent policies are likely to succeed. In terms of AV policy, this research establishes that forward-thinking, transit-oriented-development strategies can mitigate spatial dispersion while also reducing overall housing costs. In addition, I find that AVs may blunt the impacts of some current policy tools if they extend the distance individuals are willing to travel to work.

Estimation of Environmental Rebound Effect Induced by Shared Automated Passenger Transport Service in a Mid-Size European City via Microsimulation

The introduction of autonomous vehicles will revolutionize transport in urban and rural areas. Nevertheless, before we allow autonomous vehicles to roam our streets, we should strive to predict the impact that they could have and prevent as many negative externalities as possible. Moreover, it is expected that the ability to let go of the wheel and multitask could substantially decrease the in-vehicle value of time, triggering travel behavioral changes, which could in turn have a negative impact on the environmental sustainability of the transport system. Thus, we tried to estimate the environmental rebound effect linked to behavioral changes caused by shared autonomous vehicle deployment. This study presents results of a simulation of the transport system of Santander (Spain), performed by linking the activity-based demand estimation developed in SimMobility to microsimulation in Aimsun and its battery consumption and pollutant emissions models. The results yielded by the study present the magnitude to which identified travel behavioral changes could affect the environmental performance of the transport system, as well as the overall outcome of all identified behavioral changes. The outcomes show that the rebound effect could increase the CO2 emissions by almost 40% compared with a scenario with no behavioral changes. We believe this topic to be particularly interesting for policy makers, urban planners and regional authorities.

Exploring parking choices under the coexistence of autonomous and conventional vehicles

The introduction of autonomous vehicles (AVs) is likely to alter conventional parking behaviors in urban areas. In particular, AVs would allow travelers to be dropped off at their destination and park at an economic location or keep self-driving, waiting for the travelers to complete their activities. The present study investigates the parking choices under the coexistence of AVs and conventional vehicles (CVs) and compares their travel impact. To this end, we use an agent-based simulation model to consider different parking options for both vehicles. Most notably, for AVs, these choices include returning to origins, parking at a lot, and continuing self-driving. Results show that if CVs and AVs each make up half of the vehicle fleet, the distance traveled by AVs accounts for over 60% of the total vehicle kilometers traveled (VKT) by all vehicles, and the occupant-free kilometers contributes to around 30% of the VKT traveled by AVs. At 100% penetration of AVs, the total VKT by all vehicles is more than 30% greater than that of completely CVs (i.e., 0% AV penetration). At lower vehicle demand, the percentage of different AV parking choices is relatively stable, and total VKT and travel time increase proportionally with the number of demands. In comparison, at a higher vehicle demand, total travel time increases exponentially while total VKT rises modestly over time. The two pricing policies—lowering parking prices and raising travel costs—have comparable effects in motivating more AVs to park in a parking spot instead of returning to the origin. The simulation and subsequent analyses help understand parking choices and their travel consequences when AVs and CVs coexist.

The Future of Transportation Design: Balancing Aesthetics and Functionality in Autonomous Vehicles

This study examines the rapidly changing and competitive area of automobile design, with a particular focus on the introduction of autonomous vehicles. The study moves the focus of the conversation from conventional automotive design approaches to new, usercentric techniques which make use of the opportunities of autonomous technology. Moreover, it assesses the potential effects of different design choices on the experience of users, protection, and transportation within cities. Also, the research offers realistic viewpoints on the coming shifts and alterations in mobility design, predicting the significant effect of autonomous vehicles on both personal and social transportation models. When it comes to aesthetics, the recommendations for designing autonomous vehicles highlight finding an equilibrium between practicality and efficacy without compromising aesthetic appeal. Incorporating adaptable design characteristics that may be changed according to different user needs is advised, while also giving importance to ergonomics and simple designs that improve user interaction. Vehicle aerodynamics should be enhanced, and sustainable materials should be used if possible because these actions can help cut reduce emissions and energy use. In addition, the design process must take into account the integration of cutting-edge technology, including artificial intelligence and machine learning, to improve the vehicle's functionalities while verifying that aesthetic improvements have no impact on the vehicle's efficiency.

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.

Self-driving vehicles’ impacts on Americans’ long-distance domestic travel choices

ABSTRACT This research estimated models for long-distance domestic passenger trips before and after the introduction of autonomous vehicles (AVs) and their application to a 10% synthetic US population. The authors synthesized 12.1M households and 28.1M individuals across 73,056 US census tracts. To generate disaggregated passenger trips, travel demand models, including trip frequency, season, purpose, party size, mode choice, and destination choice, and vehicle ownership models were estimated. Different datasets, including a 2021 long-distance AV survey, 2016/17 National Household Travel Survey (NHTS) survey, EPA Smart Location data, and FHWA rJourney dataset were used for model estimation. The model applications indicated AV ownership to be 0.33 per capita after the introduction of AVs within the marketplace with a $3500 AV technology cost premium in the year 2040. Total person-miles traveled per capita in long-distance trips was also estimated to rise 35% (from 280 to 379 miles per month).

Autonomous vehicle market development in Beijing: A system dynamics approach

A system dynamics (SD) model for the autonomous vehicles (AVs) market development in Beijing is proposed. The model approaches the vehicle scale evolution with the marketization development of private AVs and shared AVs. Some potential factors such as trust, AV purchase willingness, AV using willingness and shared AV service accessibility, are analyzed. Based on the passenger vehicle situation in Beijing, optimistic scenarios from 2014 to 2050 are developed. The base scenario involves the marketization of private AVs and the operation of shared AV fleets, and based on the levels of driving automation, which are divided into six types. The causal feedback relationship and system flow diagram are illustrated. Results show that with the introduction of AVs in Beijing, the number of private vehicles is significantly decreased, which is dependent on the use of shared AVs. To compare with the private vehicle ownership in 2020, the total number of private vehicles has decreased and the number of the private vehicles is over 2.2 million vehicles in 2050. After 2030, the shared vehicle fleet will be dominated by the high-level shared AV and the number of high-level shared AVs is about 2 million. Further, to consider the impact of ride-sharing, the model compares the different attitudes for customers to share their rides and use the shared AV alternative ratio to approach this. The result shows that the extreme ratio will reduce the number of shared AVs, and the fleet size of shared vehicle is about 400,000 vehicles in 2050. For the low ratio scenario, the fleet size of shared vehicle will increase a lot to over 3,186,000 vehicles, which will lead to a large number of cars on the road and thus increase congestion. The introduction of AVs is expected to reduce the number of private vehicles; However, the uncertainty of residents' attitudes towards car sharing will affect the introduction of AVs. At present, the existing policies prefer to limit the number of vehicles by controlling car registration in Beijing. In the future, with the application of AVs, this could be adjusted by encouraging ride sharing, to achieve sustainable urban transport development in Beijing.

Reoptimisation strategies for dynamic vehicle routing problems with proximity-dependent nodes

Autonomous vehicles create new opportunities as well as new challenges to dynamic vehicle routing. The introduction of autonomous vehicles as information-collecting agents results in scenarios, where dynamic nodes are found by proximity. This paper presents a novel dynamic vehicle-routing problem variant with proximity-dependent nodes. Here, we introduced a novel variable, detectability, which determines whether a proximal dynamic node will be detected, based on the sight radius of the vehicle. The problem considered is motivated by autonomous weed-spraying vehicles in large agricultural operations. This work is generalisable to many other autonomous vehicle applications. The first step to crafting a solution approach for the problem is to decide when reoptimisation should be triggered. Two reoptimisation trigger strategies are considered—exogenous and endogenous. Computational experiments compared the strategies for both the classical dynamic vehicle routing problem as well as the introduced variant. Experiments used extensive standardised vehicle-routing problem benchmarks with varying degrees of dynamism and geographical node distributions. The results showed that for both the classical problem and the novel variant, an endogenous trigger strategy is better in most cases, while an exogenous trigger strategy is only suitable when both detectability and dynamism are low. Furthermore, the optimal level of detectability was shown to be dependent on the combination of trigger, degree of dynamism, and geographical node distribution, meaning practitioners may determine the required detectability based on the attributes of their specific problem.

Autonomous vehicles and street design: Exploring the role of medians in enhancing pedestrian street crossing safety using a virtual reality experiment

As traffic lanes and on-street parking spots can potentially be downsized with the introduction of autonomous vehicles (AVs), the possibility of additional spare road space becoming available arises in future urban streets. While discussions on converting the leftover space into pedestrian-friendly alternatives exist, allocating that limited space to which alternative is foreseen to be another practical issue shared in both urban and transportation planning. However, evidence-based guidance on the issue provided from the actual verification on whether or to what extent the proposed alternatives may have an effect seems to be absent. Therefore, with an emphasis on pedestrian safety, this study focused on the “median strip” alternative as a first example and, through a VR simulation experiment aimed at empirically examining its suggested role on enhancing street crossing safety and further exploring its possible influence on pedestrians’ trust toward autonomous driving. With 99 participants, perceived safety (individual assessments of safety), performance-based safety (crossing success/abandonment and collision occurrence), and trust were either questioned or recorded for nine scenarios with varying crossing conditions. A combination of multilevel models and cross-tabulation results indicate that medians seem especially significant in ensuring the performance-based safety results of pedestrians even when AVs are driving at high speeds or with smaller gaps, thus suggesting it a win–win option for both. Insights and implications on the role and management of medians in future streets are further provided.

Global policy and the adoption of autonomous vehicles

Automated driving systems are an emerging technology expected to transform the transportation industry due to reduced human involvement. While the introduction of autonomous vehicles (AVs) can significantly improve road safety, increase mobility, and reduce road crashes, there are also associated disadvantages. Policymakers have the ability to implement legislation that maximizes the benefits of AVs and minimizes their risks. Through an evaluation of barriers to AV adoption and regulations worldwide, this study aims to provide an overview of AV equity, liability, and safety policies globally. The results revealed that safety was the primary factor influencing public perception and hindering widespread adoption of autonomous vehicles. Furthermore, it was determined that governments have refrained from implementing stringent measures to encourage the advancement of AV technologies. Instead of enacting AV related laws, most countries have developed guidelines or formed councils to encourage further deliberation on safety and liability concerns associated with autonomous driving systems. Transportation professionals, AV consumers, and manufacturers will develop an understanding of AV accessibility, liability, and safety policies through this review. This paper can also help policymakers and drive future enacted legislation.

A REVIEW ON IMPERATIVE FACTORS THAT AFFECT THE FEASIBILITY OF AUTONOMOUS VEHICLES IN INDIA

In the recent times, the proliferation of the Autonomous Vehicles (AV’s) is rapid. Autonomous Vehicles which were present on screens will soon become a reality. Autonomous Vehicles are being piloted in several countries and are being tested and running on public roads, even though only in a handful of locations. Autonomous Vehicles will fundamentally change the transportation scenario in a highly vehicle-populated country, such as India. The major milestone that the community of Autonomous Vehicle Technology in India facing was the inadequate literatures in this realm. Owing to this inadequacy, through this review paper, the exploration of few imperative factors – Political, Topographical, Economic, and Consumer Behaviorism, essential for the growth and development of AV’s in India is put together. Indicative results in the form of Likert chart and graphs have been developed through exploratory analysis to understand the feasibility of the AV technology in India in respect of the imperative factors mentioned. This study also touches upon the existing standards for the various factors and the introduction of Autonomous Vehicles in Indian market in comparison to other markets.

An adapted Burckhardt tire model for off-road vehicle applications

Off-road vehicle mobility is a real challenge in many areas such as agriculture, military, or construction machinery. The introduction of autonomous vehicles in these fields requires more advanced control algorithms. Furthermore, the deformable nature of the terrain has a significant impact on vehicle behavior. In this context, a lot of research has been conducted to describe the wheel-ground interaction phenomena. Among the most significant factors, traction force, lateral force, and wheel sinkage are crucial aspects in the study of vehicle mobility. One of the most used approaches is based on the Bekker-Wong description. However, due to the mathematical complexity of this model, this approach is rather intended for vehicle simulation. Since the design of control laws requires less complex models, this paper proposes a method based on a road tire model structure adapted to deformable surfaces. This modification is based on the original Burckhardt tire model with a modified structure and estimated parameters. Furthermore, the parameter identification is performed by simulating a realistic virtual test bench allowing the data collection for the parameter estimation process. Finally, the obtained results show the applicability of the proposed method through a new tire model called the Adapted Burchardt Tire Model (ABTM).

Moral disengagement mechanisms in interactions of human drivers with autonomous vehicles: Validation of a new scale and relevance with personality, driving style and attitudes

The introduction of autonomous vehicles (AVs) in the road transportation systems raises questions with respect to their interactions with human drivers’, especially during the early stages. Issues such as unfamiliarity or false assumptions regarding the timid and safe behaviour of AVs could potentially result in undesirable human driver behaviours, for instance “testing” AVs or being aggressive towards them. Among other factors, morality has been determined as a source of aggressive driving behaviour. Following previous approaches on moral disengagement, the current paper argues that moral standards during interactions of human drivers with AVs could potentially blur, leading to the disengagement of self-regulation mechanisms of moral behaviour. The study investigates the impact of moral disengagement on the intention of human drivers to be aggressive towards AVs. To that end, an online survey was conducted including a newly developed survey of moral disengagement, adapted to the context of AVs. Moreover, measures of personality, driving style, attitudes towards sharing the road with AVs and perceived threats were collected. A confirmatory factor analysis provided support for the concept of moral disengagement in the context of AVs. Moreover, relationships between personality, driving style and attitudes towards sharing the road with AVs were found, via a structural equation modelling approach (SEM). The results could have implications in the future driver training and education programmes, as it might be necessary to not only focus on driving skills but also on the development of procedural skills that will improve the understanding of AVs’ capabilities and ensure safer interactions. Efforts on improving attitudes towards AVs may also be necessary for improving human driver behaviour.

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.

An overview of the impacts of autonomous vehicles on fire safety in parking buildings

Several fire safety benefits are realised when considering autonomous vehicles (AVs) using parking buildings. Hazards posed by the increasing number of alternatively powered vehicles on the road, such as electric vehicles (EVs), may be mitigated. Parking AVs in designated zones would allow for the installation of fire-suppression systems other than those that are water-based, permit alternative ventilation requirements and allow for the reconfiguration of the building structure. Connected AVs could communicate with other vehicles, the parking building and the emergency services, resulting in a more effective response to a developing fire incident. However, AVs may increase fire safety hazards when considering alternative parking arrangements that promote more extensive fire spread, increase the median fire load energy density by at least 13% and inhibit firefighting. This may impact on the operational capability of the emergency services and the expectations of the structural fire safety design of the building. In this paper, options are presented that would influence the design and use of parking buildings where AVs and EVs are present. Presently, it is unclear whether the introduction of AVs will increase or decrease the fire safety risk to life and property, and further research is suggested to better understand such matters.