Impact Of Autonomous Vehicles Research Articles

The impact of autonomous vehicles on mining operations: Enhancing safety and productivity through technological advancements

The impact of autonomous vehicles on mining operations: Enhancing safety and productivity through technological advancements

Revolutionizing Roads: The Impact of Autonomous Vehicles on Future Travel

This project focuses on developing an autonomous self-driving car by integrating advanced algorithms with cutting-edge hardware. The vehicle is designed to perceive its environment, make informed decisions, and navigate complex traffic scenarios autonomously by leveraging machine learning and computer vision technologies. Essential methods include using deep learning algorithms for object detection and classification, sensor fusion techniques to combine data from cameras, LiDAR, and radar, and reinforcement learning for decision-making in dynamic environments. The project emphasizes the critical role of AI in enabling the car to interpret and respond to dynamic road conditions while ensuring reliable performance through rigorous testing and validation in simulated and real-world scenarios. Initial results from testing indicate that the car can successfully navigate complex traffic situations, such as merging onto highways and responding to pedestrians, with a high degree of accuracy. By pushing the boundaries of autonomous vehicle technology, this research aspires to contribute to the ongoing evolution of self-driving cars and their potential to revolutionize mobility. With a focus on safety, efficiency, and innovation, this endeavor represents a significant step forward in developing autonomous vehicles, offering new possibilities for the future of transportation. The project promises to improve travel convenience and significantly reduce traffic accidents, ultimately contributing to a safer and more efficient road system.

Revolutionizing ocean engineering: the impact of autonomous vehicles

Revolutionizing ocean engineering: the impact of autonomous vehicles

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 impact of autonomous vehicles on the performance of steel bridge deck pavement

The widespread application of autonomous driving technology has become a prominent trend in the transportation industry. The purpose of this study was to investigate the impact of the lateral distribution pattern of autonomous vehicles (AVs) on steel bridge deck pavements (SBDP), with the aim of providing reliable theoretical support for future bridge deck pavement management and operation. A mixed traffic flow lateral distribution model was established and the Nanjing Yangtze Fourth Bridge was used as a case study. A corresponding finite-element model (FE) was constructed and Abaqus FE software was used to analyse rutting depths under different scenarios. The simulation results demonstrated that, under the zero distribution mode, a rational layout of AVs can significantly alleviate rut formation. Conversely, an unreasonable layout will reduce the service life of SBDP. Comparing the lateral distribution patterns of different vehicles, both the normal distribution and uniform distribution patterns exhibited positive effects on the SBDP. However, there was no significant difference in the magnitude of this positive impact between the two modes. This research not only fills the gap in the study of AV impact on SBDP but also proposes management methods for AVs.

The Impact of Autonomous Vehicles on Cities

Autonomous vehicles are a double-edged sword, bringing happiness and convenience to peoples lives but also silently creating various crises and bad effects. The purpose of this article is to explore and analyze the implications of autonomous vehicles, with a focus on their impact on cities, as they are and will be massively linked to smart city systems and operate on urban roads. In this paper, the author has analyzed the findings and data from many past literature and research sources and summarized them based on his own research and ideas. The findings of this article show that the positive impacts of self-driving cars are mainly in the areas of ease of travel and self-driving car fleets. The negative impacts are mainly reflected in three different aspects: safety hazards, environmental pollution and energy consumption. Of course, self-driving cars have different impacts in various fields, and these impacts need to be explored by more researchers.

Models, algorithms, and equilibrium conditions for the simulation of autonomous vehicles in exclusive and mixed traffic

This paper proposes models and algorithms for the estimation of traffic flows generated by the centralised management of autonomous vehicles under exclusive and mixed traffic conditions. It is assumed that the autonomous vehicle manager wants to obtain the system optimum that minimises the total travel time on the network, of autonomous vehicles in the case of exclusive traffic, and of all flows in the case of mixed traffic. In the case of mixed traffic, the drivers of the human-guided vehicles choose their routes from a user-optimal perspective; in this case, a fixed-point problem has to be solved, which represents a state of equilibrium between the flows of human-guided vehicles, the flows of autonomous vehicles, which follow paths aimed at the system optimum, and the link costs. The proposed models and algorithms are tested on a small network with a single origin-destination pair, and on the Sioux Falls network, with different levels of congestion and different percentages of autonomous vehicles. The results show the validity of the proposed methods and the impact of autonomous vehicles on network performance. The proposed static/equilibrium approach is necessary to evaluate transport planning, design or policy interventions that include the presence of autonomous vehicles in traffic flows.

What if autonomous vehicles had been introduced into cities? A counterfactual analysis

The impact of autonomous vehicles on the spatial size of cities remains ambiguous, as the future is highly uncertain. This paper uses counterfactual analysis techniques to examine the effects of autonomous vehicles on urban expansion for metropolitan areas in the United States if autonomous vehicles had been introduced before. We argue that distance cost and congestion cost, which are the two components of transportation cost with different effects on urban expansion, should be addressed in autonomous vehicle research. By coupling historical data with hypothetical scenarios of introducing autonomous vehicles to cities, we find that urban expansion, rather than urban densification, would have been the dominant effect if autonomous vehicles had been introduced into cities. The finding indicates that if autonomous vehicles are widely adopted in the future, they are likely to have similar, or even larger, effects on future urban expansion than in the counterfactual past.

Impact of Autonomous Vehicles on the Car-Following Behavior of Human Drivers

The past few years have been witness to an increase in autonomous vehicle (AV) development and testing. However, even with a fully developed and comprehensively tested AV technology, AVs are anticipated to share the roadway network with human drivers for the unforeseeable future. In such a mixed environment, we use naturalistic driving data from the next generation simulation (NGSIM) and Lyft Level 5 (Lyft L5) prediction data sets to investigate whether the existence of AVs influences the car-following behavior of human drivers. We use time headway time series as a proxy to capture the car-following behavior of human drivers. We then develop a nested fixed model to find possible changes in behavior when human drivers are following different types of vehicles (i.e., human-driven vehicles or AVs). The factors included in this model are the platoon structure (a legacy vehicle following a legacy vehicle, and a legacy vehicle following an autonomous vehicle), road type (freeway and urban), time period (morning and afternoon), lane (right, middle, and left), and the source of the data (NGSIM and Lyft L5). Results indicate a statistically significant difference between the car-following behavior of drivers when they follow a human-driven vehicle compared to an AV. This change in the car-following behavior of drivers is manifested in the form of a reduction in the mean and variance of time headways when human drivers follow an AV. These findings can bridge the gap between anticipated and real-world impacts of AVs on traffic streams and roadway capacity, providing meaningful insights on the influence of AVs on the driving behavior of humans in a naturalistic driving environment.

Impact of autonomous vehicles on air quality and energy efficiency of road traffic flows – a case study in an urban roundabout

Due to the likely market penetration of autonomous vehicles (AVs) in the short-medium term, several studies have analysed their environmental impact due to changes in demand patterns or market models. However, there is little research on the operational impact of AVs on air quality. This work aims to explore several scenarios of Avs penetration with different driving patterns and anticipate the potential impacts of traffic performance, emissions, and air quality. An urban intersection was used as a case study, namely a roundabout located in a critical context from the air pollution exposure point of view. Results show that the introduction of Avs may lead to higher travel times at peak hours and increased emissions, mainly due to AVs with defensive behaviour. Regarding air quality, the introduction of AVs led to a degradation of air quality, especially during peak hours.

The Impact of Autonomous Vehicles on People's Use of Vehicles and the Possible Environmental Pollution

This paper gives the introduction and impact analysis of autonomous vehicles (AVs). The intention of inventing AVs is to facilitate people's life and to reduce the waste of recourses. It brings several impacts to public society from environmental and other perspectives. Research finds that as the application of AVs is boosting, the vehicle miles traveled (VMTs) of self-driving cars keep increasing. This phenomenon was affected by a few reasons: expanded user groups, increased public transportation, and some personal factors. Furthermore, considering the environmental impacts such as the relationship between energy consumption, life span, and frequency of use. By discovering the negative impact of AVs on humans and the whole society, some of these impacts are listed, like it reduces the labor force and causes radiation to the human body. It is undoubtful that AVs have brought many improvements to human life, it contains drawbacks due to the immature technology and short development period, but protocols and technology advancements will help to minimize these drawbacks as this technology is getting more mature.

Can day-to-day dynamic model be solved analytically? New insights on portraying equilibrium and accommodating autonomous vehicles

This paper develops a new approach to portray the equilibrium and analyze the appropriate lane policy during different deployment stages of autonomous vehicles (AVs) by innovatively integrating Vickrey's bottleneck model into the day-to-day dynamic model. The travel cost function in the classical bottleneck model is described by considering the impact of AVs’ value of time (VOT) reduction and AV gain. The evolution of lane choice behaviors is described using the first-order and second-order day-to-day dynamic model. Furthermore, three lane policies are proposed to accommodate AVs’ development, and the properties for the endogenous market penetration of AVs and travel cost under three lane policies are examined to recognize which policy is more suitable at different development stages of AVs. The results demonstrate that such equilibrium solutions as traffic flow, total travel cost, the convergence time are all analytically solvable and thus can perfectly answer the problem of stability and convergence involved in the day-to-day dynamic model. Finally, the numerical analysis is conducted to verify the previous findings and get some intuitive insights on system performance. This work not only provides new insights on the evolutionary process of AVs’ and regular vehicles (RVs)’ lane choice behaviors, but also recognizes appropriate lane policy to accommodate AVs from a novel perspective.

Impact of autonomous vehicles on household relocation: An agent-based simulation

The agent-based land use/transport model SILO/MITO/MATSim is adapted to simulate the impact of AVs on household relocation. The revised model accounts for the fact that households who own conventional cars are sensitive to parking availability at their dwelling. As AVs could park themselves anywhere, this sensitivity to parking is reduced for households who own AVs. Distance to work, which serves as a hard constraint for household relocation with conventional cars, becomes less critical for households who use an AV to commute as they may perform other activities while commuting. The induced demand of travel by AV is represented and leads to increased congestion.Several scenarios were designed to analyze the effects of reduced value of time for AV travel, parking restrictions and increase of congestion. The simulation shows that AVs will compete with transit and reduce transit ridership by three quarters. The average commute distance is expected to double, and the vehicle-kilometers traveled will increase by one third. The impact of AVs on the distribution of population, however, is marginal. The urban sprawl caused by less burdensome commuting is largely compensated by the increased attractiveness of core cities in the absence of parking issues for AVs.

Impact of autonomous vehicles on the choice of residential locality

ABSTRACT Introducing autonomous vehicles (AVs) reduces generalized transportation costs and encourages people to relocate. Understanding the subsequent changes in urban structure can help predict the future development of urban economies and policies. Transportation connects multiple zones in cities, and by improving traffic flow and ease directly impacts the economies of various markets. Each direct impact then also indirectly affects related markets. Therefore, a spatial computable general equilibrium model that connects multiple zones via a transportation network is suitable for analyzing the impact of a new transport system. This study constructs a model to simulate the effects of AVs on residential location choice. The results show that increased prevalence of AVs steers people toward suburbs with poor public transportation. Thus, high-income workers react more to technological progress, while low-income workers react more to lowered ownership costs. Consumers’ location choices and budgets also affect residential zones’ development. Therefore, regional policy goals must be clarified and appropriate target consumer groups set when introducing AVs.

Introducing the Benefits of Autonomous Vehicles to Logistics During the COVID-19 Era

Recent years have seen an evolution in automation and logistics technologies, especially with the recent growth of the e-commerce industry. The current COVID-19 pandemic and the spread of smart cities have made it necessary to rethink this sector regarding sustainability and resilience. There is a widespread need to redesign some of the steps that lead goods to our homes, encouraging decarbonization policies, reducing contacts between people for possible contagions and at the same time ensuring that products arrive intact and on time. Currently, there is only limited research on the impact of autonomous vehicles on the logistics industry. This paper reviews the literature by analyzing which autonomous systems have been integrated into long, medium, and short-distance logistics. It is clear that the adoption of Autonomous Vehicles (AVs) stimulates possible innovation in mobility management and transportation logistics and provides the impetus for many unexplored research opportunities. The research investigates the main theoretical and practical benefits related to the use of autonomous vehicles in logistics, differentiating four areas of action (i.e., indoor and outdoor environments, long-distance and first and last-mile logistics).

Impact of Autonomous Vehicles on Roundabout Capacity

Studying the impact of AVs on our road infrastructure offers a lot of potential in the transportation domain; one of these issues is how capacity will be affected. This paper presents a contribution to this research area by investigating the impact of AVs on the capacity of single-lane roundabouts using a microsimulation model. For the development of the model, a roundabout situated in Győr (Hungary) was selected and field data on the roundabout geometric characteristics as well as traffic volumes were used. Simulations using Vissim were run for various scenarios based on varying input traffic volumes and market penetration rates of AVs to assess queue lengths. The highway capacity manual (HCM) roundabout model was used to estimate the capacity of the existing roundabout. Values of follow-up times and critical gaps were set to decreasing as the penetration rate of AVs increases. The results demonstrated that 20% and 40% AVs in the flow would increase leg capacities by about 10% and 20%, respectively. Furthermore, a reduction in excessive queue lengths was estimated and capacities and queue lengths were calculated by legs. It was found that these are highly influenced by the distribution of flows among legs, and the share of flows in various directions.

Analysis of left-turn lane capacity under autonomous vehicle mixed flow considering the influence of pedestrian volume and crosswalk length

Conflicts between pedestrians and left-turning vehicles cause pedestrian safety hazards and decrease vehicle turning capacity at signalized intersections. This commonly occurs in Japan where they share the same traffic light phase. Road capacity is influenced by the interaction between them. Autonomous vehicles (AVs) promise to improve the safety and efficiency of road networks. However, they will face the same problems as human-driven vehicles with pedestrian avoidance. Thus, this study estimated the capacity of the left-turn lane, in Japan, under AV mixed flows taking into account AV penetration rates, pedestrian flow rates, and crosswalk length. The impact of AVs on capacity was analyzed with different settings for the critical gap between vehicles and pedestrians. It was found that if AVs comply perfectly with traffic law, the capacity of the left-turn lane sharply decreases, especially in the case of long crosswalks.

Cruising for Parking with Autonomous and Conventional Vehicles

Parking is a cumbersome part of auto travel because travelers have to search for a spot and walk from that spot to their final destination. This conventional method of parking will change with the arrival of autonomous vehicles (AV). In the near future, users of AVs get dropped off at their final destination and the occupant-free AVs search for the nearest and most convenient parking spot. Hence, individuals no longer bear the discomfort of cruising for parking while sitting in their vehicle. This paper quantifies the impact of AVs on parking occupancy and traffic flow on a corridor that connects a home zone to a downtown zone. The model considers a heterogeneous group of AVs and conventional vehicles (CV) and captures their parking behavior as they try to minimize their generalized travel costs. Insights are obtained from applying the model to two case studies with uniform and linear parking supply along the corridor. We show that (i) CVs park closer to the downtown zone in order to minimize their walking distance, whereas AVs park farther away from the downtown zone to minimize their parking search time, (ii) AVs experience a lower search time than CVs, and (iii) higher AV penetration rates reduce travel costs for both AVs and CVs.

Impacts of Autonomous Vehicles on Traffic Flow Characteristics under Mixed Traffic Environment: Future Perspectives

Vehicle automation and communication technologies are considered promising approaches to improve operational driving behavior. The expected gradual implementation of autonomous vehicles (AVs) shortly will cause unique impacts on the traffic flow characteristics. This paper focuses on reviewing the expected impacts under a mixed traffic environment of AVs and regular vehicles (RVs) considering different AV characteristics. The paper includes a policy implication discussion for possible actual future practice and research interests. The AV implementation has positive impacts on the traffic flow, such as improved traffic capacity and stability. However, the impact depends on the factors including penetration rate of the AVs, characteristics, and operational settings of the AVs, traffic volume level, and human driving behavior. The critical penetration rate, which has a high potential to improve traffic characteristics, was higher than 40%. AV’s intelligent control of operational driving is a function of its operational settings, mainly car-following modeling. Different adjustments of these settings may improve some traffic flow parameters and may deteriorate others. The position and distribution of AVs and the type of their leading or following vehicles may play a role in maximizing their impacts.

A future with no MVC patients? Impact of autonomous vehicles on orthopaedic trauma may be slow and steady.

Introduction:Orthopaedic trauma results in significant patient morbidity. Autonomous vehicle (AV) companies have invested over $100 billion in product development. Successful AVs are projected to reduce motor vehicle collision (MVC)-related injuries by 94%. The purpose of this study was to estimate the timing and magnitude of AV impact on orthopaedic trauma volume.Methods:ICD 9 codes consistent with acetabulum (OTA 62), pelvis (OTA 61), hip (OTA 31), femur (OTA 32–33), tibia (OTA 41–43), ankle (OTA 44), and calcaneus (OTA 82) fractures and the proportion of cases caused by MVC were taken from the National Trauma Databank (NTDB) 2009–2016. Regression was performed on estimates of market penetration for autonomous vehicles taken from the literature.Results:For NTDB years 2009 to 2016, 300,233 of 987,610 fractures of interest were the result of MVC (30.4%). However, the percentage of MVC mechanism of injury ranged from 9% to 53% depending on fracture type. Regression of estimates of AV market penetration predicted an increase of 2.2% market share per year. In the next 15 years we project 22% market penetration resulting in a 6% reduction in orthopaedic lower extremity trauma volume.Conclusion:Adoption of AVs will result in a projected 8% reduction in MVC-related orthopaedic trauma-related injuries over a 15-year period. Although this represents a significant reduction in morbidity, the advent of AVs will not eliminate the need for robust orthopaedic trauma programs. The gradual rate of injury reduction will allow hospitals to adapt and reallocate resources accordingly.