Journal articles on the topic 'Autonomous Vehicle (AV)'
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1
Mohamed Jamil, H., A. Shabadin, H. H. Hashim, A. W. H. Poi, R. Hamidun, A. Roslan, and M. R. Abd Ghani. "Public Perception on Autonomous Vehicle in Malaysia." Journal of the Society of Automotive Engineers Malaysia 3, no. 4 (April 29, 2021): 8–19. http://dx.doi.org/10.56381/jsaem.v3i4.135.
Abstract:
Autonomous Vehicle (AV), also known as self-driving or driverless vehicles, is a technology that intends to replace driving tasks traditionally performed by human drivers. This study aims to gain a better understanding of opinions, concerns, and general acceptance by Malaysian drivers regarding autonomous and self-driving vehicles. The survey questionnaires were adopted from Schoettle & Sivak (2014b) on the topic of public acceptance of driverless vehicles and what would the respondents do when they are not driving. The survey was conducted among the general public in Malaysia inclusive of 19 questions to address topics on public awareness and understanding about AV, benefits of AV, concerns on AV, attitudes toward owning or sharing AV, willingness to pay for AV and their use of free time while riding AV. A total of 520 respondents involved in this survey, however the analysis only considered the respondents who have heard of AV, which amounted to 361 via face to face interview and online survey form. Based on the survey,63.7% were male and 36.3% female. Only 15.2% of the respondents said that they would not ride in a completely self-driving vehicles. AV is also expected to provide a wide range of benefits that attracted most of the respondents. The majority of the respondents expect lower vehicle emission and better fuel economy. Most respondents were also concerned with issues related to AV especially on safety consequences of equipment failure. The survey also shows that most Malaysians are not willing to invest in owning a driverless vehicle. Future studies should continuously consider people’s perception from different perspectives, by taking into account various user viewpoints and characteristics.
2
Rojas-Rueda, David, Mark J. Nieuwenhuijsen, Haneen Khreis, and Howard Frumkin. "Autonomous Vehicles and Public Health." Annual Review of Public Health 41, no. 1 (April 2, 2020): 329–45. http://dx.doi.org/10.1146/annurev-publhealth-040119-094035.
Abstract:
Autonomous vehicles (AVs) have the potential to shape urban life and significantly modify travel behaviors. “Autonomous technology” means technology that can drive a vehicle without active physical control or monitoring by a human operator. The first AV fleets are already in service in US cities. AVs offer a variety of automation, vehicle ownership, and vehicle use options. AVs could increase some health risks (such as air pollution, noise, and sedentarism); however, if proper regulated, AVs will likely reduce morbidity and mortality from motor vehicle crashes and may help reshape cities to promote healthy urban environments. Healthy models of AV use include fully electric vehicles in a system of ridesharing and ridesplitting. Public health will benefit if proper policies and regulatory frameworks are implemented before the complete introduction of AVs into the market.
3
Xiong, Huiyuan, Zhijun Wang, Guohui Wu, and Yuelong Pan. "Design and Implementation of Digital Twin-Assisted Simulation Method for Autonomous Vehicle in Car-Following Scenario." Journal of Sensors 2022 (May 25, 2022): 1–12. http://dx.doi.org/10.1155/2022/4879490.
Abstract:
The automated system replaces the driver, which makes autonomous vehicle to improve safety and convenience, so the market of autonomous vehicle is huge. However, the real-world application of autonomous vehicles faces many challenges due to the immaturity of automated systems. As a consequence, simulation verification plays an irreplaceable role in the application of autonomous vehicle (AV). Car-following is the most common driving scenario in mixed traffic flows, so it is essential to develop an appropriate and effective simulation method for AV. Combined with the existing AV simulation methods and digital twin (DT) technology, this paper proposes a DT-assisted method for AV simulation in a car-following scenario. The method makes the physical vehicle interact with the DT vehicle, and the DT vehicle can dynamically regulate the physical entities through real-time simulation data; the simulation verification can be displayed in the DT scenario to ensure the security of the simulation. Meanwhile, a DT-assisted simulation framework of AV is proposed, the framework includes physical entity components, DT components, and data processing and evaluation components. Besides, a DT-assisted simulation platform is developed base on Unity engine. Finally, the DT-assisted simulation of AV in the car-following scenario is implemented in field experiment. The experimental results show that the proposed method can be effectively conducted AV simulation in car-following, and the average of communication latency is 52.3 ms, which is smaller than the update frequency 15 Hz (66.6 ms) between DT-assisted platform and AV. The DT-assisted simulation method of AV proposed in this paper is applied in the car-following scenario, which effectively solves the challenges of car-following scenario simulation through virtual-real interaction.
4
Erskine, Michael A., Stoney Brooks, Timothy H. Greer, and Charles Apigian. "From driver assistance to fully-autonomous: examining consumer acceptance of autonomous vehicle technologies." Journal of Consumer Marketing 37, no. 7 (September 17, 2020): 883–94. http://dx.doi.org/10.1108/jcm-10-2019-3441.
Abstract:
Purpose The purpose of this paper is to inform researchers who are examining the adoption of autonomous vehicle technology and to provide marketing insights for developers and manufacturers of such vehicles and their ancillary technologies. Design/methodology/approach This study assesses consumer attitudes and behavioral intentions regarding autonomous vehicles (AV) by applying the consumer version of the unified theory of acceptance and use of technology (UTAUT2). We validate the model through a behavioral research study (n = 1,154). Findings The findings suggest that attitude toward AV is primarily formed through performance expectancy, effort expectancy, social influence and hedonic motivation. Furthermore, the level of autonomy has limited effects on attitude. Originality/value This is the first study to examine attitudes toward AV through the theoretical lens of UTAUT2. Additionally, this study provides insights into consumer perceptions and the corresponding effects on attitude by moderating the level of autonomy.
5
Hashim, H. H., and M. Z. Omar. "Towards Autonomous Vehicle Implementation: Issues and Opportunities." Journal of the Society of Automotive Engineers Malaysia 1, no. 2 (May 1, 2017): 111–23. http://dx.doi.org/10.56381/jsaem.v1i2.15.
Abstract:
This paper highlights the factors that should be addressed by any countries when considering Autonomous Vehicle (AV) implementation, along with issues and opportunities that may arise. AV is an emerging technology that has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literatures and explores a broad spectrum of readiness factors, issues and opportunities in the aspects of legislations, litigation, liability, road and surrounding infrastructures, map availability, public acceptance, privacy and public perceptions. While the intention of AV implementation may be driven to address several road safety and road efficiency issues, the traffic mix between AV and non-AV at its initial stage of implementation may introduce a negative impact towards the overall road safety and traffic operations. A systematic framework in adopting and implementing the technology should therefore be established to ensure its objectives are met. This paper contributes to the literature on the fronts that it attempts to shed light on future opportunities as well as possible issues associated with AV implementation; and provide an overall guidance on fundamental factors to be considered before implementing AV.
6
Bridgelall, Raj, and Denver Tolliver. "Deciphering Autonomous Vehicle Regulations with Machine Learning." Applied Sciences 14, no. 4 (February 8, 2024): 1396. http://dx.doi.org/10.3390/app14041396.
Abstract:
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.
7
Baz, Abdullah, Ping Yi, and Ahmad Qurashi. "Intersection Control and Delay Optimization for Autonomous Vehicles Flows Only as Well as Mixed Flows with Ordinary Vehicles." Vehicles 2, no. 3 (August 26, 2020): 523–41. http://dx.doi.org/10.3390/vehicles2030029.
Abstract:
The rapidly improving autonomous vehicle (AV) technology will have a significant impact on traffic safety and efficiency. This study introduces a game-theory-based priority control algorithm for autonomous vehicles to improve intersection safety and efficiency with mixed traffic. By using vehicle-to-infrastructure (V2I) communications, this model allows an AV to exchange information with the roadside units (RSU) to support the decision making of whether an ordinary vehicle (OV) or an AV should pass the intersection first. The safety of vehicles is taken in different stages of decisions to assure collision-free intersection operations. Two different mathematical models have been developed, where model one is for an AV/AV situation and model two is when an AV meets an OV. A simulation model was developed to implement the algorithm and compare the performance of each model with the conventional traffic control at a four-legged signalized intersection and at a roundabout. Three levels of traffic volume and speed combinations were tested in the simulation. The results show significant reductions in delay for both cases; for case (I), AV/AV model, a 65% reduction compared to a roundabout and 84% compared to a four-legged signalized intersection, and for case (II), AV/OV model, the reduction is 30% and 89%, respectively.
8
Raiyn, Jamal. "Data and Cyber Security in Autonomous Vehicle Networks." Transport and Telecommunication Journal 19, no. 4 (December 1, 2018): 325–34. http://dx.doi.org/10.2478/ttj-2018-0027.
Abstract:
Abstract An autonomous vehicle (AV) is a vehicle that operates and performs tasks under its own power. Some features of autonomous vehicle are sensing the environment, collecting information and managing communication with other vehicles. Many autonomous vehicles in development use a combination of cameras, sensors, GPS, radar, LiDAR, and on-board computers. These technologies work together to map the vehicle’s position and its proximity to everything around it. Because of their reliance on these sorts of technologies, which are easily accessible to tampering, a autonomous vehicles are susceptible to cyber attacks if an attacker can discover a weakness in a certain type of vehicle or in a company’s electronic system. This lack of information security can lead to criminal and terrorist acts that eventually cost lives. This paper gives an overview of cyber attack scenarios relating to autonomous vehicles. The cyber security concept proposed here uses biometric data for message authentication and communication, and projects stored and new data based on iris recognition. Iris recognition system can provide other knowledge about drivers as well, such as how tired and sleepy they might be while driving, and they are designed to encrypt the vehicle-to-vehicle and vehicle-to-environment communication based on encryption security mechanisms.
9
Orlický, Adam, Alina Mashko, and Josef Mík. "Assessment of external interface of autonomous vehicles." Acta Polytechnica 61, no. 6 (December 31, 2021): 733–39. http://dx.doi.org/10.14311/ap.2021.61.0733.
Abstract:
The paper deals with the problem of a communication interface between autonomous vehicles (AV) and pedestrians. The introduced methodology for assessing new and existing e-HMI (external HMI) contributes to traffic safety in cities. The methodology is implemented in a pilot experiment with a scenario designed in virtual reality (VR). The simulated scene represents an urban zebra crossing with an approaching autonomous vehicle. The projection is implemented with the help of a head-up display – a headset with a built-in eye tracker. The suggested methodology analyses the pedestrian’s decision making based on the visual cues – the signals displayed on the autonomous vehicle. Furthermore, the decision making is correlated to subjects’ eye behaviour, based on gaze-direction data. The method presented in this paper contributes to the safety of a vehicle-pedestrian communication of autonomous vehicles and is a part of a research that shall further contribute to the design and assessment of external communication interfaces of AV in general.
10
Rahmati, Yalda, Mohammadreza Khajeh Hosseini, Alireza Talebpour, Benjamin Swain, and Christopher Nelson. "Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 12 (July 16, 2019): 367–79. http://dx.doi.org/10.1177/0361198119862628.
Abstract:
Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.
11
Dixit, Aditya, Ramesh Kumar Chidambaram, and Zaheer Allam. "Safety and Risk Analysis of Autonomous Vehicles Using Computer Vision and Neural Networks." Vehicles 3, no. 3 (September 15, 2021): 595–617. http://dx.doi.org/10.3390/vehicles3030036.
Abstract:
The autonomous vehicle (AVs) market is expanding at a rapid pace due to the advancement of information, communication, and sensor technology applications, offering a broad range of opportunities in terms of energy efficiency and addressing climate change concerns and safety. With regard to this last point, the rate of reduction in accidents is considerable when switching safety control tasks to machines from humans, which can be noted as having significantly slower response rates. This paper explores this thematic by focusing on the safety of AVs by thorough analysis of previously collected AV crash statistics and further discusses possible solutions for achieving increased autonomous vehicle safety. To achieve this, this technical paper develops a dynamic run-time safe assessment system, using the standard autonomous drive system (ADS), which is developed and simulated in case studies further in the paper. OpenCV methods for lane detection are developed and applied as robust control frameworks, which introduces the factor of vehicle crash predictability for the ego vehicle. The developed system is made to predict possible crashes by using a combination of machine learning and neural network methods, providing useful information for response mechanisms in risk scenarios. In addition, this paper explores the operational design domain (ODD) of the AV’s system and provides possible solutions to extend the domain in order to render vehicle operationality, even in safe mode. Additionally, three case studies are explored to supplement a discussion on the implementation of algorithms aimed at increasing curved lane detection ability and introducing trajectory predictability of neighbouring vehicles for an ego vehicle, resulting in lower collisions and increasing the safety of the AV overall. This paper thus explores the technical development of autonomous vehicles and is aimed at researchers and practitioners engaging in the conceptualisation, design, and implementation of safer AV systems focusing on lane detection and expanding AV safe state domains and vehicle trajectory predictability.
12
Loken, Andrew E., Joshua S. Steelman, Scott K. Rosenbaugh, and Ronald K. Faller. "Autonomous Vehicle Safe Operating Speeds on the Automated Skyway Express in Jacksonville, Florida." Transportation Research Record: Journal of the Transportation Research Board 2675, no. 6 (February 17, 2021): 188–99. http://dx.doi.org/10.1177/0361198121991834.
Abstract:
Autonomous vehicles (AV) differ significantly from traditional passenger vehicles in both their behavior and physical characteristics. As such, the validity of the guidance provided in the Manual for Assessing Safety Hardware, Second Edition (MASH 2016) is questionable in AV applications. Impact angles, speeds, and vehicle weights specified in MASH 2016 are inextricably linked to the traditional vehicles underlying the estimates. For AV applications, these parameters must be estimated from the ground up, stepping outside the guidance of MASH 2016. In this paper, a conservative method for evaluating existing infrastructure to support AV traffic is proposed. The method integrates traditional structural analyses with unconventional methods of estimating impact conditions. This methodology was developed for the Jacksonville Transportation Authority, who, when faced with unique challenges in maintaining and expanding their Automated Skyway Express, opted to convert the system from monorail to AV traffic. Leading AV developers were surveyed to develop a portfolio of potential candidates for the conversion. Estimated impact conditions were then compared against the capacity of the system’s existing concrete parapets. Ultimately, safe operating speeds for each AV candidate were recommended on the bases of structural capacity and vehicle stability. All but one AV candidate were deemed capable of safely operating at the desired speed of 25 mph without any modifications to the barrier. Although the methodology was developed for a particular case, it is applicable to future implementations of AVs on existing infrastructure, provided the roadway is confined similarly to the Skyway deck.
13
Lavieri, Patrícia S., Venu M. Garikapati, Chandra R. Bhat, Ram M. Pendyala, Sebastian Astroza, and Felipe F. Dias. "Modeling Individual Preferences for Ownership and Sharing of Autonomous Vehicle Technologies." Transportation Research Record: Journal of the Transportation Research Board 2665, no. 1 (January 2017): 1–10. http://dx.doi.org/10.3141/2665-01.
Abstract:
Considerable interest exists in modeling and forecasting the effects of autonomous vehicles on travel behavior and transportation network performance. In an autonomous vehicle (AV) future, individuals may privately own such vehicles, use mobility-on-demand services provided by transportation network companies that operate shared AV fleets, or adopt a combination of those two options. This paper presents a comprehensive model system of AV adoption and use. A generalized, heterogeneous data model system was estimated with data collected as part of the Puget Sound, Washington, Regional Travel Study. The results showed that lifestyle factors play an important role in shaping AV usage. Younger, urban residents who are more educated and technologically savvy are more likely to be early adopters of AV technologies than are older, suburban and rural individuals, a fact that favors a sharing-based service model over private ownership. Models such as the one presented in this paper can be used to predict the adoption of AV technologies, and such predictions will, in turn, help forecast the effects of AVs under alternative future scenarios.
14
Evans, Katherine, Nelson de Moura, Stéphane Chauvier, Raja Chatila, and Ebru Dogan. "Ethical Decision Making in Autonomous Vehicles: The AV Ethics Project." Science and Engineering Ethics 26, no. 6 (October 13, 2020): 3285–312. http://dx.doi.org/10.1007/s11948-020-00272-8.
Abstract:
AbstractThe ethics of autonomous vehicles (AV) has received a great amount of attention in recent years, specifically in regard to their decisional policies in accident situations in which human harm is a likely consequence. Starting from the assumption that human harm is unavoidable, many authors have developed differing accounts of what morality requires in these situations. In this article, a strategy for AV decision-making is proposed, the Ethical Valence Theory, which paints AV decision-making as a type of claim mitigation: different road users hold different moral claims on the vehicle’s behavior, and the vehicle must mitigate these claims as it makes decisions about its environment. Using the context of autonomous vehicles, the harm produced by an action and the uncertainties connected to it are quantified and accounted for through deliberation, resulting in an ethical implementation coherent with reality. The goal of this approach is not to define how moral theory requires vehicles to behave, but rather to provide a computational approach that is flexible enough to accommodate a number of ‘moral positions’ concerning what morality demands and what road users may expect, offering an evaluation tool for the social acceptability of an autonomous vehicle’s ethical decision making.
15
Osman, Radwa Ahmed, and Ahmed Kadry Abdelsalam. "A Novel Adaptive Approach for Autonomous Vehicle Based on Optimization Technique for Enhancing the Communication between Autonomous Vehicle-to-Everything through Cooperative Communication." Applied Sciences 11, no. 19 (September 29, 2021): 9089. http://dx.doi.org/10.3390/app11199089.
Abstract:
Recent autonomous intelligent transportation systems commonly adopt vehicular communication. Efficient communication between autonomous vehicles-to-everything (AV2X) is mandatory to ensure road safety by decreasing traffic jamming, approaching emergency vehicle warning, and assisting in low visibility traffic. In this paper, a new adaptive AV2X model, based on a novel optimization method to enhance the connectivity of the vehicular networks, is proposed. The presented model optimizes the inter-vehicle position to communicate with the autonomous vehicle (AV) or to relay information to everything. Based on the system quality-of-service (QoS) being achieved, a decision will be taken whether the transmitting AV communicates directly to the destination or through cooperative communication. To achieve the given objectives, the best position of the relay-vehicle issue was mathematically formulated as a constrained optimization problem to enhance the communication between AV2X under different environmental conditions. To illustrate the effectiveness of the proposed model, the following factors are considered: distribution of vehicles, vehicle density, vehicle mobility and speed. Simulation results show how the proposed model outperforms other previous models and enhances system performance in terms of four benchmark aspects: throughput (S), packet loss rate (PLR), packet delivery ratio (PDR) and average delivery latency (DL).
16
Galvao, Luiz G., Maysam Abbod, Tatiana Kalganova, Vasile Palade, and Md Nazmul Huda. "Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review." Sensors 21, no. 21 (October 31, 2021): 7267. http://dx.doi.org/10.3390/s21217267.
Abstract:
Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.
17
Schepis, Daniel, Sharon Purchase, Doina Olaru, Brett Smith, and Nick Ellis. "How governments influence autonomous vehicle (AV) innovation." Transportation Research Part A: Policy and Practice 178 (December 2023): 103874. http://dx.doi.org/10.1016/j.tra.2023.103874.
18
Gluck, Aaron, Earl W. Huff, Mengyuan Zhang, and Julian Brinkley. "Lights, Camera, Autonomy! Exploring the Opinions of Older Adults Regarding Autonomous Vehicles Through Enactment." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1971–75. http://dx.doi.org/10.1177/1071181320641475.
Abstract:
Autonomous vehicles (AV), one of the transportation industry’s biggest innovations of the past few decades, bring the promise of safer roads and significantly lower vehicle-related fatalities. While many studies have found largely positive consumer opinions regarding operating and owning such a vehicle, older adults (55+) tend to express concerns about the safety and operational risks of a vehicle with unknown capabilities. To investigate how older adults and AVs may interact, we conducted an improv-style enactment-based partic-ipatory design pilot study. We found that initial concerns about trust and safety can be diminished through training and repetitive successful vehicle operation. Additionally, our participants provided insights into the AV design considerations, needs, and interactions for older adults. These findings add to the collective body of autonomous vehicle research by demonstrating that the needs of this growing population, who may benefit significantly from access to AVs, should be considered by manufacturers.
19
Conlon, James, and Jane Lin. "Greenhouse Gas Emission Impact of Autonomous Vehicle Introduction in an Urban Network." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (April 3, 2019): 142–52. http://dx.doi.org/10.1177/0361198119839970.
Abstract:
This study attempts to quantify the change in carbon dioxide (CO2) emissions resulting from autonomous vehicle (AV) introduction in a congested urban network. An integrated traffic microsimulation and emission model is introduced, described, and used to estimate emissions for different AV penetration scenarios ranging from 0% to 100%. AVs show potential to reduce total CO2 emissions at a network scale, approaching 4% reduction at full autonomy, assuming the same fuel technology in AV as in today’s fleet. Furthermore, it is found that this reduction is sensitive to the penetration ratio of AVs, and is greatest at full AV penetration; AV penetration may generate greater total vehicular CO2 emissions than 0% AV penetration does, contrary to popular belief. This may be because of the heterogeneity in a mixed traffic environment of human-driven vehicles (HDVs) and AVs, as well as the complex interaction between HDVs and AVs that is not yet fully understood.
20
Pokorny, Petr, and Alena Høye. "Descriptive analysis of reports on autonomous vehicle collisions in California: January 2021–June 2022." Traffic Safety Research 2 (September 28, 2022): 000011. http://dx.doi.org/10.55329/xydm4000.
Abstract:
The characteristics of autonomous vehicles’ collisions from 2021 and the first half of 2022 in California confirm trends reported in previous years. Driving in autonomous mode was associated with fewer instances in which the AV was deemed to be at fault in a collision. Most collisions in autonomous mode were rear-end collisions at intersections. Single vehicle collisions occurred mostly in manual mode. Collisions with vulnerable road users occurred mostly while the autonomous vehicle was in manual mode, often right after disengagement from autonomous mode. In collisions with other vehicles that occurred after disengagement, the other vehicles were frequently deemed to be at fault. Compared to 2021, the collision reports from the first half of 2022 indicate higher shares of collisions in autonomous mode, rear-end collisions, and collisions with vulnerable road users.
21
Hyland, Michael F., and Hani S. Mahmassani. "Taxonomy of Shared Autonomous Vehicle Fleet Management Problems to Inform Future Transportation Mobility." Transportation Research Record: Journal of the Transportation Research Board 2653, no. 1 (January 2017): 26–34. http://dx.doi.org/10.3141/2653-04.
Abstract:
This paper presents a taxonomy for classifying vehicle fleet management problems, across several dimensions, to inform future research on autonomous vehicle (AV) fleets. Modeling the AV fleet management problem will bring about new classes of vehicle routing, scheduling, and fleet management problems; nevertheless, the existing literature related to vehicle routing, scheduling, and fleet management is a valuable foundation for future research on the AV fleet management problem. This paper classifies the broadly defined AV fleet management problem by using existing taxonomic categories in the literature; adds additional, or more nuanced, dimensions to existing taxonomic categories; and presents new taxonomic categories to classify specific AV fleet management problems. The broadly defined AV fleet management problem can be classified as a dynamic multivehicle pickup and delivery problem with explicit or implicit time window constraints. Existing studies that fit into this class of fleet management problems are reviewed. New taxonomy categories presented in this paper include fleet size elasticity, reservation structure, accept–reject decision maker, reservation time frame, ridesharing, vehicle repositioning, underlying network structure, and network congestion. Two goals of the taxonomy presented in this study are to provide researchers with a valuable reference as they begin to model AV fleet management problems and to present novel AV fleet management problems to spur interest from researchers.
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Sharma, Nishant, and Parveen Sultana Habibullah. "A Review of IoT Technology for the Connected Autonomous Vehicles Ecosystem." Trends in Sciences 19, no. 7 (March 5, 2022): 3072. http://dx.doi.org/10.48048/tis.2022.3072.
Abstract:
Connected Autonomous Vehicles Ecosystem (CAVE) is fast becoming a reality. This paper reviews various architectures that leverage IoT technology to progressively create a system of interconnected Autonomous Vehicles (AV) that is effective, robust and secure. Accurate and fast perception of the external environment is key to movement of AVs in CAVE. IoT technology through sensors and internetworking can help an AV to do reliable perception. State of the art methods for perception of AVs in CAVE are reviewed. Self-awareness is another important aspect. IoT and Artificial Intelligence (AI) can come together to assist an AV in self-diagnosing anything abnormal. One such use case is presented for onboard diagnostics in an AV. Accurate AI is key to making effective strategies and to take control actions with error feedbacks that carefully maneuver an AV from its source to destination. The transition from legacy vehicles to CAVE requires AI methods that combat threats and have effective overall movement in mixed mode traffic systems (MMTS). Strategies for effective movement in MMTS are presented.
HIGHLIGHTS
Role of IoT technology is identified in different areas of Connected Autonomous Vehicle Ecosystem (CAVE)
A comparison of different technologies in various domains of CAVE is presented and their pros and cons are discussed
Use case for onboard diagnostics of an autonomous vehicle in CAVE is presented to emphasize the role of IoT Technology in [automated] preventive maintenance
Key studies are reviewed that emphasize important issues like robust architectures for CAVE, threats in CAVE and reliable functioning in mixed mode traffic systems
GRAPHICAL ABSTRACT
23
Lee, Dongwoo, John Mulrow, Chana Joanne Haboucha, Sybil Derrible, and Yoram Shiftan. "Attitudes on Autonomous Vehicle Adoption using Interpretable Gradient Boosting Machine." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (June 23, 2019): 865–78. http://dx.doi.org/10.1177/0361198119857953.
Abstract:
This article applies machine learning (ML) to develop a choice model on three choice alternatives related to autonomous vehicles (AV): regular vehicle (REG), private AV (PAV), and shared AV (SAV). The learned model is used to examine users’ preferences and behaviors on AV uptake by car commuters. Specifically, this study applies gradient boosting machine (GBM) to stated preference (SP) survey data (i.e., panel data). GBM notably possesses more interpretable features than other ML methods as well as high predictive performance for panel data. The prediction performance of GBM is evaluated by conducting a 5-fold cross-validation and shows around 80% accuracy. To interpret users’ behaviors, variable importance (VI) and partial dependence (PD) were measured. The results of VI indicate that trip cost, purchase cost, and subscription cost are the most influential variables in selecting an alternative. Moreover, the attitudinal variables Pro-AV Sentiment and Environmental Concern are also shown to be significant. The article also examines the sensitivity of choice by using the PD of the log-odds on selected important factors. The results inform both the modeling of transportation technology uptake and the configuration and interpretation of GBM that can be applied for policy analysis.
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Gupta, Samar, and Jitendra Kumar Verma. "Reverse Traceability Framework for Identifying Liability of Crashes for Self-Driving Vehicles Using Blockchains." Journal of Global Information Management 31, no. 7 (September 8, 2023): 1–21. http://dx.doi.org/10.4018/jgim.329961.
Abstract:
Modern vehicles are increasingly having a higher level of technology and automation. Humans are increasingly becoming dependent on these modern technologies to take decisions related to their lives and safety. Such an increasing dependence on automation raises an important question. If an autonomous vehicle (AV) meets an accident, who will be responsible? It is not the human driver, but technology that makes those crucial decisions on the road. This question is attracting considerable attention in the insurance industry because traditional vehicle insurance is based on the liability of human drivers, but in the future, vehicle technology will replace human drivers. Therefore, the vehicle manufacturer or one of its suppliers may be held responsible for the accident. This paper presents a crash liability identification framework that can identify who is liable if there is a crash or an accident of an autonomous self-driving vehicle. The use cases demonstrate that the proposed framework can be used by regulators to efficiently identify the liable party when an AV crashes.
25
Ramjan, Sarawut, and Purimprach Sangkaew. "Understanding the adoption of autonomous vehicles in Thailand: an extended TAM approach." Engineering Management in Production and Services 14, no. 1 (March 1, 2022): 49–62. http://dx.doi.org/10.2478/emj-2022-0005.
Abstract:
Abstract Autonomous vehicles (AVs) are receiving attention in many countries, including Thailand. However, implementing an intelligent transport system has many challenges, such as safety and reliability and the lack of policy supporting such technology use, leading to hazards for passengers and pedestrians. Hence, factors affecting the adoption of autonomous vehicles require better understanding. This research proposes and employs an extended Technology Acceptance Model (TAM) by integrating ethical standards, legal concerns, and trust to predict the intended use of autonomous vehicles by Thai citizens. A total of 318 questionnaires were collected from online panel respondents. Research hypotheses were tested using a structural equation modelling approach. The study results suggest that ethical standards have a significant positive effect on the intention to use the technology. Meanwhile, the intention was negatively affected by perceived usefulness, perceived ease of use and legal concerns. On the other hand, the results indicate that perceived ease of use directly affected trust, leading to AV adoption. However, other factors influenced trust insignificantly. This study demonstrates the vital role of trust in AV adoption. The study also suggests ideas for further study and discusses the implications for the government and autonomous vehicle companies. The article aims to forecast a success factor that the Thai government should use to consider the policy for autonomous vehicle adoption in Thailand. This paper relies on the technology acceptance model to assess and forecast autonomous vehicle adoption. The theoretical model also includes ethical issues, legal concerns and trust in technology. The model was analysed using the structure equation modelling technique to confirm the factor affecting Thailand’s successful autonomous vehicle adoption. This research confirmed that ethical standards, legal concerns, and trust in technology are the factors significantly affecting the intention to use an autonomous vehicle in Thailand. On the other hand, the perceived ease of use significantly affects the trust in autonomous vehicle technology. This research found that such social factors as ethical standards, legal concerns, and trust in technology affect technology adoption significantly, especially technology related to AI operation. Therefore, the technology acceptance model could be modified to confirm technology adoption in terms of social factors. The government could use the research results to develop a public policy for the regulation and standard supporting autonomous vehicle adoption in Thailand.
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Favero, Renan, and José Reynaldo Setti. "Factors Affecting the Impact of Autonomous Vehicles on Freeway Operations – An Exploratory Analysis Using PCEs." Promet - Traffic&Transportation 36, no. 1 (March 1, 2024): 55–68. http://dx.doi.org/10.7307/ptt.v36i1.356.
Abstract:
Autonomous vehicles (AVs) and human-driven vehicles (HDVs) will share the roads for a long time, hence the need to study traffic flows mixing AVs and HDVs, especially during the AV introduction period. This paper aims to investigate the roadway and traffic characteristics that affect the impact of AVs on freeway traffic operations, using an adapted version of the HCM6 truck passenger-car equivalent (PCE) methodology. A large number of scenarios comprising different roadway characteristics, AV types and traffic flow compositions were simulated using Vissim to obtain AV PCEs. The results indicated that, for all scenarios considered, an AV has a 20% lower impact on the quality of service and operation than an HDV. A CART decision tree indicated that the most important factors affecting the AVs’ impact on traffic operations are vehicle-to-vehicle connectivity level and the capability of travelling in platoons. Maximum platoon length did not matter, and the increase in the number of traffic lanes reduced the positive impact of AVs on service quality.
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Chng, Samuel, Sabreena Anowar, and Lynette Cheah. "Understanding Shared Autonomous Vehicle Preferences: A Comparison between Shuttles, Buses, Ridesharing and Taxis." Sustainability 14, no. 20 (October 21, 2022): 13656. http://dx.doi.org/10.3390/su142013656.
Abstract:
Shared autonomous vehicles (AVs) will soon be introduced in public transportation as cities and their transportation systems become ‘smarter’. This brings long-term environmental, economic and societal benefits to cities. However, shared AVs will not only need to overcome technological challenges but also prevail against social barriers for successful marketplace penetration. Hence, we proposed and investigated the acceptance of four shared AV service designs for public use in this study, namely, autonomous buses, shuttles, AV rideshares and autonomous or robo-taxis. An online survey conducted in Singapore with 734 adults found the greatest receptiveness toward the introduction of autonomous shuttles, in part due to perceptions that they will perform well and be easy to adopt. This aligns with ongoing shared AV trials where AV shuttles are mostly used. Larger autonomous buses had the second-highest acceptance. AV rideshares and taxis seem to largely appeal to the existing regular users of the conventional counterparts of these services. These results suggest that to encourage a mode switch from public transport to ridesharing and taxis, or vice versa, shared AVs need to appeal to users beyond being an automated version of existing modes. That is, shared AVs need to address an underserved or unmet transportation need or population.
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Abdeen, Mohammad A. R., Ansar Yasar, Mohamed Benaida, Tarek Sheltami, Dimitrios Zavantis, and Youssef El-Hansali. "Evaluating the Impacts of Autonomous Vehicles’ Market Penetration on a Complex Urban Freeway during Autonomous Vehicles’ Transition Period." Sustainability 14, no. 16 (August 15, 2022): 10094. http://dx.doi.org/10.3390/su141610094.
Abstract:
Autonomous vehicles (AVs) have been a rapidly emerging phenomenon in recent years, with some automated features already available in vehicles. AVs are expected to potentially revolutionize the existing inefficient state of urban transportation and be a step closer to environmental sustainability. This study focuses on simulation modeling in assessing the potential effects of autonomous vehicles (AVs) and on mobility and safety by developing a framework model based on traffic microsimulation for a real network located in Al-Madinah, Saudi Arabia. The market penetration rates (MPRs) will not reach 100% in the near future; instead, penetration will progressively increase. As a result, in our study, we investigated the potential effect of AV technology in five different AV market penetration rates: 0% (baseline), 25%, 50%, 75%, and 100%. The results suggest that Avs significantly improve the network’s safety and operational performance at high penetration rates. Specifically, estimated vehicle delays decreased by 26%, 34.4%, 63.7%, and 74.2% for 25%, 50%, 75%, and 100% AV penetration rates, respectively. Finally, we think this study will help decisionmakers over in the long-term in their attempts to achieve sustainable development through the optimal integration of innovative and novel technologies.
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Movaghar, Sara, Mahmoud Mesbah, and Meeghat Habibian. "Optimum Location of Autonomous Vehicle Lanes: A Model Considering Capacity Variation." Mathematical Problems in Engineering 2020 (May 12, 2020): 1–13. http://dx.doi.org/10.1155/2020/5782072.
Abstract:
This paper proposes a model to find the optimal location of autonomous vehicle lanes in a transportation network consisting of both Autonomous Vehicles (AVs) and Human-Driven Vehicles (HDVs) while accounting for the roadway capacity variation. The main contribution of the model is considering a generalized definition of capacity as a function of AV proportion on a link and incorporating it into the network design problem. A bilevel optimization model is proposed with total travel time as the objective function to be minimized. At the upper-level problem, the optimal locations of AV lanes are determined, and at the lower level which is a multiclass equilibrium assignment, road users including both AVs and HDVs seek to minimize their individual travel times. It is shown that if capacity variation is ignored, the effect of AV lane deployment can be misleading. Since there will be a long transition period during which both AVs and HDVs will coexist in the network, this model can help the network managers to optimally reallocate the valuable road space and better understand the effects of AV lane deployment at the planning horizon as well as during the transition period. Employing this model as a planning tool presents how the proposed AV lane deployment plan could consider the AV market penetration growth during the transition period. Numerical analysis based on the Sioux Falls network is presented in two cases with and without variable capacity to illustrate the application of this model. At the 60% penetration rate of AVs, the improvement in total travel time was 3.85% with a fix capacity while this improvement was 9.88% with a variable capacity.
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Ma, Yincong, Kit Guan Lim, Min Keng Tan, Helen Sin Ee Chuo, Ali Farzamnia, and Kenneth Tze Kin Teo. "Research on Risk Detection of Autonomous Vehicle Based on Rapidly-Exploring Random Tree." Computation 11, no. 3 (March 17, 2023): 61. http://dx.doi.org/10.3390/computation11030061.
Abstract:
There is no doubt that the autonomous vehicle is an important developing direction of the auto industry, and, thus, more and more scholars are paying attention to doing more research in this field. Since path planning plays a key role in the operation of autonomous vehicles, scholars attach great importance to this field. Although it has been applied in many fields, there are still some problems, such as low efficiency of path planning and collision risk during driving. In order to solve these problems, an automotive vehicle-based rapid exploration random tree (AV-RRT)-based non-particle path planning method for autonomous vehicles is proposed. On the premise of ensuring safety and meeting the requirements of the vehicle’s kinematic constraints through the expansion of obstacles, the dynamic step size is used for random tree growth. A non-particle collision detection (NPCD) collision detection algorithm and path modification (PM) path modification strategy are proposed for the collision risk in the turning process, and geometric constraints are used to represent the possible security threats, so as to improve the efficiency and safety of vehicle global path driving and to provide reference for the research of driverless vehicles.
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Polydoropoulou, Amalia, Ioannis Tsouros, Nikolas Thomopoulos, Cristina Pronello, Arnór Elvarsson, Haraldur Sigþórsson, Nima Dadashzadeh, et al. "Who Is Willing to Share Their AV? Insights about Gender Differences among Seven Countries." Sustainability 13, no. 9 (April 23, 2021): 4769. http://dx.doi.org/10.3390/su13094769.
Abstract:
The introduction of shared autonomous vehicles into the transport system is suggested to bring significant impacts on traffic conditions, road safety and emissions, as well as overall reshaping travel behaviour. Compared with a private autonomous vehicle, a shared automated vehicle (SAV) is associated with different willingness-to-adopt and willingness-to-pay characteristics. An important aspect of future SAV adoption is the presence of other passengers in the SAV—often people unknown to the cotravellers. This study presents a cross-country exploration of user preferences and WTP calculations regarding mode choice between a private non-autonomous vehicle, and private and shared autonomous vehicles. To explore user preferences, the study launched a survey in seven European countries, including a stated-preference experiment of user choices. To model and quantify the effect of travel mode attributes and socio-demographic characteristics, the study employs a mixed logit model. The model results were the basis for calculating willingness-to-pay values for all countries and travel modes, and provide insight into the significant heterogeneous, gender-wise effect of cotravellers in the choice to use an SAV. The study results highlight the importance of analysis of the effect of SAV attributes and shared-ride conditions on the future acceptance and adoption rates of such services.
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Fayyad, Jamil, Mohammad A. Jaradat, Dominique Gruyer, and Homayoun Najjaran. "Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review." Sensors 20, no. 15 (July 29, 2020): 4220. http://dx.doi.org/10.3390/s20154220.
Abstract:
Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.
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Kindi, Husnul Khotami, Nungki Selviandro, and Gia Wulandari. "AUTONOMOUS VEHICLE SIMULATION WITH MULTI HUMAN DRIVING BEHAVIOR USING DEEP LEARNING." JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 8, no. 3 (August 30, 2023): 755–63. http://dx.doi.org/10.29100/jipi.v8i3.3900.
Abstract:
Advances in Autonomous Vehicle (AV) technology have made this topic popular in recent years, both large and small companies have started to develop this AV technology. Apart from large companies, several researchers are also interested in developing this technology. However, due to cost constraints and security issues, the researchers developed AV using a computer simulation approach. The main objective of this paper is to create a simulation (AV). The simulation was created using Udacity self-driving-car from Unity 3D. The first step we took was to take a dataset in the form of images from a number of participants by manually driving a car in a simulation to get Human-driving-behavior. After the dataset is obtained, the AV model formation process will then be carried out using the deep learning method of the Convolutional Neural Network algorithm. In this research, a good AV simulation has been successfully made, the car can run perfectly following the track without experiencing a collision or going off the track. From the results of the testing carried out, the model that was built got pretty good results where the accuracy was 71% and the loss was 0.0165.
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Fatima, H. Sundus, Ammar N. Abbas, Faraz Bawany, Huma Zia, Syed Adnan Yusuf, and Muhammad Khurram. "Autonomous vehicle navigation using high-definition maps through CARLA-ROS simulator bridge." Journal of Physics: Conference Series 2330, no. 1 (August 1, 2022): 012016. http://dx.doi.org/10.1088/1742-6596/2330/1/012016.
Abstract:
Abstract Autonomous vehicles (AV) have garnered significant interest in recent years due to its potential for controlling thousands of accidents that happen yearly due to human error. However, AV bring with it very complex and sophisticated requirements and challenges related to extensive testing of the algorithms and hardware in the physical world. The evolution of automotive simulation tools provides an opportunity to fully test and validate AV architectures without the risk of creating hazardous situations in real world. This research demonstrates the application of HD (High Definition) maps in autonomous vehicle navigation using ROS interface integrated with the CARLA (CAR Learning to Act) simulator. The sensor data includes Light Detection and Ranging (LiDAR), RGB-Depth (RGB-D), and vehicle odometry. HD maps play an important role in robustness of the autonomous systems where due to sensor obstruction or weather conditions the vehicle is unable to perceive the information ahead. It also aids the vehicle to sense its environment even outside from the sensor’s field of view. The research is divided into the three fundamental concepts of Simultaneous Localization and Mapping (SLAM) approach that is, (i) mapping, (ii) localization, and (iii) navigation. Two ROS tools are used for mapping, (a) OctoMap mapping, and (ii) Real-Time Appearance-Based Mapping (RTAB-Map). We demonstrate the effectiveness of localization using RTAB-Map and compare actual path, position and orientation to their estimated equivalents. Our results show acceptable error in XY axes and exemplifies the error accumulated in Z axis.
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Muslim, Husam, and Jacobo Antona-Makoshi. "A Review of Vehicle-to-Vulnerable Road User Collisions on Limited-Access Highways to Support the Development of Automated Vehicle Safety Assessments." Safety 8, no. 2 (April 6, 2022): 26. http://dx.doi.org/10.3390/safety8020026.
Abstract:
This study aims to provide evidence to support the development of automated vehicle (AV) safety assessments that consider the possible presence of non-motorized vulnerable road-users (VRUs) on limited-access highways. Although limited-access highways are designed to accommodate high-speed motor vehicles, collisions involving VRUs on such roadways are frequently reported. A narrative review is conducted, covering the epidemiology of VRUs crashes on limited-access highways to identify typical crash patterns considering collisions severity and the underlying reasons for the VRUs to use the highway. The review results show that occupants alighting from a disabled or crashed vehicle, people seeking help or helping others, highway maintenance zones, police stops, and people crossing a highway should be given priority to ensure VRU safety on limited-access highways. The results are summarized in figures with schematic models to generate test scenarios for AV safety assessment. Additionally, the results are discussed using two examples of traffic situations relevant to the potential AV-VRU crashes on highways and the current performance of autonomous emergency braking and autonomous emergency steering systems. These findings have important implications for producing scenarios in which AV may not produce crashes lest it performs worse than human drivers in the proposed scenarios.
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Sun, Xu, Jingpeng Li, Pinyan Tang, Siyuan Zhou, Xiangjun Peng, Hao Nan Li, and Qingfeng Wang. "Exploring Personalised Autonomous Vehicles to Influence User Trust." Cognitive Computation 12, no. 6 (September 1, 2020): 1170–86. http://dx.doi.org/10.1007/s12559-020-09757-x.
Abstract:
AbstractTrust is a major determinant of acceptance of an autonomous vehicle (AV), and a lack of appropriate trust could prevent drivers and society in general from taking advantage of such technology. This paper makes a new attempt to explore the effects of personalised AVs as a novel approach to the cognitive underpinnings of drivers’ trust in AVs. The personalised AV system is able to identify the driving behaviours of users and thus adapt the driving style of the AV accordingly. A prototype of a personalised AV was designed and evaluated in a lab-based experimental study of 36 human drivers, which investigated the impact of the personalised AV on user trust when compared with manual human driving and non-personalised AVs. The findings show that a personalised AV appears to be significantly more reliable through accepting and understanding each driver’s behaviour, which could thereby increase a user’s willingness to trust the system. Furthermore, a personalised AV brings a sense of familiarity by making the system more recognisable and easier for users to estimate the quality of the automated system. Personalisation parameters were also explored and discussed to support the design of AV systems to be more socially acceptable and trustworthy.
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Liu, Chao, Hongfei Jia, Yu Lin, Guanfeng Wang, Jingjing Tian, Ruiyi Wu, and Heyao Gao. "Policy Efforts to Promote the Adoption of Autonomous Vehicles: Subsidy and AV Lanes." Journal of Advanced Transportation 2023 (August 18, 2023): 1–17. http://dx.doi.org/10.1155/2023/5658495.
Abstract:
Autonomous vehicles (AVs) have the potential to improve safety, traffic capacity, and energy efficiency, but these advantages can only be realized when the AV market penetration is sufficiently high. To promote the adoption of AVs, it would be crucial for the government to take policy measures. This paper develops a two-stage model to explore the effects of subsidy and AV lanes’ policies on AV adoption. In the first stage, given the subsidy policy, the vehicle manufacturer sets AV price to pursue maximum profit while anticipating the choice of consumers for AVs or conventional vehicles (CVs), from which the AV market penetration can be accessed. Subsequently, based on the AV market penetration acquired from the first stage model, an optimization model integrating the mixed traffic assignment is developed in the second stage to determine the time-dependent progressive AV lanes’ deployment plan. The first and second stage models are solved using the simulation-optimization and genetic-algorithm-based approaches, respectively. Due to the mutual influence of the two models, an iterative optimization approach is applied to solve the whole model. Two numerical experiments are conducted, and the results demonstrate the positive effects of subsidy and AV lane policies on increasing AV market penetration. The analysis provides significant managerial insights for policymakers to promote the development of AVs.
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Lowe, Evan, and Levent Guvenc. "Autonomous Vehicle Emergency Obstacle Avoidance Maneuver Framework at Highway Speeds." Electronics 12, no. 23 (November 24, 2023): 4765. http://dx.doi.org/10.3390/electronics12234765.
Abstract:
An autonomous vehicle (AV) uses high-level decision making and lower-level actuator controls, such as throttle (acceleration), braking (deceleration), and steering (change in lateral direction) to navigate through various types of road networks. Path planning and path following for highway driving are currently available in series-produced highly automated vehicles. In addition to these, emergency collision avoidance decision making and maneuvering are another key and essential feature that is needed in a series production AV at highway driving speeds. For reliability, low cost, and fast computation, such an emergency obstacle avoidance maneuvering system should use well-established conventional methods as opposed to data-driven neural networks or reinforcement learning methods, which are currently not suitable for use in highway AV driving. This paper presents a novel Emergency Obstacle Avoidance Maneuver (EOAM) methodology for AVs traveling at higher speeds and lower road surface friction, involving time-critical maneuver determination and control. The proposed EOAM framework offers usage of the AV’s sensing, perception, control, and actuation system abilities as one cohesive system to avoid an on-road obstacle, based first on performance feasibility and second on passenger comfort, and it is designed to be well integrated within an AV’s high-level control and decision-making system. To demonstrate the efficacy of the proposed method, co-simulation including the AV’s EOAM logic in Simulink and a vehicle model in CarSim is conducted with speeds ranging from 55 to 165 km/h and on road surfaces with friction ranging from 1.0 to 0.1. The results are analyzed and interpreted in the context of an entire AV system, with implications for future work.
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Yu, Yuewen, Shikun Liu, Peter J. Jin, Xia Luo, and Mengxue Wang. "Multi-Player Dynamic Game-Based Automatic Lane-Changing Decision Model under Mixed Autonomous Vehicle and Human-Driven Vehicle Environment." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 11 (August 24, 2020): 165–83. http://dx.doi.org/10.1177/0361198120940990.
Abstract:
The lane-changing decision-making process is challenging but critical to ensure safe and smooth maneuvers for autonomous vehicles (AVs). Conventional Gipps-type algorithms lack the flexibility for practical use under a mixed autonomous vehicle and human-driven vehicle (AV-HV) environment. Algorithms based on utility ignore the reactions of surrounding vehicles to the lane-changing vehicle. Game theory is a good way to solve the shortcomings of current algorithms, but most models based on game theory simplify the game with surrounding vehicles to the game with the following vehicle in the target lane, which means that the lane-changing decision under a mixed environment is not realized. This paper proposes a lane-changing decision-making model which is suitable for an AV to change lanes under a mixed environment based on a multi-player dynamic game theory. The overtaking expectation parameter (OEP) is introduced to estimate the utility of the following vehicle, OEP can be calculated by the proposed non-lane-based full velocity difference model with the consideration of lateral move and aggressiveness. This paper further proposes a hybrid splitting method algorithm to obtain the Nash equilibrium solution in the multi-player game to obtain the optimal strategy of lane-changing decision for AVs. An adaptive cruise control simulation environment is developed with MATLAB’s Simulink toolbox using Next Generation Simulation (NGSIM) data as the background traffic flow. The classic bicycle model is used in the control of involved HVs. Simulation results show the efficiency of the proposed multi-player dynamic game-based algorithm for lane-changing decision making by AVs under a mixed AV-HV environment.
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Prabhu, Dr B. Dinesh, Pradhyumna UR, and Dr N. Jagadeesh. "A REVIEW ON IMPERATIVE FACTORS THAT AFFECT THE FEASIBILITY OF AUTONOMOUS VEHICLES IN INDIA." International Journal of Engineering Applied Sciences and Technology 7, no. 7 (November 1, 2022): 43–52. http://dx.doi.org/10.33564/ijeast.2022.v07i07.008.
Abstract:
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.
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Volkhonskaya, Elizaveta Evgenievna, and Sergey Pavlovich Orlov. "Autonomous vehicle fleet system models for virtual tests in organizing maintenance." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2023, no. 3 (July 28, 2023): 7–16. http://dx.doi.org/10.24143/2072-9502-2023-3-7-16.
Abstract:
The article considers the problem of managing a fleet of agricultural autonomous vehicles (AV) and unmanned combine harvesters. The maintenance system of such a fleet should ensure the implementation of various strategies for the operation, maintenance, and repair of vehicles. To minimize costs and reduce design time, virtual tests based on digital twins in the form of mathematical and simulation models are widely used. A set of system models used in the virtual testing of AV at the stage of virtual commissioning is proposed. The complex includes two optimization models and a simulation model of the agrotechnical production-logistics system. The problem of finding the optimal calendar network schedule for agrotechnical work and the problem of assigning AV to production work are formulated and solved. The simulation model is designed as a stochastic timed colored Petri net. The structure of the hierarchical simulation model is given and the maintenance model based on the Petri net is described. Virtual tests are carried out as a statistical experiment on a simulation model. There is given an example of the resulting graphs of the AV operating rate, which allows determining the periods between preventive maintenance. The choice of the apparatus of stochastic colored Petri nets as the basis of simulation models is due to the convenience and possibilities of setting probabilistic laws for production processes and the processes of AV units’ degradation and deterioration. A formal description of AV in the form of multisets with components corresponding to time characteristics and cost parameters is proposed. Conducting virtual tests on the developed models makes it possible to formulate requirements and recommendations for organizing the operation and maintenance of AV.
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Cohn, Jesse, Richard Ezike, Jeremy Martin, Kwasi Donkor, Matthew Ridgway, and Melissa Balding. "Examining the Equity Impacts of Autonomous Vehicles: A Travel Demand Model Approach." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (March 19, 2019): 23–35. http://dx.doi.org/10.1177/0361198119836971.
Abstract:
As investments in autonomous vehicle (AV) technology continue to grow, agencies are beginning to consider how AVs will affect travel behavior within their jurisdictions and how to respond to this new mobility technology. Different autonomous futures could reduce, perpetuate, or exacerbate existing transportation inequities. This paper presents a regional travel demand model used to quantify how transportation outcomes may differ for disadvantaged populations in the Washington, D.C. area under a variety of future scenarios. Transportation performance measures examined included job accessibility, trip duration, trip distance, mode share, and vehicle miles traveled. The model evaluated changes in these indicators for disadvantaged and non-disadvantaged communities under scenarios when AVs were primarily single-occupancy or high-occupancy, and according to whether transit agencies responded to AVs by maintaining the status quo, removing low-performing routes, or applying AV technology to transit vehicles. Across the performance measures, the high-occupancy AV and enhanced transit scenarios provided an equity benefit, either mitigating an existing gap in outcomes between demographic groups or reducing the extent to which that gap was expanded.
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Wang, Jun, Li Zhang, Yanjun Huang, Jian Zhao, and Francesco Bella. "Safety of Autonomous Vehicles." Journal of Advanced Transportation 2020 (September 15, 2020): 1–13. http://dx.doi.org/10.1155/2020/8867757.
Abstract:
Autonomous vehicle (AV) is regarded as the ultimate solution to future automotive engineering; however, safety still remains the key challenge for the development and commercialization of the AVs. Therefore, a comprehensive understanding of the development status of AVs and reported accidents is becoming urgent. In this article, the levels of automation are reviewed according to the role of the automated system in the autonomous driving process, which will affect the frequency of the disengagements and accidents when driving in autonomous modes. Additionally, the public on-road AV accident reports are statistically analyzed. The results show that over 3.7 million miles have been tested for AVs by various manufacturers from 2014 to 2018. The AVs are frequently taken over by drivers if they deem necessary, and the disengagement frequency varies significantly from 2 × 10−4 to 3 disengagements per mile for different manufacturers. In addition, 128 accidents in 2014–2018 are studied, and about 63% of the total accidents are caused in autonomous mode. A small fraction of the total accidents (∼6%) is directly related to the AVs, while 94% of the accidents are passively initiated by the other parties, including pedestrians, cyclists, motorcycles, and conventional vehicles. These safety risks identified during on-road testing, represented by disengagements and actual accidents, indicate that the passive accidents which are caused by other road users are the majority. The capability of AVs to alert and avoid safety risks caused by the other parties and to make safe decisions to prevent possible fatal accidents would significantly improve the safety of AVs. Practical applications. This literature review summarizes the safety-related issues for AVs by theoretical analysis of the AV systems and statistical investigation of the disengagement and accident reports for on-road testing, and the findings will help inform future research efforts for AV developments.
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Abu Kassim, K. A., Z. Mohd Jawi, and M. A. Nasruddin. "Is Malaysia Ready to Adopt Autonomous Vehicles?" Journal of the Society of Automotive Engineers Malaysia 3, no. 1 (April 29, 2021): 84–88. http://dx.doi.org/10.56381/jsaem.v3i1.102.
Abstract:
Malaysia is a rapidly developing country with the potential for growth of future technologies. Autonomous Vehicle (AV) is one of these new technologies which evokes both excitement and apprehension among the general public. This paper shall discuss Malaysia's preparedness to accept AV on its roads. Finally, this paper will also explain the possible steps to facilitate efforts to have AVs on Malaysia's roads in the imminent future.
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Ataya, Aya, Won Kim, Ahmed Elsharkawy, and SeungJun Kim. "Gaze-Head Input: Examining Potential Interaction with Immediate Experience Sampling in an Autonomous Vehicle." Applied Sciences 10, no. 24 (December 17, 2020): 9011. http://dx.doi.org/10.3390/app10249011.
Abstract:
Autonomous vehicles (AV) increasingly allow drivers to engage in secondary tasks such as eating or working on a laptop and thus require easy and reliable interaction inputs to facilitate communication between the driver and the vehicle. However, drivers report feeling less in control when driving is no longer the primary task, which suggests that novel approaches for assessing satisfaction regarding AV decision-making are needed. Therefore, we propose an immediate experience sampling method (IESM) that learns driver preferences for AV actions. We also suggest gaze-head input (G-HI) as a novel input in an AV. G-HI provides a hands-free, remote, and intuitive input modality that allows drivers to interact with the AV while continuing to engage in non-driving related tasks. We compare G-HI with voice and touch inputs via IESM for two simulated driving scenarios. Our results report the differences among the three inputs in terms of system usability, reaction time, and perceived workload. It also reveals that G-HI is a promising candidate for AV input interaction, which could replace voice or touch inputs where those inputs could not be utilized. Variation in driver satisfaction and expectations for AV actions confirms the effectiveness of using IESM to increase drivers’ sense of control.
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Zali, Nader, Sara Amiri, Tan Yigitcanlar, and Ali Soltani. "Autonomous Vehicle Adoption in Developing Countries: Futurist Insights." Energies 15, no. 22 (November 12, 2022): 8464. http://dx.doi.org/10.3390/en15228464.
Abstract:
In recent years, research into autonomous vehicles (AVs) has become highly popular in industrialized nations due to their importance in the future success of smart cities. Research on this mobility technology and the critical elements affecting its development in developing countries, however, has been rather limited. This paper aims to shed light on the most influential elements of AV adoption in developing nations. A structural analysis approach is used, based on the primary qualitative data—that was gathered via an expert-opinion poll utilizing the fuzzy Delphi with a snowballing method and engaging 25 experts in the field in two rounds—, in accordance with the tradition of futurist research. The analysis has led to the identification of 11 key factors, from the initial factor pool of 54, affecting AV adoption in the case study context of Iran. The results of the analysis revealed the following conclusions, that fall under the policy and legislation domain, and present the most significant issues impacting AV adoption: (a) Future orientation of government—consistency and of accountability of policies, the long-term vision of the government for developing AVs industry; (b) Managing the international sanctions for foreign investment, and; (c) Funding mega projects to enable AVs. The results indicate that the establishment of legislation and the formulation of government policy regarding the provision of infrastructure, investment, and credit allocation are crucial for the development of AVs in Iran and other developing nations with comparable characteristics.
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Boin, Christian, Lei Lei, and Simon X. Yang. "AVDDPG – Federated reinforcement learning applied to autonomous platoon control." Intelligence & Robotics 2, no. 2 (2022): 145–67. http://dx.doi.org/10.20517/ir.2022.11.
Abstract:
Since 2016 federated learning (FL) has been an evolving topic of discussion in the artificial intelligence (AI) research community. Applications of FL led to the development and study of federated reinforcement learning (FRL). Few works exist on the topic of FRL applied to autonomous vehicle (AV) platoons. In addition, most FRL works choose a single aggregation method (usually weight or gradient aggregation). We explore FRL's effectiveness as a means to improve AV platooning by designing and implementing an FRL framework atop a custom AV platoon environment. The application of FRL in AV platooning is studied under two scenarios: (1) Inter-platoon FRL (Inter-FRL) where FRL is applied to AVs across different platoons; (2) Intra-platoon FRL (Intra-FRL) where FRL is applied to AVs within a single platoon. Both Inter-FRL and Intra-FRL are applied to a custom AV platooning environment using both gradient and weight aggregation to observe the performance effects FRL can have on AV platoons relative to an AV platooning environment trained without FRL. It is concluded that Intra-FRL using weight aggregation (Intra-FRLWA) provides the best performance for controlling an AV platoon. In addition, we found that weight aggregation in FRL for AV platooning provides increases in performance relative to gradient aggregation. Finally, a performance analysis is conducted for Intra-FRLWA versus a platooning environment without FRL for platoons of length 3, 4 and 5 vehicles. It is concluded that Intra-FRLWA largely out-performs the platooning environment that is trained without FRL.
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Abdullah, Azrol, and Nazura Abdul Manap. "THE MALAYSIAN PERSPECTIVE ON IMPOSING CIVIL LIABILITIES IN ROAD ACCIDENTS INVOLVING AUTONOMOUS VEHICLE." UUM Journal of Legal Studies 12, Number 2 (July 5, 2021): 203–28. http://dx.doi.org/10.32890/uumjls2021.12.2.9.
Abstract:
The advancement of artificial intelligence (AI) technology has become the fundamental catalyst in the research and development of autonomous vehicle (AV). AVs equipped with AI are expected to perform better than humans and forecasted to reduce the number of road accidents. AV will improve humans’ quality of life, such as creating more mobility for the elderly and disabled, increasing productivity, and creating an environmentally friendly system. Despite AV’s promising abilities, reports indicate that AV can go phut, causing road fatalities to the AV user and other road users. The autonomous nature of AV exacerbates the difficulty in determining who is at fault. This article aims to examine the ability of the existing legal framework to identify the person at fault so as to determine the tortious liability in road accidents involving AV. This article demonstrated that the existing legal scheme is insufficient to determine tortious liability in road accidents involving AV. This article explored the possibility of shouldering the liability on the manufacturer, the user, and even on the AV itself. This article also investigated alternative approaches that could be adopted to resolve issues on the distribution of tortious liability in road accidents involving AV. The outcome of this article could contribute to issues relating to the liability of AI.
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Chang, Hyun-ho, and Byoung-jo Yoon. "Potentialities of Autonomous Vehicles for Online Monitoring of Motorway Traffic Volume." Journal of Advanced Transportation 2018 (September 6, 2018): 1–12. http://dx.doi.org/10.1155/2018/4276593.
Abstract:
The fact that real-time autonomous vehicle (AV) traffic volume can be collected without a field detector by virtue of advanced global positioning system (GPS) and wireless communication technologies can render a promising solution to online monitoring of traffic volume in the upcoming AV era. To demonstrate this opportunity, this paper proposes a new method to monitor real-time motorway traffic volumes for road locations where no detector is installed using AV traffic volume. The modeling concept is based on the obvious fact that AV traffic volume is a direct portion of total traffic volume. The capabilities of the method are demonstrated through an experimental study using real-world GPS-enabled smartphone vehicle navigation data. The results show that online motorway traffic volume can be effectively monitored throughout the day with 5.69% average error at the 14.91% penetration rate of AVs during the daytime. Therefore, it is expected that AVs can at least be used as complementary means for the role of vehicle detectors in the near future due to the fact that the detection range of AVs is not spatially constrained.
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Ahmed, Hafiz Usman, Ying Huang, Pan Lu, and Raj Bridgelall. "Technology Developments and Impacts of Connected and Autonomous Vehicles: An Overview." Smart Cities 5, no. 1 (March 17, 2022): 382–404. http://dx.doi.org/10.3390/smartcities5010022.
Abstract:
The scientific advancements in the vehicle and infrastructure automation industry are progressively improving nowadays to provide benefits for the end-users in terms of traffic congestion reduction, safety enhancements, stress-free travels, fuel cost savings, and smart parking, etc. The advances in connected, autonomous, and connected autonomous vehicles (CV, AV, and CAV) depend on the continuous technology developments in the advanced driving assistance systems (ADAS). A clear view of the technology developments related to the AVs will give the users insights on the evolution of the technology and predict future research needs. In this paper, firstly, a review is performed on the available ADAS technologies, their functions, and the expected benefits in the context of CVs, AVs, and CAVs such as the sensors deployed on the partial or fully automated vehicles (Radar, LiDAR, etc.), the communication systems for vehicle-to-vehicle and vehicle-to-infrastructure networking, and the adaptive and cooperative adaptive cruise control technology (ACC/CACC). Secondly, for any technologies to be applied in practical AVs related applications, this study also includes a detailed review in the state/federal guidance, legislation, and regulations toward AVs related applications. Last but not least, the impacts of CVs, AVs, and CAVs on traffic are also reviewed to evaluate the potential benefits as the AV related technologies penetrating in the market. Based on the extensive reviews in this paper, the future related research gaps in technology development and impact analysis are also discussed.