Achieving fully autonomous driving requires seamless collaboration between advanced autonomous driving and road infrastructure technologies. As the proportion of autonomous vehicles (AVs) increases, challenges may arise from their insufficient knowle...
Buses constitute a crucial component of public transportation systems in numerous urban centers. Integrating autonomous driving technology into the bus transportation ecosystem has the potential to enhance overall urban mobility. The management of mi...
Autonomous Vehicles (AVs) with their immaculate sensing and navigating capabilities are expected to revolutionize urban mobility. Despite the expected benefits, this emerging technology has certain implications pertaining to their deployment in mixed...
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 char...
Autonomous vehicles (AVs) are expected to be seen easily on our roads, providing benefits to society in the future. However, due to the challenges faced such as technical and regulatory issues, it will take a long time to reach 100% AVs, suggesting t...
Collision avoidance between vehicles is a great challenge, especially in the context of mixed driving of connected and autonomous vehicles (CAVs) and human-driven vehicles (HVs). Advances in automation and connectivity technologies provide opportunit...
Car-following is an essential trajectory control strategy for the autonomous vehicle, which not only improves traffic efficiency, but also reduces fuel consumption and emissions. However, the prediction of lane change intentions in adjacent lanes is...
Autonomous Vehicle (AV) platooning is a promising solution for enhancing traffic efficiency and safety. However, real-world deployment faces challenges due to uncertainties and delays, which can impact platoon stability and safety. This study analyze...
This study presents a TD3-driven Bilateral Control Model (TD3-BCM) aimed at improving the stability of mixed traffic flows in autonomous vehicle (AV) chains. By integrating deep reinforcement learning, TD3-BCM optimizes control strategies to reduce t...
Connected and Autonomous Vehicles (CAVs) are expected to reshape future transportation systems. During the long transition period, in which CAVs and human-driven vehicles (HVs) coexist, deploying CAV-dedicated lanes offers a promising approach to enh...
This paper offers a novel view for managing and controlling the movement of driverless, i.e., autonomous, vehicles by converting this movement to a simulated train movement moving on a rail track. It expands on the “virtual track” theory...
External human–machine interfaces (eHMIs) serve as communication bridges between autonomous vehicles (AVs) and road users, ensuring that vehicles convey information clearly to those around them. While their potential has been explored in one-to...
Mixed traffic flows are opening up new areas for research and are seen as key drivers in the field of data and services that will make roads safer and more environmentally friendly. Understanding the effects of Connected Vehicles (CVs) and Connected...
The transition to fully autonomous roadways will include a long period of mixed-autonomy traffic. Mixed-autonomy roadways pose a challenge for autonomous vehicles (AVs) which use conservative driving behaviours to safely negotiate complex scenarios....
The introduction of connected autonomous vehicles may bring opportunities and challenges to traditional traffic control instruments, like ramp metering. This paper starts by constructing the fundamental diagram for mixed-autonomy traffic based on the...
With the accelerated commercialization of autonomous vehicles, new accident types and complex risk factors have emerged beyond the scope of existing traffic safety management systems. This study aims to contribute to sustainable safety by establishin...
The proper functioning of connected and autonomous vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic...
It is of great significance to construct a networked energy-saving driving strategy method and application framework to solve the problems of traffic disorder, speed fluctuations, and high energy consumption caused by frequent acceleration, decelerat...
Mixed traffic composed of human-driven vehicles (HDVs) and CAVs will exist for an extended period before connected and autonomous vehicles (CAVs) are fully employed on the road. There is a consensus that dense fog can cause serious traffic accidents...
In this study, a novel methodology is proposed to evaluate automated driving safety in mixed traffic environments, including autonomous vehicles (AVs) and manually driven vehicles (MVs). An open-source AV dataset obtained from a real-world autonomous...
The rapid development of Connected and Autonomous Vehicles (CAVs) presents challenges in managing mixed traffic flows. Previous studies have primarily focused on mixed traffic flow involving CAVs and Human-Driven Vehicles (HDVs), or on the combinatio...
Next to environmental aspects, establishing areas for safe and economically viable automated driving in mixed-traffic settings is one major challenge for sustainable development of Autonomous Vehicles (AVs). This work investigates safety in the inter...
Integrating connected and autonomous vehicle dedicated lanes (CAVDLs) into existing road networks under mixed traffic conditions presents a complex challenge, often requiring a balance of multiple conflicting objectives. This study develops a dynamic...
Significant progress in autonomous vehicle (AV) development has been made over the years through advancements in artificial intelligence, sensor technology, and data processing; however, many challenges remain, particularly regarding road safety and...
This paper proposes a novel hybrid car-following model: the physics-informed conditional generative adversarial network (PICGAN), designed to enhance multi-step car-following modeling in mixed traffic flow scenarios. This hybrid model leverages the s...
The introduction of autonomous vehicles (AVs) on roadways will result in a mixed vehicle environment consisting of these vehicles and manual vehicles (MVs). This vehicular environment will impact intersection sight distances (ISDs) due to differences...
As autonomous vehicles proliferate in mixed traffic streams, heterogeneous flows comprising vehicles with diverse driving strategies introduce significant complexity to cascading conflict propagation, while conventional conflict risk assessment metho...
Autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) are expected to have a significant impact on highways, but their planning horizon impacts have not been fully studied in the literature. This study seeks to address this gap by invest...
Due to the escalating transportation demand and the significant ramifications of traffic congestion, there is an imperative to investigate the sources of congestion, known as “congestion bottlenecks”. The implementation of control methods...
Determining an appropriate time to execute a lane change is a critical issue for the development of Autonomous Vehicles (AVs).However, few studies have considered the rear and the front vehicle-driver’s risk perception while developing a human-...
Different methodologies are being used to study the effects of autonomous vehicles (AVs) in mixed traffic to exhibit the interactions between autonomous and human-driven vehicles (HVs). Microscopic simulation tools are popular in such an assessment,...
Connected and autonomous vehicles (CAVs) are expected to significantly improve traffic efficiency and safety. However, the overall impacts of CAVs on mixed traffic have not been clearly studied because most previous research focused on one subset of...
Automated vehicles are expected to greatly boost traffic efficiency. However, how to estimate traffic breakdown probability for the mixed flow of autonomous vehicles and human driven vehicles around ramping areas remains to be answered. In this paper...
As autonomous driving technology advances, connected and autonomous vehicles (CAVs) will coexist with human-driven vehicles (HDVs) for an extended period. The deployment of CAVs will alter traffic flow characteristics, making it crucial to investigat...
With recent technological advancements, the commercialization of autonomous vehicles (AVs) is expected to be realized soon. However, it is anticipated that a mixed traffic of AVs and human-driven vehicles (HVs) will persist for a considerable period...
In the transition era towards connected autonomous vehicles (CAVs), the sharing of the roadway by CAVs and human-driven vehicles (HDVs) in a mixed-traffic stream is expected to pose safety and flow efficiency concerns even though CAVs may tend to ado...
As the transition towards connected and autonomous vehicles gradually happens, different phases with CAVs and human-driven vehicles sharing the same network will occur. This paper’s purpose is to increase the knowledge of these mixed situations...
The continuous increase in the penetration rate of autonomous vehicles in highway traffic flow has become an irreversible development trend; in this paper, a novel hybrid prediction model of deep sequence learning and an integrated decision tree is p...
This study proposes a new method to improve the accuracy of car-following models in predicting the mobility of mixed traffic flow involving trucks and automated vehicles (AVs). A classification is developed to categorize car-following behaviors into...
In comparison to conventional human-driven vehicles (HVs), connected and automated vehicles (CAVs) provide benefits (e.g., reducing travel time and improving safety). However, before the period of fully CAVs appears, there will be a situation in whic...
Autonomous vehicles have the potential to significantly improve modes of transportation, and many businesses and research facilities are developing such systems. Although there are studies on the social implementation of autonomous vehicles, these st...
In order to explore the changes that autonomous vehicles would bring to the current traffic system, we analyze the car-following behavior of different traffic scenarios based on an anti-collision theory and establish a traffic flow model with an arbi...
This study analyzes the impact of autonomous-vehicle (AV) market-penetration rates on traffic-flow safety using a genetic algorithm. We set up a microscopic traffic-simulation scenario on a 640 m section of the US I-101 freeway using VISSIM, a micros...
As the market penetration rate (MPR) of autonomous vehicles increases, it is expected that the safety of mixed traffic situations will change due to interactions between vehicles. A proactive safety analysis of mixed traffic situations is needed for...
Given the rapid development of technologies such as new energy vehicles, autonomous driving, and vehicle-to-everything (V2X) communication, a mixed traffic flow comprising connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs) is a...
Based on analytical and simulation methods, this paper discusses the path choice behavior of mixed traffic flow with autonomous vehicles, advanced traveler information systems (ATIS) vehicles and ordinary vehicles, aiming to promote the development o...
The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicl...
This study investigates the stability and performance of mixed-traffic flows consisting of human-driven vehicles (HDVs), connected autonomous vehicles (CAVs), autonomous vehicles (AVs), and connected human-driven vehicles (CHVs). Recognizing the comp...
Autonomous driving technology is vital for intelligent transportation systems. Vehicle driving behavior prediction is the foundation and core of autonomous driving. A detailed review of the existing research on vehicle driving behavior prediction can...
This study analyzed the impact of human drivers’ Keep Right Rule noncompliance on sustainable freeway operations in mixed traffic. Using the microscopic traffic simulation tool, a total of 36 scenarios were examined based on variations in drivi...
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