Decision-Making of Drivers Following Autonomous Vehicles: Developing a Bayesian Network on the Basis of Field Tests and Questionnaire Data

As a currently emerging technology and an emerging intelligent mode of transport, autonomous vehicles (AVs) with lots of potential advantages need attention in terms of acceptability of their users. This research incorporates three psychological factors of perceived trust, perceived value, and perceived enjoyment into the technology acceptance model, and explores the influence of these factors on the potential use intention of AVs users. In this study, the questionnaire data from 232 participants were analysed, and the structural equation model test study model was adopted, and nine hypotheses proposed in this study were verified. The results show that perceived enjoyment, perceived trust, perceived usefulness, and attitude have a direct positive impact on users' usage intentions. Perceived value, perceived usefulness, and perceived ease of use have a direct positive impact on user attitudes. In addition, perceived ease of use has also been shown to directly affect perceived usefulness. This study constructs and demonstrates a model of autonomous vehicle acceptance. This model can be used for user acceptance research of unmanned vehicles. The research expands the theory of technology acceptance model and its applicable fields, and enriches the theory of user research on unmanned vehicles. This study provides predictors of AVs acceptance for AVs designers, automakers, automotive policy makers, and related practitioners. Help them make actionable autonomous vehicle-related decisions to promote high-acceptance autonomous vehicle design and user intent for autonomous vehicles.

Stated preference analysis of autonomous vehicle among bicyclists and pedestrians in Pittsburgh using Bayesian Networks

In autonomous driving vehicles, the driver can engage in non-driving-related tasks and does not have to pay attention to the driving conditions or engage in manual driving. If an unexpected situation arises that the autonomous vehicle cannot manage, then the vehicle should notify and help the driver to prepare themselves for retaking manual control of the vehicle. Several effective notification methods based on multimodal warning systems have been reported. In this paper, we propose an advanced method that employs alarms for specific conditions by analyzing the differences in the driver’s responses, based on their specific situation, to trigger visual and auditory alarms in autonomous vehicles. Using a driving simulation, we carried out human-in-the-loop experiments that included a total of 38 drivers and 2 scenarios (namely drowsiness and distraction scenarios), each of which included a control-switching stage for implementing an alarm during autonomous driving. Reaction time, gaze indicator, and questionnaire data were collected, and electroencephalography measurements were performed to verify the drowsiness. Based on the experimental results, the drivers exhibited a high alertness to the auditory alarms in both the drowsy and distracted conditions, and the change in the gaze indicator was higher in the distraction condition. The results of this study show that there was a distinct difference between the driver’s response to the alarms signaled in the drowsy and distracted conditions. Accordingly, we propose an advanced notification method and future goals for further investigation on vehicle alarms.

Robotic vehicles and especially autonomous robotic vehicles can be attractive targets for attacks that cross the cyber-physical divide, that is cyber attacks or sensory channel attacks affecting the ability to navigate or complete a mission. Detection of such threats is typically limited to knowledge-based and vehicle-specific methods, which are applicable to only specific known attacks, or methods that require computation power that is prohibitive for resource-constrained vehicles. Here, we present a method based on Bayesian Networks that can not only tell whether an autonomous vehicle is under attack, but also whether the attack has originated from the cyber or the physical domain. We demonstrate the feasibility of the approach on an autonomous robotic vehicle built in accordance with the Generic Vehicle Architecture specification and equipped with a variety of popular communication and sensing technologies. The results of experiments involving command injection, rogue node and magnetic interference attacks show that the approach is promising.

Modeling and analysis of human-machine mixed traffic flow considering the influence of the trust level toward autonomous vehicles

Longitudinal traffic conflict analysis of autonomous and traditional vehicle platoons in field tests via surrogate safety measures

The overall aim of the ROSM project is the implementation of an innovative solution based on heterogeneous autonomous vehicles to tackle maritime pollution (in particular, oil spills). These solutions will be based on native microbial consortia with bioremediation capacity, and the adaptation of air and surface autonomous vehicles for in-situ release of autochthonous microorganisms (bioaugmentation) and nutrients (biostimulation). By doing so, these systems can be used as the first line of the responder to pollution incidents from several origins that may occur inside ports, around industrial and extraction facilities, or during transport activities, in a fast, efficient and low-cost way. The paper will address the development of a team of autonomous vehicles able to carry, as payload, native organisms to naturally degrade oil spills (avoiding the introduction of additional chemical or biological additives), the development of a multi-robot system able to provide a first line responses to oil spill incidents under unfavourable and harsh conditions with low human intervention, and then a decentralized cooperative planning with the ability to coordinate an efficient oil spill combat. Field tests have been performed in Leixões Harbour in Porto and Medas, Portugal, with a simulated oil spill and validated the decentralized coordinated task between the autonomous surface vehicle (ASV) ROAZ and the unmanned aerial vehicle (UAV).

Underwater localization is one of the main problems that must be addressed in subsea exploration, where no global positioning system (GPS) is available. In addition to the traditional underwater localization systems, such as long base line (LBL), new methods have been developed to increase the navigation performance and flexibility and to reduce the deployment costs. For example, range-only and single-beacon (ROSB) is based on an autonomous vehicle that localizes and tracks different underwater targets using slant range measurements carried out with acoustic modems. This paper presents different strategies to improve ROSB tracking methods. The ROSB target tracking method can be seen as a hidden Markov model (HMM) problem. Using Bayes’ rule, the probability distribution function of the HMM states can be solved by using different filtering methods. Here, we present and compare different methods under different scenarios, both evaluated in simulations and field tests. The main mathematical notation and performance of each algorithm are presented, where best practice has been derived. From a methodological point of view, this paper advanced the understanding of accuracy that can be achieved by using the ROSB target tracking methods with autonomous underwater vehicles.

This paper presents the implementation of an innovative solution based on heterogeneous autonomous vehicles to tackle maritime pollution (in particular, oil spills). This solution is based on native microbial consortia with bioremediation capacity, and the adaptation of air and surface autonomous vehicles for in situ release of autochthonous microorganisms (bioaugmentation) and nutrients (biostimulation). By doing so, these systems can be applied as the first line of the response to pollution incidents from several origins that may occur inside ports, around industrial and extraction facilities, or in the open sea during transport activities in a fast, efficient, and low-cost way. The paper describes the work done in the development of a team of autonomous vehicles able to carry as payload, native organisms to naturally degrade oil spills (avoiding the introduction of additional chemical or biological additives), and the development of a multi-robot framework for efficient oil spill mitigation. Field tests have been performed in Portugal and Spain’s harbors, with a simulated oil spill, and the coordinate oil spill task between the autonomous surface vehicle (ASV) ROAZ and the unmanned aerial vehicle (UAV) STORK has been validated.

Autonomous vehicles (AV) are being widely tested around the world. California is one of the jurisdictions permitting extensive AV testing. Field testing resulted in approximately 0.02 AV crashes per 1,000 AV miles traveled, as well as incidents leading to driver disengagement of the AV systems. Factors related to human error, system failure, surrounding vehicles, and roadway features could cause an AV-involved crash to occur or result in pre-crash disengagement. This study focuses on AV crashes in which the AV was operating in active AV mode or shortly after the test operator had disengaged the AV to resume conventional control. AV crash data were extracted for the years 2017 to 2021 from California’s AV crash database maintained by the California Department of Motor Vehicles (DMV) to account for the rapid pace of AV development and focus on current causes of incidents. This paper utilized multiple statistical approaches to quantitatively investigate AV crashes and disengagement events. Investigation of latent manifest using exploratory factor analysis (EFA) was conducted and ordinal logit models and decision trees were employed in this study. EFA clarified latent variables that could identify an AV crash, in which operator involvement, incorrect maneuver decision, crash severity, and environmental conditions were the manifests obtained from the analysis. Results from the logistic regression and the decision trees showed that collision type, AV movement type, and other vehicle movement type are significant factors contributing to AV crashes.

Multi-source and heterogeneous information fusion (MSHIF) is a critical approach for enhancing the performance of autonomous vehicles (AVs), particularly in environmental perception and decision-making. This review discusses the potential of AVs in reducing carbon emissions and traffic flow through the revolution of transportation systems. Various types of sensors in AV systems are determined in this review. They are cameras, LiDAR, MMW-Radar, and GPS/IMU modules. Multiple fusion algorithms are employed to harness the full potential of the sensors, such as Kalman filtering, particle filters, and Bayesian networks. These sensors significantly enhance the accuracy and reliability of AV operations; however, addressing their inherent challenges and exploring future research directions in the AV domain are essential. AVs require real-time data processing so that rapid decision-making can be made to handle the dynamic environments. It is also crucial to be concerned about the advancements in computational efficiency and algorithmic sophistication. Cybersecurity emerges as another critical concern, given the increasing connectivity of AVs to external networks. Besides that, the integration of blockchain technology is also addressed in this review to enhance security measures and facilitate transparent data sharing among AV stakeholders. Last but not least, ethical considerations surrounding AI-driven decision-making in AVs are also discussed because human safety needs to be prioritized for establishing ethical guidelines. Further studies and development for AVs could focus on sensor fusion techniques, cybersecurity, and ethical frameworks. The advancements will not only enhance the safety and reliability of AV systems but also pave the way for their widespread adoption in future transportation ecosystems.

What is the public really concerned about the AV crash? Insights from a combined analysis of social media and questionnaire survey

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Associating stated preferences of emerging mobility options among Gilbert City residents using Bayesian Networks

Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers’ performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California’s Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles.