Safety and feasibility of in-hospital autonomous transportation using a driverless mobility for patients with musculoskeletal disorders: preliminary clinical study to achieve mobility as a service in medical care.

BACKGROUND: Recent advancements in and the proliferation of autonomous mobility technology, such as intelligent wheelchairs, have made it possible to provide mobility services for patients with reduced mobility due to musculoskeletal disorders. In the present study, we conducted a preliminary clinical study to assess the safety and feasibility of in-hospital autonomous transportation using a driverless mobility (wheelchair) for patients with musculoskeletal disorders. METHODS: From January to February 2022, 51 patients with musculoskeletal disorders exhibiting gait disturbance who presented to our institution were included in the present study. Driverless mobility rides were conducted over a straight-line distance of 100 m from the orthopaedic outpatient reception to the payment counter after the outpatient consultation. We assessed the quality of life using an EQ-5D-5 L index and pain using a VAS score before riding the mobility to investigate the patient's condition. After the ride, a questionnaire survey was conducted to assess patient satisfaction on a 5-point scale. In addition, adverse events during the mobility ride were investigated. RESULTS: Overall satisfaction levels showed that 44 out of 51 (86%) patients rated the level as 3 or higher. There were no significant differences in the level of satisfaction based on the cause of disorders or EQ-5D-5 L Index. Among 19 patients who rated the level of satisfaction as 2-3, the ratio of postoperative patients and those with pain tended to be higher (p < 0.05). While 26 of 51 (51%) patients reported moments of feeling unsafe during the mobility ride, no actual adverse events, such as collisions, were observed. CONCLUSIONS: An in-hospital autonomous transportation service using a driverless mobility for patients with musculoskeletal disorders demonstrated high satisfaction levels and was safe with no severe adverse events observed. The expansion of autonomous mobility deployment is expected to achieve mobility as a service in medical care.

Using System Dynamics Approach to Explore the Mode Shift between Automated Vehicles, Conventional Vehicles, and Public Transport in Melbourne, Australia.

With the increasing use of automated vehicles (AVs) in the coming decades, government authorities and private companies must leverage their potential disruption to benefit society. Few studies have considered the impact of AVs towards mode shift by considering a range of factors at the city level, especially in Australia. To address this knowledge gap, we developed a system dynamic (SD)-based model to explore the mode shift between conventional vehicles (CVs), AVs, and public transport (PT) by systematically considering a range of factors, such as road network, vehicle cost, public transport supply, and congestion level. By using Melbourne's Transport Network as a case study, the model simulates the mode shift among AVs, CVs, and PT modes in the transportation system over 50 years, starting from 2018, with the adoption of AVs beginning in 2025. Inputs such as current traffic, road capacity, public perception, and technological advancement of AVs are used to assess the effects of different policy options on the transport systems. The data source used is from the Victorian Integrated Transport Model (VITM), provided by the Department of Transport and Planning, Melbourne, Australia, data from the existing literature, and authors' assumptions. To our best knowledge, this is the first time using an SD model to investigate the impacts of AVs on mode shift in the Australian context. The findings suggest that AVs will gradually replace CVs as another primary mode of transportation. However, PT will still play a significant role in the transportation system, accounting for 50% of total trips by person after 2058. Cost is the most critical factor affecting AV adoption rates, followed by road network capacity and awareness programs. This study also identifies the need for future research to investigate the induced demand for travel due to the adoption of AVs and the application of equilibrium constraints to the traffic assignment model to increase model accuracy. These findings can be helpful for policymakers and stakeholders to make informed decisions regarding AV adoption policies and strategies.

A Point Cloud Data-Driven Pallet Pose Estimation Method Using an Active Binocular Vision Sensor.

Pallet pose estimation is one of the key technologies for automated fork pickup of driverless industrial trucks. Due to the complex working environment and the enormous amount of data, the existing pose estimation approaches cannot meet the working requirements of intelligent logistics equipment in terms of high accuracy and real time. A point cloud data-driven pallet pose estimation method using an active binocular vision sensor is proposed, which consists of point cloud preprocessing, Adaptive Gaussian Weight-based Fast Point Feature Histogram extraction and point cloud registration. The proposed method overcomes the shortcomings of traditional pose estimation methods, such as poor robustness, time consumption and low accuracy, and realizes the efficient and accurate estimation of pallet pose for driverless industrial trucks. Compared with traditional Fast Point Feature Histogram and Signature of Histogram of Orientation, the experimental results show that the proposed approach is superior to the above two methods, improving the accuracy by over 35% and reducing the feature extraction time by over 30%, thereby verifying the effectiveness and superiority of the proposed method.

Shared control versus traded control in driving: a debate around automation pitfalls.

A major question in human-automation interaction is whether tasks should be traded or shared between human and automation. This work presents reflections-which have evolved through classroom debates between the authors over the past 10 years-on these two forms of human-automation interaction, with a focus on the automated driving domain. As in the lectures, we start with a historically informed survey of six pitfalls of automation: (1) Loss of situation and mode awareness, (2) Deskilling, (3) Unbalanced mental workload, (4) Behavioural adaptation, (5) Misuse, and (6) Disuse. Next, one of the authors explains why he believes that haptic shared control may remedy the pitfalls. Next, another author rebuts these arguments, arguing that traded control is the most promising way to improve road safety. This article ends with a common ground, explaining that shared and traded control outperform each other at medium and low environmental complexity, respectively. Practitioner summary: Designers of automation systems will have to consider whether humans and automation should perform tasks alternately or simultaneously. The present article provides an in-depth reflection on this dilemma, which may prove insightful and help guide design. Abbreviations: ACC: Adaptive Cruise Control: A system that can automatically maintain a safe distance from the vehicle in front; AEB: Advanced Emergency Braking (also known as Autonomous Emergency Braking): A system that automatically brakes to a full stop in an emergency situation; AES: Automated Evasive Steering: A system that automatically steers the car back into safety in an emergency situation; ISA: Intelligent Speed Adaptation: A system that can limit engine power automatically so that the driving speed does not exceed a safe or allowed speed.

Bioprotection of Transportation and Facilities from SARS-CoV-2 (COVID-19).

The recent COVID-19 pandemic has led to a nearly world-wide shelter-in-place strategy. This raises several natural concerns about the safe relaxing of current restrictions. This article focuses on the design and operation of heating ventilation and air conditioning (HVAC) systems in the context of transportation. Do HVAC systems have a role in limiting viral spread? During shelter-in-place, can the HVAC system in a dwelling or a vehicle help limit spread of the virus? After the shelter-in-place strategy ends, can typical workplace and transportation HVAC systems limit spread of the virus? This article directly addresses these and other questions. In addition, it also summarizes simplifying assumptions needed to make meaningful predictions. This article derives new results using transform methods first given in Ginsberg and Bui. These new results describe viral spread through an HVAC system and estimate the aggregate dose of virus inhaled by an uninfected building or vehicle occupant when an infected occupant is present within the same building or vehicle. Central to these results is the derivation of a quantity called the "protection factor"-a term-of-art borrowed from the design of gas masks. Older results that rely on numerical approximations to these differential equations have long been lab validated. This article gives the exact solutions in fixed infrastructure for the first time. These solutions, therefore, retain the same lab validation of the older methods of approximation. Further, these exact solutions yield valuable insights into HVAC systems used in transportation.

Would You Trust Driverless Service? Formation of Pedestrian's Trust and Attitude Using Non-Verbal Social Cues.

Despite the widespread application of Autonomous Vehicles (AV) to various services, there has been relatively little research carried out on pedestrian-AV interaction and trust within the context of service provided by AV. This study explores the communication design strategy promoting a pedestrian's trust and positive attitude to driverless services within the context of pedestrian-AV interaction using non-verbal social cues. An empirical study was conducted with an experimental VR environment to measure participants' intimacy, trust, and brand attitude toward AV. Further understanding of their social interaction experiences was explored through semi-structured interviews. As a result of the study, the interaction effect of social cues was found, and it was revealed that brand attitude was formed by the direct effects of intimacy and trust as well as the indirect effects of intimacy through trust's mediation. Furthermore, 'Conceptual Definition of Space' was identified to generate differences in the interplay among intimacy, trust, and brand attitude according to social cues. Quantitative and qualitative results were synthesized to discuss implications considering the service context. Practical implications were also addressed suggesting specific design strategies for utilizing the sociality of AV.

Understanding Autonomous Shuttle Adoption Intention: Predictive Power of Pre-Trial Perceptions and Attitudes.

The capability of 'demand-responsive transport', particularly in autonomous shared form, to better facilitate road-based mobility is considered a significant advantage because improved mobility leads to enhanced quality of life and wellbeing. A central point in implementing a demand-responsive transit system in a new area is adapting the operational concept to the respective structural and socioeconomic conditions. This requires an extensive analysis of the users' needs. There is presently limited understanding of public perceptions and attitudes toward the adoption of autonomous demand-responsive transport. To address this gap, a theory-based conceptual framework is proposed to provide detailed empirical insights into the public's adoption intention of 'autonomous shuttle buses' as a form of autonomous demand-responsive transport. South East Queensland, Australia, was selected as the testbed. In this case study, relationships between perceptions, attitudes, and usage intention were examined by employing a partial least squares structural equation modeling method. The results support the basic technology acceptance model casual relationships that correspond with previous studies. Although the direct effects of perceived relative advantages and perceived service quality on usage intention are not significant, they could still affect usage intention indirectly through the attitude factor. Conversely, perceived risks are shown to have no association with perceived usefulness but can negatively impact travelers' attitudes and usage intention toward autonomous shuttle buses. The research findings provide implications to assist policymakers, transport planners, and engineers in their policy decisions and system plans as well as achieving higher public acknowledgment and wider uptake of autonomous demand-responsive transport technology solutions.

Collective Reflective Equilibrium in Practice (CREP) and controversial novel technologies.

In this paper, we investigate how data about public preferences may be used to inform policy around the use of controversial novel technologies, using public preferences about autonomous vehicles (AVs) as a case study. We first summarize the recent 'Moral Machine' study, which generated preference data from millions of people regarding how they think AVs should respond to emergency situations. We argue that while such preferences cannot be used to directly inform policy, they should not be disregarded. We defend an approach that we call 'Collective Reflective Equilibrium in Practice' (CREP). In CREP, data on public attitudes function as an input into a deliberative process that looks for coherence between attitudes, behaviours and competing ethical principles. We argue that in cases of reasonable moral disagreement, data on public attitudes should play a much greater role in shaping policies than in areas of ethical consensus. We apply CREP to some of the global preferences about AVs uncovered by the Moral Machines study. We intend this discussion both as a substantive contribution to the debate about the programming of ethical AVs, and as an illustration of how CREP works. We argue that CREP provides a principled way of using some public preferences as an input for policy, while justifiably disregarding others.

The Immoral Machine.

In a recent paper in Nature1 entitled The Moral Machine Experiment, Edmond Awad, et al. make a number of breathtakingly reckless assumptions, both about the decisionmaking capacities of current so-called "autonomous vehicles" and about the nature of morality and the law. Accepting their bizarre premise that the holy grail is to find out how to obtain cognizance of public morality and then program driverless vehicles accordingly, the following are the four steps to the Moral Machinists argument:1)Find out what "public morality" will prefer to see happen.2)On the basis of this discovery, claim both popular acceptance of the preferences and persuade would-be owners and manufacturers that the vehicles are programmed with the best solutions to any survival dilemmas they might face.3)Citizen agreement thus characterized is then presumed to deliver moral license for the chosen preferences.4)This yields "permission" to program vehicles to spare or condemn those outside the vehicles when their deaths will preserve vehicle and occupants.This paper argues that the Moral Machine Experiment fails dramatically on all four counts.

Holding the keys to health? A scoping study of the population health impacts of automated vehicles.

BACKGROUND: Automated Vehicles (AVs) are central to the new mobility paradigm that promises to transform transportation systems and cities across the globe. To date, much of the research on AVs has focused on technological advancements with little emphasis on how this emerging technology will impact population-level health. This scoping study examines the potential health impacts of AVs based on the existing literature. METHODS: Using Arksey and O'Malley's scoping protocol, we searched academic and 'grey' literature to anticipate the effects of AVs on human health. RESULTS: Our search captured 43 information sources that discussed a least one of the five thematic areas related to health. The bulk of the evidence is related to road safety (n = 37), followed by a relatively equal distribution between social equity (n = 24), environment (n = 22), lifestyle (n = 20), and built environment (n = 18) themes. There is general agreement that AVs will improve road safety overall, thus reducing injuries and fatalities from human errors in operating motorized vehicles. However, the relationships with air quality, physical activity, and stress, among other health factors may be more complex. The broader health implications of AVs will be dependent on how the technology is adopted in various transportation systems. Regulatory action will be a significant determinant of how AVs could affect health, as well as how AVs influence social and environmental determinants of health. CONCLUSION: To support researchers and practitioners considering the health implications of AVs, we provide a conceptual map of the direct and indirect linkages between AV use and health outcomes. It is important that stakeholders, including public health agencies work to ensure that population health outcomes and equitable distribution of health impacts are priority considerations as regulators develop their response to AVs. We recommend that public health and transportation officials actively monitor trends in AV introduction and adoption, regulators focus on protecting human health and safety in AV implementation, and researchers work to expand the body of evidence surrounding AVs and population health.

Behavioral Pedestrian Tracking Using a Camera and LiDAR Sensors on a Moving Vehicle.

In this paper, we present a novel 2D⁻3D pedestrian tracker designed for applications in autonomous vehicles. The system operates on a tracking by detection principle and can track multiple pedestrians in complex urban traffic situations. By using a behavioral motion model and a non-parametric distribution as state model, we are able to accurately track unpredictable pedestrian motion in the presence of heavy occlusion. Tracking is performed independently, on the image and ground plane, in global, motion compensated coordinates. We employ Camera and LiDAR data fusion to solve the association problem where the optimal solution is found by matching 2D and 3D detections to tracks using a joint log-likelihood observation model. Each 2D⁻3D particle filter then updates their state from associated observations and a behavioral motion model. Each particle moves independently following the pedestrian motion parameters which we learned offline from an annotated training dataset. Temporal stability of the state variables is achieved by modeling each track as a Markov Decision Process with probabilistic state transition properties. A novel track management system then handles high level actions such as track creation, deletion and interaction. Using a probabilistic track score the track manager can cull false and ambiguous detections while updating tracks with detections from actual pedestrians. Our system is implemented on a GPU and exploits the massively parallelizable nature of particle filters. Due to the Markovian nature of our track representation, the system achieves real-time performance operating with a minimal memory footprint. Exhaustive and independent evaluation of our tracker was performed by the KITTI benchmark server, where it was tested against a wide variety of unknown pedestrian tracking situations. On this realistic benchmark, we outperform all published pedestrian trackers in a multitude of tracking metrics.

Some pitfalls in the promises of automated and autonomous vehicles.

Differing forms of self-operating transportation are already among us and some have been in operation now for an extended period of time. From elevators and escalators to airport transit trams, we already use many fully automatic systems. Now such technologies are very publicly and prominently penetrating into the on-road environment of everyday personal vehicle usage. The present article raises and addresses a number of the specific and more general human factors/ergonomic issues associated with such an evolutionary step. One particular concern is that of identified responsibility when such systems fail to perform flawlessly. The ways in which this (r)evolution will impact the social and cultural fabric of affected societies is also considered. Further observations as to the vector of the future characteristics of these vehicular forms and how they and other autonomous systems will affect our world are examined. The very future of the human experience depends upon the ways in which such systems are designed, enacted and integrated into everyday life and these are fundamentally ergonomic endeavours.Practitioner's Summary: The prominence of practitioners working on advanced human-machine systems will increase with public concerns surrounding self-driving vehicles. Driverless cars are not only a technological step but they will also exert widespread effects throughout society. Practitioners should prepare for these broad socio-technical challenges in an evolving, autonomous world.

An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints.

A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS-inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

A novel fast pedestrian recognition algorithm based on point cloud compression and boundary extraction.

Reason: Pedestrian recognition has great practical value and is a vital step toward applying path planning and intelligent obstacle avoidance in autonomous driving. In recent years, laser radar has played an essential role in pedestrian detection and recognition in unmanned driving. More accurate high spatial dimension and high-resolution data could be obtained by building a three-dimensional point cloud. However, the point cloud data collected by laser radar is often massive and contains a lot of redundancy, which is not conducive to transmission and storage. So, the processing speed grows slow when the original point cloud data is used for recognition. On the other hand, the compression processing of many laser radar point clouds could save computing power and speed up the recognition processing. Methodology: The article utilizes the fusion point cloud data from laser radar to investigate the fast pedestrian recognition algorithm. The focus is to compress the collected point cloud data based on the boundary and feature value extraction and then use the point cloud pedestrian recognition algorithm based on image mapping to detect pedestrians. This article proposes a point cloud data compression method based on feature point extraction and reduced voxel grid. Results: The Karlsruhe Institute of Technology and Toyota Technological Institute data set is used to investigate the proposed algorithm experimentally. The outcomes indicate that the peak signal-to-noise ratio of the compression algorithm is improved by 6.02%. The recognition accuracy is improved by 16.93%, 17.2%, and 16.12%, corresponding to simple, medium, and difficult scenes, respectively, when compared with the point cloud pedestrian recognition method based on image mapping, which uses the random sampling method to compress the point cloud data. Conclusion: The proposed method could achieve data compression better and ensure that many feature points are retained in the compressed Point Cloud Data (PCD). Thus, the compressed PCD achieves pedestrian recognition through an image-based mapping recognition algorithm.

Modelling cybersecurity regulations for automated vehicles.

Technological advancements in Connected and Automated Vehicles (CAVs), particularly the integration of diverse stakeholder groups (communication service providers, road operators, automakers, repairers, CAV consumers, and the general public) and the pursuit of new economic opportunities, have resulted in the emergence of new technical, legal, and social challenges. The most pressing challenge is deterring criminal behaviour in both the physical and cyber realms through the adoption of CAV cybersecurity protocols and regulations. However, the literature lacks a systematic decision tool to analyze the impact of the potential cybersecurity regulations for dynamically interacting stakeholders, and to identify the leverage points to minimise the cyber-risks. To address this knowledge gap, this study uses systems theory to develop a dynamic modelling tool to analyze the indirect consequences of potential CAVs cybersecurity regulations in the medium to long term. It is hypothesized that CAVs Cybersecurity Regulatory Framework (CRF) is the property of the entire ITS stakeholders. The CRF is modelled using the System Dynamic based Stock-and-Flow-Model (SFM) technique. The SFM is founded on five critical pillars: the Cybersecurity Policy Stack, the Hacker's Capability, Logfiles, CAV Adopters, and intelligence-assisted traffic police. It is found that decision-makers should focus on three major leverage points: establishing a CRF grounded on automakers' innovation; sharing risks in eliminating negative externalities associated with underinvestment and knowledge asymmetries in cybersecurity; and capitalising on massive CAV-generated data in CAV operations. The formal integration of intelligence analysts and computer crime investigators to strengthen traffic police capabilities is pivotal. Recommendations for automakers include data-profiteering in CAV design, production, sales, marketing, safety enhancements and enabling consumer data transparency.Furthermore, CAVs-CRF necessitate a balanced approach to the trade-off between: i) data accessibility constraints on CAV automakers and ITS service providers; ii) regulator command and control thresholds; iii) automakers' business investment protection; and iv) consumers' data privacy guard.

The effect of a startle-based warning, age, sex, and secondary task on takeover actions in critical autonomous driving scenarios.

Introduction: In highly autonomous driving scenarios, it is critical to identify strategies to accelerate reaction times since drivers may take too long to take over control of the vehicle. Previous studies reported that an Acoustic Startling Pre-Stimulus (ASPS, i.e., a loud warning preceding an action) accelerated reaction times in simple ankle flexion exercises. Methods: In this study, we examined if an ASPS warning leads to shorter takeover reaction times in a sled-simulated evasive swerving maneuver. Twenty-eight participants (seven male adults, seven male teenagers, seven female adults, and seven female teenagers) were instructed to align a marker on the steering wheel with a marker on a lateral post as fast as they could as soon as the lateral sled perturbation (0.75 g) started. Four conditions were examined: with and without an ASPS (105 dB, played 250 ms before sled perturbation for 40 ms), and with and without a secondary task (i.e., texting). A catch trial (ASPS only) was used to minimize anticipation. Human kinematics were captured with an 8-camera 3D motion capture system. Results: Results showed that the drivers' hands lifted towards the steering wheel more quickly with the ASPS (169 ± 55 ms) than without (194 ± 46 ms; p = 0.01), and that adult drivers touched the steering wheel quicker with the ASPS (435 ± 54 ms) than without (470 ± 33 ms; p = 0.01). Similar findings were not observed for the teen drivers. Additionally, female drivers were found to lift their hands towards the steering wheel faster than male drivers (166 ± 58 ms vs. 199 ± 36 ms; p = 0.009). Discussion: Our findings suggest that the ASPS may be beneficial to accelerate driver reaction times during the initiation of a correction maneuver, and that autonomous vehicle warnings may need to be tailored to the age and sex of the driver.

An integrated route planning approach for driverless vehicle delivery system.

With the rapid growth of express delivery in urban areas, the use of driverless vehicles as an alternative to traditional human delivery can reduce costs and improve efficiency. The route planning of driverless vehicles is crucial in realizing autonomous navigation, which improves the working level and ensures improvements in efficiency. However, it is difficult to reasonably organize the real-time delivery, taking into account several factors that influence the planning of routes, such as load capabilities, power limits and traffic conditions. To deal with this concern, we propose an integrated approach including a multistage model and improved genetic algorithm to obtain the optimal delivery plan for driverless vehicles. The experimental results in an urban scenario with a realistic delivery service show the superiority of our proposition in the delivery efficiency.

A conceptual system dynamics model for cybersecurity assessment of connected and autonomous vehicles.

Emerging Connected and Autonomous Vehicles (CAVs) technology have a ubiquitous communication framework. It poses security challenges in the form of cyber-attacks, prompting rigorous cybersecurity measures. There is a lack of knowledge on the anticipated cause-effect relationships and mechanisms of CAVs cybersecurity and the possible system behaviour, especially the unintended consequences. Therefore, this study aims to develop a conceptual System Dynamics (SD) model to analyse cybersecurity in the complex, uncertain deployment of CAVs. Specifically, the SD model integrates six critical avenues and maps their respective parameters that either trigger or mitigate cyber-attacks in the operation of CAVs using a systematic theoretical approach. These six avenues are: i) CAVs communication framework, ii) secured physical access, iii) human factors, iv) CAVs penetration, v) regulatory laws and policy framework, and iv) trust-across the CAVs-industry and among the public. Based on the conceptual model, various system archetypes are analysed. "Fixes that Fail", in which the upsurge in hacker capability is the unintended natural result of technology maturity, requires continuous efforts to combat it. The primary mitigation steps are human behaviour analysis, knowledge of motivations and characteristics of CAVs cyber-attackers, CAVs users and Original Equipment Manufacturers education. "Shifting the burden", where policymakers counter the perceived cyber threats of hackers by updating legislation that also reduces CAVs adaptation by imitations, indicated the need for calculated regulatory and policy intervention. The "limits to success" triggered by CAVs penetration increase the defended hacks to establish regulatory laws, improve trust, and develop more human analysis. However, it may also open up caveats for cyber-crimes and alert that CAVs deployment to be alignment with the intended goals for enhancing cybersecurity. The proposed model can support decision-making and training and stimulate the roadmap towards an optimized, self-regulating, and resilient cyber-safe CAV system.

Deliberately prejudiced self-driving vehicles elicit the most outrage.

Should self-driving vehicles be prejudiced, e.g., deliberately harm the elderly over young children? When people make such forced-choices on the vehicle's behalf, they exhibit systematic preferences (e.g., favor young children), yet when their options are unconstrained they favor egalitarianism. So, which of these response patterns should guide AV programming and policy? We argue that this debate is missing the public reaction most likely to threaten the industry's life-saving potential: moral outrage. We find that people are more outraged by AVs that kill discriminately than indiscriminately. Crucially, they are even more outraged by an AV that deliberately kills a less preferred group (e.g., an elderly person over a child) than by one that indiscriminately kills a more preferred group (e.g., a child). Thus, at least insofar as the public is concerned, there may be more reason to depict and program AVs as egalitarian.

Naming is framing: The framing effect of technology name on public attitude toward automated vehicles.

Vehicles with automated driving systems are called by many names, which are used interchangeably in public discourse, with different and at times misleading meanings. In two studies (total N = 908), we examined the naming effects on people's cognitive (perceived benefit and risk), affective (negative and positive affect), and behavioral responses (behavioral intention) to and trust in these vehicles in the Chinese context. Study 1 considered four names (intelligent, automated, autonomous, and driverless vehicles). Study 2 presented an identical description of vehicles with full automation and considered their five names (fully intelligent, fully automated, fully autonomous, fully driverless, and driverless vehicles). We corroborated the naming effects on affective responses and trust. The framing of "driverless vehicle" was less favorable in Study 1 but more favorable in Study 2. Technology names indirectly influenced behavioral intention through certain cognitive and affective responses. Theoretical and practical implications of our results are discussed.