Humanizing autonomous vehicle driving: Understanding,modeling and impact assessment

The advent of autonomous vehicles brings major changes in the transportation systems influencing the infrastructure design, the network performance, as well as driving functions and habits. The penetration rate of this new technology highly depends on the acceptance of the automated driving services and functions, as well as on their impacts on various traffic, user oriented and environmental aspects. This research aims to present a methodological framework aiming to facilitate the modelling of the behaviour of new AV driving systems and their impacts on traffic, safety and environment. This framework introduces a stepwise approach, which will be leveraged by stakeholders in order to evaluate the new technology and its components at the design or implementation phase in order to increase acceptance and favor the adoption of the new technology. The proposed framework consists of four sequential steps: i. conceptual design, ii. data collection, processing and mining, iii. modelling and iv. autonomous vehicles impact assessment. The connection between these steps is illustrated and various Key Performance Indicators are specified for each impact area. The paper ends with highlighting some conceptual and modeling challenges that may critically affect the study of acceptance of autonomous vehicles in future mobility scenarios.     

Sharing the road with autonomous vehicles: A qualitative analysis of the perceptions of pedestrians and bicyclists

Public perception assessment is important for gaining a better understanding of the acceptance of autonomous vehicles (AVs) and identifying potential ways to resolve public concerns. This study investigated how pedestrians and bicyclists perceived AVs based on their knowledge and road sharing experiences, applying a combined inductive and deductive data analysis approach. Survey responses of pedestrians and bicyclists in Pittsburgh, Pennsylvania, USA collected by Bike Pittsburgh (BikePGH) in 2019, were analyzed in this research. AVs following traffic rules appropriately and AVs driving safer than the human drivers were the most notable positive perceptions towards AVs. Pedestrians and bicyclists showed comparatively fewer negative perceptions towards AVs than positive perceptions. Negative perceptions mostly included a lack of perceived safety and comfort around AVs and trust in the AV technology. Respondents also concerned about AV technology issues (e.g., slow and defensive driving, disruptive maneuver), while sharing the road with AVs. Perceptions of the respondents were significantly influenced by their views on AV safety, familiarity with the technology, the extent respondents followed AV on the news, and household automobile ownership. Regulating AV movement on roadways, developing safety assessment guidelines, and controlling oversights of improper practices by AV companies were the major suggestions from the survey participants. Findings of this study might help AV companies to identify potential improvement needed in AV technology to increase pedestrians and bicyclists acceptance, and policymakers to develop policy guidelines to ensure safe road sharing among pedestrians, bicyclists, and AVs.     

Different level automation technology acceptance: Older adult driver opinion

The increase in the number of older adult drivers in developed countries has raised safety concerns due to the decline in their sensory, motor, perceptual, and cognitive abilities which can limit their driving capabilities. Their driving safety could be enhanced by the use of modern Automated Driver Assistance Systems (ADASs) and might totally resolved by full driving automation. However, the acceptance of these technologies by older adult drivers is not yet well understood. Thus, this study investigated older adult drivers’ intention to use six ADASs and full driving automation through two questionnaires with 115 and 132 participants respectively in Rhode Island, USA. A four-dimensional model referred to as the USEA model was used for exploring older adult drivers’ technology acceptance. The USEA model included perceived usefulness, perceived safety, perceived ease of use, and perceived anxiety. Path Analysis was applied to evaluate the proposed model. The results of this study identified the important factors in older adult drivers’ intention to use ADASs and full driving automation, which could assist stakeholders in improving technologies for use by older drivers.     

This is not me! Technology-identity concerns in consumers’ acceptance of autonomous vehicle technology

The relationship between drivers and their cars is increasingly personal, where the cars become an extension of the drivers’ self-identity. However, the penetration of autonomous vehicle (AV) technology threatens consumers’ self-identity as expressed by the act of driving. This study thus aims to examine the impacts of technology-identity concerns on consumers’ acceptance of AV technology. Theories of identity threat, identity control and innovation diffusion are synthesised to build the conceptual framework. Face-to-face interview data were collected from 353 consumers (all with a driving license) in Singapore. The results show that consumers’ technology anxiety and self-identity expressiveness act as two sources of resistance that cause consumers’ intentional avoidance of the AV technology. The avoidance is further characterised by consumers’ disengagement from the ‘observe’ and ‘try’ stages of technology penetration, which ultimately dissuades consumers’ acceptance of AV technology. The impacts of socio-demographics are also explored. Our findings contribute to multiple streams of literature and create practical implications to AV manufacturers and retailers.     

A data–information–knowledge cycle for modeling driving behavior

When talking about automation, “autonomous vehicles”, often abbreviated as AVs, come to mind. In transitioning from the “driver” mode to the different automation levels, there is an inevitable need for modeling driving behavior. This often happens through data collection from experiments and studies, but also information extraction, a key step in behavioral modeling. Particularly, naturalistic driving studies and field operational trials are used to collect meaningful data on drivers’ interactions in real–world conditions. On the other hand, information extraction methods allow to predict or mimic driving behavior, by using a set of statistical learning methods. In simple words, the way to understand drivers’ needs and wants in the era of automation can be represented in a data–information cycle, starting from data collection, and ending with information extraction. To develop this cycle, this research reviews studies with keywords “data collection”, “information extraction”, “AVs”, while keeping the focus on driving behavior. The resulting review led to a screening of about 161 papers, out of which about 30 were selected for a detailed analysis. The analysis included an investigation of the methods and equipment used for data collection, the features collected, the size and frequency of the data along with the main problems associated with the different sensory equipment; the studies also looked at the models used to extract information, including various statistical techniques used in AV studies. This paved the way to the development of a framework for data analytics and fusion, allowing the use of highly heterogeneous data to reach the defined objectives; for this paper, the example of impacts of AVs on a network level and AV acceptance is given. The authors suggest that such a framework could be extended and transferred across the various transportation sectors.     

What are the factors determining user intentions to use AV while impaired?

In this study, we employ the Integrated Choice Latent Variable (ICLV) framework to model the public’s intention of using Autonomous Vehicles (AVs) while impaired under the influence of alcohol, medicine, or fatigue. We identify five latent constructs from psychometric indicators that define respondent’s perception and attitudes towards AVs which are i) perceived benefits, ii) perceived risks, iii) enjoy driving, iv) wheels public transport attitude, and v) rails public transport attitude. We use these latent variables along with explanatory variables to study user intentions regarding delegation of vehicle control from human driving to autonomous driving. The study uses survey data collected from 1,065 Czech residents between 2017 and 2018. Our findings indicate that user intentions are primarily defined by attitudes rather than socio-demographic attributes. However, the inclusion of both types of variables is crucial in evaluating user intentions. Despite a positive outlook towards AVs, people were found to be reluctant in using AVs while impaired which can be attributed to distrust towards the technology. Our analysis shows that with appropriate efforts from policymakers, the public’s attitude can be changed to promote adoption. The efforts will have to be emphasized towards building positive attitudes (such as perceived benefits) and diminishing existing negative attitudes (such as perceived risks).     

“What Makes a Cooperative Driver?” Identifying parameters of implicit and explicit forms of communication in a lane change scenario

An automated vehicle needs to learn how human road users communicate with each other in order to avoid misunderstandings and prevent giving a negative outward image during interactions. The aim of the present work is to develop an autonomous driving system which communicates its intentions to change lanes based on implicit and explicit rules used by human drivers. To reach this goal, we aimed at gaining a deeper understanding of which aspects of lane change behaviour makes them cooperative from the perspective of other drivers. Therefore a vehicle used various lane change announcement strategies by varying combinations of driving parameters in a static driving simulator. (First study: Start indicator signal, Waittime, lane change duration; Second study: Longitudinal acceleration). It’s impact on the perception and behaviour of other road users was observed in two studies (N = 25 per study). The results showed that the earlier the merging vehicle was indicating its intentions, the more cooperative it was perceived. When turning on the indicator at a later time participants considered it as more cooperative to merge with a slower or faster lane change duration or to wait longer in the lane before starting to move to the other lane. An early longitudinal acceleration when starting to change lanes is perceived more cooperative. These findings can be used to model a lane change strategy based on human behaviour, which will eventually lead to more acceptable and safer interactions between automated and non-automated road users.     

From autonomy to heteronomy (and back): The enaction of social life

The term “social cognition” can be construed in different ways. On the one hand, it can refer to the cognitive faculties involved in social activities, defined simply as situations where two or more individuals interact. On this view, social systems would consist of interactions between autonomous individuals; these interactions form higher-level autonomous domains not reducible to individual actions. A contrasting, alternative view is based on a much stronger theoretical definition of a truly social domain, which is always defined by a set of structural norms; moreover, these social structures are not only a set of constraints, but actually constitute the possibility of enacting worlds that would just not exist without them. This view emphasises the heteronomy of individuals who abide by norms that are impersonal, culturally inherited and to a large extent independent of the individuals. Human beings are socialised through and through; consequently, all human cognition is social cognition. The article argues for this second position. Finally, it appears that fully blown autonomy actually requires heteronomy. It is the acceptance of the constraints of social structures that enables individuals to enter new realms of common meaningfulness. The emergence of social life marks a crucial step in the evolution of cognition; so that at some evolutionary point human cognition cannot but be social cognition.     

Analyse critique des approches de l’acceptation des technologies : de l’utilisabilité à la symbiose humain-technologie-organisation

This article proposes a criticism of the concept of technology acceptance. Starting from a distinction between operational and social acceptance, the authors present an overview of the concepts and notions associated to the paradigm of technology acceptance. Then, they criticize the paradigm and propose the idea that human and technologies are connected by interactions that are so narrow that one can refer to human-machine symbiosis, as Licklider (1960) suggested. The first part of the paper is devoted to the various ergonomic aspects of the operational acceptance (usability, ergonomic criteria, model of the interaction) while the second part addresses the psychosocial aspects of acceptance (Technology Acceptance Model, User Information Satisfaction Theory, Expectancy Disconfirmation Theory, Agile Organizational Development…). The idea behind the authors’ point of view is that technology must not inevitably be thought of as a foreign thing that the human must accept or refuse. From a metaphorical point of view, technology is more and more part of ourselves and behaves like a symbiont. From the time human and technologies start to form coherent and linked units, the relation between human, technology and organization can be apprehended according to a symbiotic approach which considers that human and technologies are connected by strong dependent relationships. In short, this article proposes to consider that the concept of symbiosis is more capable to explain the relationships of human with technology than the acceptance concept.     

Can we adapt to highly automated vehicles as passengers? The mediating effect of trust and situational awareness on role adaption moderated by automated driving style

The emergence of highly automated driving technology provides safe and convenient travel while also causing user inadaptation. Therefore, based on human factors engineering, it is necessary to study highly automated vehicles (HAVs) that meet different user needs. Thus, this study aims to investigate the relationships between state anxiety, situational awareness, trust, and role adaptation. The adaptation model is constructed to conduct a study on the adaptation of HAVs with different automated styles when user roles change from driver to passenger. Simulated riding was conducted in the HAV experiment (N = 117), collecting scale data after each participant had experienced each automated driving style. A structural equation modeling approach was applied to analyze the adaptation model based on scale data. The results showed that there was a significant correlation between state anxiety, situational awareness, trust, and role adaptation. State anxiety has a significant negative predictive effect on trust, situational awareness, and role adaptation. In addition to its direct impact on role adaptation, state anxiety also has an indirect effect on role adaptation through situational awareness and trust. Furthermore, the automated driving style has been confirmed to have a moderating role in the relationship between the direct and indirect effects of state anxiety and role adaptation. Our findings contribute to multiple streams of the literature and have important implications for designing personalized automated driving to improve user acceptance.     

Effects of explanation types and perceived risk on trust in autonomous vehicles

Despite technological advances, trust still remains as a major issue facing autonomous vehicles. Existing studies have reported that explanations of the status of automation systems can be an effective strategy to increase trust, but these effects can differ depending on the forms of explanations and autonomous driving situations. To address this issue, this study examines the effects of explanation types and perceived risk on trust in autonomous vehicles. Three types of explanations (i.e., no, simple, and attributional explanations) are designed based on attribution theory. Additionally, four autonomous driving situations with different levels of risk are designed based on a simulator program. Results show that explanation type significantly affects trust in autonomous vehicles, and the perceived risk of driving situations significantly moderates the effect of the explanation type. At a high level of perceived risk, attributional explanations and no explanations lead to the lowest and highest values in trust, respectively. However, at a low level of perceived risk, these effects reverse.     

Can driving condition prompt systems improve passenger comfort of intelligent vehicles? A driving simulator study

The main trend in the development of intelligent vehicles has been on ensuring comfort, safety, efficiency, and environmental sustainability. However, current research focuses primarily on the safety and energy saving of intelligent vehicles, and a comfortable driving experience through a human–machine interaction system has not been sufficiently investigated. This study used a high-fidelity 6-degree-of-freedom driving simulator to evaluate the impact of an independently-designed vehicle driving condition prompt (DCP) systems on subjective passenger comfort and motion sickness. The experiment showed that when future driving information is obtained through the vehicle DCP systems, the passengers' subjective comfort is improved, motion sickness is alleviated, and the degree of passenger posture instability is reduced. These conclusions contribute toward improving the comfort of autonomous vehicles and providing a reference for the future design of human–machine interaction systems for intelligent vehicles.     

User acceptance of automated public transport: Valence of an autonomous minibus experience

Autonomous driving is receiving increasing attention in the automotive industry as well as in public transport. However, it is still unclear whether users are willing to use automated public transportation at all. In order to answer this and other questions, the transport company of the city of Mainz, Germany, tested the autonomous minibus EMMA (Elektro-Mobilität Mainz Autonom) on a 600-meter-long test track in public space. The study presented here was conducted with the aim of exploring crucial determinants for the use of an autonomous minibus. On the basis of established acceptance models, a questionnaire was developed, which was completed in a field survey by a total of 942 participants before or after their journey with the minibus. Autonomous vehicles in public transport in general and the minibus in particular were evaluated positively by the majority of respondents. Above all, participants judged safety and environmental friendliness of the minibus as important. Participants who completed the questionnaire after their first trip with EMMA provided higher ratings of acceptance than those who had not travelled on the bus. Performance expectancy was the most important predictor for both acceptance of automated public transport in general and acceptance of the minibus EMMA. However, the experienced valence of the ride, in terms of how pleasant or unpleasant passengers experienced the first trip with the minibus, also affected acceptance of the minibus. This suggests a role of valence on intention-to-use, which has hardly been considered in previous theories and studies.     

The impact of E-bus satisfaction on driving behaviour: A questionnaire-based study on E-bus drivers

The aggressive promotion of electric buses (E-buses) is receiving increasing attention as an important measure to reduce carbon emissions in the transport sector. Electric buses (E-buses) still have some defects, which may reduce driver satisfaction and affect a drivers’ driving behaviour (such as frequent attention to the instrument panel given a lack of energy and overspeeding given the time pressure caused by a lack of energy). In this study, the influencing factors of drivers’ overall satisfaction (perceived usefulness, perceived ease of use, environmental performance, technology acceptance and line environment) were investigated using the bus driver satisfaction questionnaire. The overall satisfaction level was measured directly by the overall satisfaction scale. Aberrant and positive driving behaviours were observed by the professional driver driving behaviour questionnaire. Taking overall satisfaction as the intermediary variable and driving behaviour (aberrant and positive driving behaviours) as the dependent variable, we established the hypothetical relationships among overall satisfaction, driving behaviour, perceived usefulness, perceived ease of use, environmental performance, technology acceptance, and line environment and constructed an E-bus driver driving behaviour structural equation model (SEM). Through an online questionnaire survey, the survey data of 313 E-bus drivers were obtained and verified for analysis. The results show that perceived usefulness, perceived ease of use, environmental performance and technology acceptance have a positive impact on overall satisfaction, and environmental performance has the greatest impact on overall satisfaction; overall satisfaction has a negative impact on aberrant driving behaviour and a positive impact on positive driving behaviour; and the line environment has a positive impact on aberrant driving behaviour. From the driver's point of view, this study plays a positive role in a more comprehensive understanding of the advantages and disadvantages, and the factors restricting the application, of E-buses. Meanwhile, it provides a strong basis for government departments, industry management departments and bus companies to formulate policies (such as E-bus driver training and management strategies).     

Empirical evidence of bias in public acceptance of autonomous vehicles

This study synthesizes 91 peer-reviewed survey studies examining the public acceptance of Autonomous Vehicles (AVs). The framework of the study is informed by three questions: (1) How well do the collected samples represent the acceptance of the general population? (2) How often does bias exist in measuring public acceptance in AV’s questionnaires? (3) How much bias persists in reporting public acceptance of AV’s research? The findings indicate that (1) people with disabilities and racial minorities are only included in 10% and 20% of the studies, respectively (2) 50% of the studies present their questionnaire, and most are perceived to be biased as a result of systematic errors such as leading questions, missing questions, and suggestive information, and (3) 72% of the studies suffer from the sentiment bias, where the positive tone in the title and abstract is more significant than in the result. This leads to imprecise findings and unrealistic depictions of acceptance of autonomous vehicles by the public. The analysis alerts researchers and practitioners to empirical evidence of bias in public acceptance of autonomous vehicles and recommends preventive actions.     

After you?! – Use of external human-machine interfaces in road bottleneck scenarios

In the near future, automated vehicles (AVs) will enter the urban transport system. This fact will lead to mixed traffic consisting of AVs, human car drivers and vulnerable road users. Since the AV’s passenger no longer has to monitor the driving scene, conventional communication does not exist anymore, which is essential for traffic efficiency and safety. In research, there are plenty of studies focusing on how AVs could communicate with pedestrians. One approach is to use external human-machine interfaces (eHMIs) on the AV’s surface. In contrast to the studies dealing with AV-pedestrian communication, this paper focuses on communication strategies of AVs with drivers of regular vehicles in different road bottleneck scenarios. The eHMI development and design is building on previously defined requirements and on fundamentals of human visual perception. After designing several eHMI drafts, we conducted a user survey with 29 participants resulting in the final eHMI concept. The evaluation of the evolved eHMI was conducted in a driving simulator experiment with 43 participants investigating the AV-human driver interaction at road bottlenecks. The participants were assigned either to the experimental group being faced with the eHMI or to the baseline group without explicit communication. The results show significantly shorter passing times and fewer crashes among the human drivers in the group with the eHMI. Additionally, the paper researches the aftereffects of an automation failure, where the AV first yields the right of way and then changes its strategy and insisted on priority. Experiencing the automation failure is reflected in increased passing times, reduced acceptance ratings and a lower perceived usefulness. In conclusion, especially in unregulated bottleneck scenarios flawless communication via eHMIs increases traffic efficiency and safety.     

Valid representation of a highly dynamic collision avoidance scenario in a driving simulator

In Germany the second-most frequent accidents in road traffic are rear-end collisions. For this reason rear-end collisions are quite important for accident research and the development of driving safety systems. To examine the functionality and to design the human–machine-interface of new driving safety systems, especially in the early development phase, subject tests are necessary. Because of the great hazard potential of such safety critical scenarios for the test persons, they are often conducted in a driving simulator (DS). Accordingly, validity is an important qualification to ensure that the findings collected in a simulated test environment can be directly transferred to the real world.This paper regards the question of driving behavior validity of DS in critical situations. There are hardly any validation studies which analyze the driving behavior in a specific collision avoidance situation.The validation study described in this paper aims to evaluate the behavioral validity of a fixed-base simulator in a collision avoidance situation. For this reason a field study from 2007 was replicated in a fixed-base simulator environment.The main questions of this validation study were if the driver can notice an active hazard braking system and if the driving behavior in a static simulator can be valid in such a critical situation.The key finding of the study states that there is no driving behavior validity in a static driving simulator for the tested dynamic scenario. The missing vestibular feedback causes a different behavior of the participants in field and simulator. The resulting absence of comparability leads to non-valid performance indicators. But these indicators are key parameters for analyzing the function and acceptance of active braking systems. So the question arises, which motion performance does a motion base have to provide in order to achieve valid acceleration simulation of such a highly dynamic collision avoidance scenario. The DS’s performance is measured in workspace, velocity and acceleration.     

Modelling the acceptance of fully autonomous vehicles: A media-based perception and adoption model

As the technology matures, fully autonomous vehicles (AVs) are on the corner. This calls for exploring the factors that might influence potential users’ perception and acceptance of AVs. Limited existing studies related to acceptance modeling investigated the effects of media and human on fully AVs’ beliefs. Hence, a media-based perception and adoption model (MPAM) is developed to investigate how information and opinion (from mass media and social media) affect human self-perception (including self-efficacy and subjective norms) and product value perception (including perceived usefulness and risks), which in turn drive users’ adoption intention to private AVs and public AVs as well. Through a questionnaire survey, 355 samples from two universities were collected in Beijing. The structural equation model results confirm that media channels have salient effects on consumer and product with different emphases. Mass media enhances potential users’ self-efficacy of fully AVs, while social media strengthens subjective norms. Both usefulness and risks of AVs are perceived simultaneously via mass media, whereas risks perception can be significantly eliminated by social media. All constructs of user’s self-perception and product perception are verified to drive users’ intention to using AVs and public AVs. Besides the theoretical and modeling contributions, practical implications are provided for the marketers and stakeholders in the early stages of AVs launch.     

Public acceptance of semi-automated truck platoon driving. A comparison between Germany and California

Platooning technology aims at achieving fuel savings by reducing the distance between two or more electronically coupled vehicles. This technology has recently been tested on public highways with heavy trucks in Germany and California. The objective of this study is to assess the level of acceptance among other road users as well as influencing factors of acceptance. An online questionnaire was administered in Germany and California with a total of N = 536 participants. They received information about truck platoon driving (level-1 and level-2 automation) and answered questions about their attitudes towards the technology as well as their behavioral intention to cooperate with the truck platoons. The overall results showed that 70% of respondents indicated acceptance towards the technology, with acceptance rates being significantly higher in California than in Germany. German respondents were more willing to drive into the gap of platoon vehicles and preferred larger platooning gaps. An adaption of the Technology Acceptance Model (TAM) showed that the expected usefulness, and the expected ease of sharing the highway, were the strongest predictors for the behavioral intention to cooperate with platoon vehicles. However, the intention to cut in between platoon vehicles could not be predicted by these variables. Cut-in vehicles are a potential safety risk and decrease the efficiency of platoon driving. Therefore, future research should focus on finding behavioral countermeasures.     

驾驶决策的研究现状与展望

驾驶决策是驾驶员对不同交通场景进行判断、选择,并产生驾驶行为的过程。驾驶决策的研究起源于人因工程学,并随着认知心理学的发展而逐渐受到关注。驾驶决策的研究范式主要采用心理测量与实验法;并与年龄、性别、风险感知、情绪以及决策风格相关;为了有效防止交通事故的发生,驾驶决策的改善逐步成为研究的新热点。今后的研究可以从认知与情绪角度综合探讨适合中国国情的驾驶决策的结构与特征,以提高驾驶员的决策能力。