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.     

Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle

Autonomous vehicles (AVs) can potentially compensate for human error whilst driving. Thus, the number of accidents and the amount of congestion, emissions and fuel consumption could be reduced. For such reduction to occur, insights into public acceptance of AVs must be considered because they are a crucial factor for the successful implementation of AVs in existing traffic systems.PurposeThis study aims to explore (1) the mobility behaviour of current passengers, (2) the perception of the public of how AVs can be applied, (3) the perceived challenges of AVs in terms of integration into existing traffic systems, (4) the general attitude of the public towards AVs and (5) the perceived safety of passengers after riding an autonomous shuttle with a maximum SAE level of 3 in the setting of mixed traffic.MethodThis qualitative study was conducted in Carinthia (Austria) in September 2018. Interviews were conducted with 19 participants aged between 20 and 75 years. The participants were recruited through purposeful sampling, and data were collected through in-depth, semistructured interviews which were recorded, transcribed, organised and analysed using systematic, qualitative content analysis.ResultsResults show that transport mobility plays an important role in social life. In rural areas, AVs can shift transportation modes from private cars to public transportation. The respondents perceive AVs as an alternative more than as a substitute for existing means of transportation. With the integration of AVs into existing traffic systems, the transportation of goods and people could be improved despite related uncertainties. This study explores different aspects of societal, technical, legal and economic challenges. Overall, the respondents feel positive about AVs. Responses regarding safety show that experience with AVs and speed are key factors.Originality/ValueThis study is one of the first to explain qualitatively public perceptions of AVs after an actual ride in an AV under real-life conditions. The results, which help understand AVs from the public’s perspective, may also provide guidelines for the successful integration of AVs in Austria.     

Neuroevolution-based autonomous robot navigation: A comparative study

The field of neuroevolution has achieved much attention in recent years from both academia and industry. Numerous papers have reported its successful applications in different fields ranging from medical domain to autonomous systems. However, it is not clear which evolutionary optimization techniques lead to the best results. In this paper, multilayer perceptron (MLP) neural networks (NNs) are trained and optimized using four advanced bio-inspired evolutionary algorithms (EA). The algorithms are Multi-Verse Optimizer (MVO), Moth-flame optimization (MFO), Cuckoo Search (CS) and Particle Swarm Optimization (PSO). Each algorithm is equipped with two operators: evolutionary population dynamics and mutation, which impact on exploration and exploitation. Optimized MLPs are then used for the navigation of an autonomous robot. Accuracy and area under the curve metrics are used for the evaluation and comparison metrics. Moreover, two well-regarded gradient descent algorithms including Back propagation (BP) and Levenberg Marquardt (LM) are utilized to validate the results obtained by evolutionary-based MLP trainers. It is observed that MLPs developed using MFO are the most robust ones among MLPs trained using other evolutionary and gradient descent algorithms.     

External HMI for self-driving vehicles: Which information shall be displayed?

The objective of this study was to investigate pedestrians’ informational needs towards self-driving vehicles (SDVs). Previous research has shown that external human-machine interfaces (eHMIs) compensate for pedestrian-driver communication when SDVs are integrated into traffic. However, detailed insights on which information the eHMI shall provide lack so far. In a mixed design study, N = 59 participants encountered a simulated driverless vehicle in different traffic scenarios (a. unsignalized intersection vs. b. parking lot; between-subject factor). We investigated the effect of no eHMI (baseline) vs. eHMIs displaying the automated driving system (ADS) status, and informing subsequently about its perception of the pedestrian and/or its intent for the next maneuver ((1) no eHMI, (2) status eHMI, (3) status + perception eHMI, (4) status + intent eHMI, (5) status + perception + intent eHMI; within-subject factor). A mixed-methods design was used to explore participants’ subjective feelings, traffic behavior, and underlying attitudes. The findings reveal that any eHMI contributes to a more positive feeling towards SDVs compared to the baseline condition without eHMI, consistent among traffic scenarios: participants felt significantly safer, reflected greater trust and user experience ratings, and perceived the SDV as more intelligent and transparent. The status indicator mainly drives these beneficial effects on subjective measures. Participants reported that the status information explains the absence of a driver steering the vehicle. Compared to the status eHMI, the status + perception eHMI reflects no further benefit regarding subjective feelings and even has a negative impact on traffic flow. Moreover, participants regarded the additional information on the vehicle’s perception as an obvious gimmick. On the contrary, the status + intent eHMI increases user experience, perceived intelligence, and transparency for pedestrians more than the mere status eHMI. Participants reported that additionally informing about the vehicle’s intent adds a further sense of safety. The present study failed to show any improvements in traffic flow but found evidence for individual crossing and clearing strategies among pedestrians. This work can inform the future design of eHMIs.     

Does students’ self-determined motivation toward Physical Education influence the effectiveness of a fitness teaching unit? A cluster-randomized controlled trial and cluster analysis

ObjectiveThe aim was to compare the effect of a Physical Education (PE)-based fitness teaching unit to improve cardiorespiratory fitness levels between students’ motivational profiles toward PE in high-school students using cluster analysis.MethodOne hundred and eighty-one high school students (final sample 165, 52.1% females; M_age = 12.9 ± 0.9) from nine pre-established classes were cluster-randomly assigned into an experimental group (n = 120) and control group (n = 61). The experimental group performed a fitness teaching unit twice a week for nine weeks. Students’ baseline of self-determined motivation toward PE was measured by the Spanish version of the Perceived Locus of Causality-II Scale. Cardiorespiratory fitness was measured before and after the intervention through the 20-m shuttle run test.ResultsBased on a two-stage cluster analysis approach, four clusters were identified in both groups. Results showed that the experimental group students within the high self-determined motivational profiles toward PE (i.e., clusters 2 and 4) statistically significantly improved their cardiorespiratory fitness levels compared with the control group students in cluster 2 (i.e., those with moderate self-determined motivational profile toward PE) (p < 0.05; d = 0.37–0.48). However, statistically significant differences between the students with a moderate autonomous motivation profile toward PE (i.e., cluster 3 of the experimental group) and the control group students in clusters 2 and 3 were not found (p > 0.05). Conclusions: With the objective of increasing all students’ cardiorespiratory fitness levels, PE teachers should encourage students’ motivation toward PE applying specific motivational strategies during PE lessons.     

Individually constructed criteria for perception of urban transportation means – An approach based on Kelly’s personal construct theory

Understanding of acceptance of electric mobility has been typically discussed by a comparison of vehicles with different types of propulsion engines, battery electric vehicles and vehicles with an internal combustion engine. Nevertheless, electric mobility comprehends a combination of public transport and electric vehicles. The aim of this paper is to understand peoples’ outlook on electric mobility by identifying shared aspects of the assessment of battery electric vehicles and different user perspectives on transportation. A special research design in the form of repertory grids provides an opportunity to study the underlying causes of the cognitive perceptions and emotions relating to electric mobility. Cognitive interviews motivate respondents to reflect beyond the insights provided by standard forms of interview. Especially for the topic of battery electric vehicles, prejudices - for instance, those propagated by the media - are discarded and the actual requirements and patterns of mobility become visible. The special tasks involved in the interviews lead, for example, to deliberation on how to integrate battery charging processes into existing mobility patterns.This special method reveals that individuals take an interest in more characteristics of modes of transport than those that are usually analysed when researching electric mobility. In addition, three anticipation clusters can be identified for individuals with a higher affinity for cars. First, the perception of battery electric vehicles shows high levels of similarity to cars with internal combustion engines and that differentiating between types of engines is meaningless. Second, battery electric vehicles are perceived as a part of urban public transport. Third, battery electric vehicles are viewed as similar to pedelecs and segways, whereas questions of range, innovation and environmental aspects play a greater role in perceptions. These results lead to the conclusion that when studying the acceptance of BEVs, a comparison between cars with internal combustion engines and battery electric vehicles is not sufficient to grasp the complete user perspective. An analysis within the framework of a wider range of modes of transport is required in order to address people’s transportation needs.     

Technology enabled work: The role of self-efficacy in determining telecommuter adjustment and structuring behavior

We explore factors associated with employees’ ability to cope with the challenges of telecommuting—an increasingly pervasive new work mode enabled by advances in information technologies. Telecommuting can trigger important changes in employees’ job responsibilities, especially with respect to the degree of proactivity required to effectively work from a distance. Survey responses from a sample of 723 participants in one organization’s formal telecommuting program were used to examine the inter-relationships between telecommuter self-efficacy and extent of telecommuting on telecommuters’ ability to cope with this new work context. Results indicate that there is a positive association between telecommuter self-efficacy and both employees’ behavioral strategies (i.e., structuring behaviors) and work outcomes (i.e., telecommuter adjustment). Moreover, these positive relationships are accentuated for employees who telecommute more extensively. Implications for research and practice concerning the effect of technology on jobs and careers are presented.     

Research on intelligent cognitive function enhancement of intelligent robot based on ant colony algorithm

In this paper, ant colony algorithm is studied to improve the visual cognitive function of intelligent robots. Based on the detailed understanding of the research status in this field at home and abroad, and learning from cognitive science and neurobiology research results, a solution is proposed from the perspective of ant colony algorithm based on human brain structure and function. By simulating the process of autonomous learning controlled by human long-term memory and its working memory, a visual strangeness-driven growth long-term memory autonomous learning algorithm is proposed. This method takes incremental self-organizing network as long-term memory structure, and combines with visual strangeness internal motivation Q learning method in working memory. The visual knowledge acquired by self-learning is accumulated into long-term memory continuously, thus realizing the ability of self-learning, memory and intelligence development similar to human beings. The experimental results show that the robot can learn visual knowledge independently, store and update knowledge incrementally, and improve its intelligence development, classification and recognition ability compared with the method without long-term memory. At the same time, the generalization ability and knowledge expansion ability are also improved.     

The role of self-concordance for self-reported strategic,tactical, and operational eco-driving

Although most people are aware of the harmful CO₂ emissions produced by the transport sector threatening life on earth now and in the future, they do not eco-drive. Eco-driving improves the vehicle’s fuel or energy economy and reduces greenhouse gas emissions. We investigated the motivational predictors of eco-driving based on the theory of self-concordance (i.e., the consistency between a behavior/goal with the person’s pre-existing values and interests). Data from a cross-sectional online survey with 536 German drivers revealed that self-reported eco-driving was significantly predicted by sustained effort towards eco-driving, which in turn was predicted by self-concordance variables. Therefore, individuals pursuing eco-driving out of strong interest or deep personal beliefs (i.e., autonomous motivation) as opposed to external forces or internal pressures (i.e., controlled motivation) reported greater effort towards this behavior. Furthermore, biospheric striving coherence, i.e., the coherence between personal valuable biopsheric values (i.e., values addressing the well-being of the environment/biosphere) and eco-driving, significantly predicted effort towards eco-driving. In sum, our results suggest that autonomous rather than controlled motives and coherence between behavior and intrinsic rather than extrinsic values are relevant predictors for eco-driving. We discuss implications for future strategies and interventions fostering eco-driving in the long term.