Using the UTAUT2 model to explain public acceptance of conditionally automated (L3) cars: A questionnaire study among 9,118 car drivers from eight European countries

We investigated public acceptance of conditionally automated (SAE Level 3) passenger cars using a questionnaire study among 9,118 car-drivers in eight European countries, as part of the European L3Pilot project. 71.06% of respondents considered conditionally automated cars easy to use while 28.03% of respondents planned to buy a conditionally automated car once it is available. 41.85% of respondents would like to use the time in the conditionally automated car for secondary activities. Among these 41.85%, respondents plan to talk to fellow travellers (44.76%), surf the internet, watch videos or TV shows (44%), observe the landscape (41.70%), and work (17.06%). The UTAUT2 (Unified Theory of Acceptance and Use of Technology) was applied to investigate the effects of performance and effort expectancy, social influence, facilitating conditions, and hedonic motivation on the behavioural intention to use conditionally automated cars. Structural equation analysis revealed that the UTAUT2 can be applied to conditional automation, with hedonic motivation, social influence, and performance expectancy influencing the behavioural intention to buy and use a conditionally automated car. The present study also found positive effects of facilitating conditions on effort expectancy and hedonic motivation. Social influence was a positive predictor of hedonic motivation, facilitating conditions, and performance expectancy. Age, gender and experience with advanced driver assistance systems had significant, yet small (<0.10), effects on behavioural intention. The implications of these results on the policy and best practices to enable large-scale implementation of conditionally automated cars on public roads are discussed.     

Satisfiability Testing for Boolean Formulas Using Δ-trees

The tree-based data structure of -tree for propositional formulas is introduced in an improved and optimised form. The -trees allow a compact representation for negation normal forms as well as for a number of reduction strategies in order to consider only those occurrences of literals which are relevant for the satisfiability of the input formula. These reduction strategies are divided into two subsets (meaning- and satisfiability-preserving transformations) and can be used to decrease the size of a negation normal form A at (at most) quadratic cost. The reduction strategies are aimed at decreasing the number of required branchings and, therefore, these strategies allow to limit the size of the search space for the SAT problem.     

Two-step communication for the interaction between automated vehicles and pedestrians

An important challenge of automated vehicles (AV) will be the cooperative interaction with surrounding road users such as pedestrians. One solution to compensate for the missing driver-pedestrian interaction might be explicit communication via external human machine interfaces (eHMIs). Additionally, implicit communication such as a vehicle pitch motion might support AVs when interacting with pedestrians. While previous work explored various explicit and implicit communication cues, these concepts communicated the intention of the AV at one single time point. Currently, empirical findings on two-step communication lack so far. In this study, we empirically test the effect of a two-step AV communication that uses an implicit cue at a long distance and subsequently provides an implicit or explicit cues at a short distance. We compared its efficiency to single-step communication concepts providing implicit or explicit cues at the shorter distance only. To explore whether the right communication cue is used at the right distance, we analyzed pedestrians’ fixations while approaching an AV using an eye tracking device.We conducted a virtual reality study (N = 30) with AV communication concept that provided an active pitch motion or an eHMI and compared them with a two-step AV communication concept that provided an additional active pitch motion at a long distance when approaching the pedestrian. Furthermore, we recorded pedestrians’ fixation behavior while the AV approached.Consistently to previous work, single-step AV communication showed a beneficial effect on crossing behavior. Pedestrians initiated their crossing earlier while approaching an AV with an active pitch motion or an eHMI compared to the baseline condition. While active pitch motion reduced subjective safety feeling, eHMI increased it. However, the two-step communication concept did not further improve pedestrians’ crossing initiation times and their safety feeling. The pattern of fixation behavior differed as a function of AV distance. When the approaching AV was far away, pedestrians exclusively looked at the environment. During the approach, pedestrians gradually fixated the bumper and the hood and only then the windshield of the AV. Hence, it seems to be useful to present an AV intent communication at a certain distance from the pedestrian. These findings posit the importance of considering pedestrians’ fixation behavior when developing communication concepts between AVs and pedestrians.     

Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation (SAE Level 3) or monitored the drive (SAE Level 2). Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ∼ 18 min each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. Results showed that driver workload due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that a lead vehicle maintain a shorter THW can significantly increase driver workload during takeover scenarios, potentially affecting driver safety. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional cognitive or attentional demands on the driver. Our results indicated that ECG and EDA signals are sensitive to variations in workload, which warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help future driver monitoring systems respond appropriately to the limitations of the driver, and predict their performance in the driving task, if and when they have to resume manual control of the vehicle after a period of automated driving.     

How to identify the take-over criticality in conditionally automated driving? An examination using drivers’ physiological parameters and situational factors

Autonomous vehicles and advanced driver assistance technology are growing exponentially, and vehicles equipped with conditional automation, which has features like Traffic Jam Pilot and Highway Assist, are already available in the market. And this could expose the driver to a stressful driving condition during the takeover mission. To identify stressful takeover situations and better interact with automated systems, the relationship and effect between drivers’ physiological responses, situational factors (e.g., takeover request [TOR] lead time, takeover frequencies, and scenario types), and takeover criticality were investigated.34 participants were involved in a series of takeover events in a simulated driving environment, which are varied by different TOR lead time conditions and driving scenes. The situational factors, drivers’ skin conductance (SC), heart rate (HR), gaze behaviors, and takeover criticality ratings were collected and analyzed. The results indicated that drivers had a higher takeover criticality rating when they experienced a shorter TOR lead time level or at first to fourth take-overs. Besides, drivers who encountered a dynamic obstacle reported higher takeover criticality ratings when they were at the same Time to collision (TTC). We also observed that the takeover situations of higher criticality have larger driver’s maximum HR, mean pupil size, and maximum change in the SC (relative to the initial value of a takeover stage). Those findings of situational factors and physiological responses can provide additional support for the designing of adaptive alert systems and environmental soothing technology in conditionally automated driving, which will improve the takeover performances and drivers’ experience.     

The Influence of Rater Effects in Training Sets on the Psychometric Quality of Automated Scoring for Writing Assessments

Automated essay scoring engines (AESEs) are becoming increasingly popular as an efficient method for performance assessments in writing, including many language assessments that are used worldwide. Before they can be used operationally, AESEs must be “trained” using machine-learning techniques that incorporate human ratings. However, the quality of the human ratings used to train the AESEs is rarely examined. As a result, the impact of various rater effects (e.g., severity and centrality) on the quality of AESE-assigned scores is not known. In this study, we use data from a large-scale rater-mediated writing assessment to examine the impact of rater effects on the quality of AESE-assigned scores. Overall, the results suggest that if rater effects are present in the ratings used to train an AESE, the AESE scores may replicate these effects. Implications are discussed in terms of research and practice related to automated scoring.     

Response acquisition by zebrafish (Danio rerio) with delayed reinforcement

Zebrafish (Danio rerio) is a common vertebrate animal model in biomedical research and is a promising species for studying how genes interact with environmental factors in determining behavior. The present study investigated how reinforcement parameters affect zebrafish behavior by assessing response acquisition with delayed reinforcement, which has been studied with other species (e.g., rats, pigeons, humans, etc.) but not with zebrafish. Twenty‐four experimentally naïve subjects were exposed to a tandem fixed‐ratio 1 differential‐reinforcement‐of‐other‐behavior x‐s schedule of reinforcement, where x varied across subjects. There were six different delay‐to‐reinforcement durations and sets of four fish were assigned to each delay duration. All of the fish assigned to a 0‐, 0.5‐, or 1‐s delay acquired responding. Two fish acquired responding with a 3‐s delay and one fish appeared to have acquired it with a 6‐s delay although the latter result was less clear. None acquired responding with a 12‐s delay. These results suggest that zebrafish behavior is sensitive to delays to reinforcement and the time frame over which reinforcement is effective may be limited approximately to 6 s. This time frame is shorter than that found with other species. Practical and theoretical implications of the present finding are discussed.     

基于认知机制的有条件自动驾驶接管中的人因问题

自动驾驶能在很大程度上缓解现代交通问题并提升驾驶舒适度。有条件自动驾驶下, 驾驶员可执行非驾驶相关任务但需要在系统无法处理的状况下接管车辆。在这一关键过程中, 驾驶员需要进行注意转换并获得情境意识以成功接管。已有研究表明, 接管请求、非驾驶相关任务、驾驶情景及驾驶员因素是影响接管过程重要因素。未来可从认知机制角度研究各因素对接管过程产生的影响, 以及探究接管过程中各因素之间可能存在的交互作用。