Automated Integrative Complexity: Current Challenges and Future Directions

Automating integrative complexity is fraught with many challenges. To address these challenges, we discuss the tension between a specificity approach and a more flexible multiple‐pass approach, the multifaceted nature of the complexity construct, the gold standard for complexity measurement, the difficulty of human scoring and its consequences for automation, and some ways forward for creating the best complexity measurements. In so doing, we present new data demonstrating (1) initial evidence for the validity of a new automated system for measuring two different forms of complexity (elaborative and dialectical), (2) the danger of constructing measurements in a purely ad hoc fashion that ignores prospective testing, (3) human‐to‐computer correspondence is in part a function of human‐to‐human correspondence, (4) human‐to‐computer correspondence increases systematically as one uses tests with larger units of analysis, and (5) the lack of correspondence of different systems (both human and automated) may occur in part because they were designed for different units of analysis.     

Investigation of drivers’ thresholds of a subjectively accepted driving performance with a focus on automated driving

Lately, the development and implementation of automated driving moved to the center of interest in the automotive industry. In this context, one of the central issues – the configuration of adequate trajectories – is mainly tackled using a technical approach. However, it appears that a technically ideal driving performance does not necessarily coincide with the drivers’ subjective preferences. This study strives to determine thresholds of a subjectively accepted driving performance regarding lateral vehicle control. A second objective is to analyze the influence of selected personal and situational factors on these thresholds. An empirical online survey with 161 participants rating video sequences of driving performances was conducted. The video sequences differed not only with regard to the lateral offset of the ego-vehicle but also concerning the weather (sun/rain) and traffic conditions (existence/driving behavior of oncoming traffic). Additionally, the participants’ driving experience and sensation seeking were considered in the data evaluation. To analyze the data, binary logistic regression analyses were calculated. They revealed that the subjective evaluation of driving performances varies primarily depending on the lateral offset of both the ego-vehicle and the oncoming traffic. The results indicate that regarding the lateral offset certain thresholds of subjectively accepted driving performances do exist. Regarding the development of automated driving systems, two issues need to be considered in order to ultimately guarantee user acceptance. First, the subjective thresholds need to be integrated into the systems’ trajectory planning. Second, the oncoming traffic’s driving behavior has to be considered.     

The estimation of vehicle speed and stopping distance by pedestrians crossing streets in a naturalistic traffic environment

The ability to estimate vehicle speed and stopping distance accurately is important for pedestrians to make safe road crossing decisions. In this study, a field experiment in a naturalistic traffic environment was conducted to measure pedestrians’ estimation of vehicle speed and stopping distance when they are crossing streets. Forty-four participants (18–45 years old) reported their estimation on 1043 vehicles, and the corresponding actual vehicle speed and stopping distance were recorded. In the speed estimation task, pedestrians’ performances change in different actual speed levels and different weather conditions. In sunny conditions, pedestrians tended to underestimate actual vehicle speeds that were higher than 40 km/h but were able to accurately estimate speeds that were lower than 40 km/h. In rainy conditions, pedestrians tended to underestimate actual vehicle speeds that were higher than 45 km/h but were able to accurately estimate speeds ranging from 35 km/h to 45 km/h. In stopping distance estimation task, the accurate estimation interval ranged from 60 km/h to 65 km/h, and pedestrians generally underestimated the stopping distance when vehicles were travelling over 65 km/h. The results show that pedestrians have accurate estimation intervals that vary by weather conditions. When the speed of the oncoming vehicle exceeded the upper bound of the accurate interval, pedestrians were more likely to underestimate the vehicle speed, increasing their risk of incorrectly deciding to cross when it is not safe to do so.     

Evaluating Attitudes and Experience With Emerging Technology in Cadets and Civilian Undergraduates

Existing research on the characteristics of digital natives, traditionally defined as those born after 1980, has shown subtle differences in how they approach technology compared with other cohorts. However, much of the existing research has focused on a limited set of conventional technologies, mostly related to learning. In addition, prior research has shown differences within this cohort in how they respond to autonomous technology (e.g., trust, reliance; Pak, Rovira, McLaughlin, &; Baldwin, 2016 Pak, R., Rovira, E., McLaughlin, A., &; Baldwin, N. (2016). Does the domain of technology impact user trust? Investigating trust in automation across different consumer-oriented domains in young adults, military, and older adults. Theoretical Issues in Ergonomics Science, 18, 199–220. 10.1080/1463922x.2016.1175523[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). The purpose of this short report, representing the first wave of data collection in a larger study examining technology experience and attitude change, is to directly address 2 shortcomings in the literature on digital natives which tends to emphasize: (a) civilian students; and (b) conventional, often learning technologies. We addressed these 2 issues by recruiting 2 subgroups of digital natives (students and military cadets) and assessing attitudes and experience with a wide range of technology spanning from conventional (e.g., mobile) to emerging (e.g., robotics). The results showed that that both groups were surprisingly unfamiliar with emerging consumer technologies. Additionally, contrary to expectations, cadets were significantly, albeit only slightly, less experienced with mobile technologies, VR/augmented reality, social media, and entertainment technology as compared to civilian undergraduates.     

Auditory localisation of conventional and electric cars: Laboratory results and implications for cycling safety

When driven at low speeds, cars operating in electric mode have been found to be quieter than conventional cars. As a result, the auditory cues which pedestrians and cyclists use to assess the presence, proximity and location oncoming traffic may be reduced, posing a safety hazard. This laboratory study examined auditory localisation of conventional and electric cars including vehicle motion paths relevant for cycling activity. Participants (N = 65) in three age groups (16–18, 30–40 and 65–70 year old) indicated the location and movement direction (approaching versus receding) of cars driven at 15, 30 and 50 km/h in two ambient sound conditions (low and moderate). Results show that low speeds, higher ambient sound level and older age were associated with worse performance on the location and motion direction tasks. In addition, participants were less accurate at determining the location of electric and conventional car sounds emanating from directly behind the participant. Implications for cycling safety and proposals for adding extra artificial noise or warning sounds to quiet (electric) cars are discussed.     

一种对动物学习记忆能力突变筛查的新途

本文详细描述了一种全自动化的行为检测方案。在自然饲养环境/检测环境(24/7)中, 我们测量了小鼠针对两个给食器中获得食物的比例与它们在相应给食器停留时长的比例进行匹配的精确性与准确性。该方案是对传统条件性习得 (trials-to-acquisition) 行为测验设备的改进, 可以检测动物时间间隔能力的精确性与准确性, 对定时目标选择的相关概率的效果, 以及记忆一天中从不同给食器中获得食物次数的精确性与准确性。该压缩系统避免了在整个实验过程中对小鼠的持握操作, 可忽略实验者/技术员的实验操作时间, 而且可以递送小鼠置入实验环境后, 7~9个实验日中全部3组实验流程产生的大量结果。其中, 第一个实验流程为单个24小时周期内完成的时间匹配能力的筛查, 它对动物的时间、空间估计能力的记忆机制进行精确检测。因此, 该系统允许在有限的实验空间、较短的实验周期内, 对大量的实验小鼠进行有可能存在的学习记忆能力缺陷进行大规模筛查。此外, 该系统运行所依赖的软件可以在公共开放平台获得。     

Feedback and driver situation awareness (SA): A comparison of SA measures and contexts

The dominant technological trajectory in vehicle design brings with it similarly dominant driver performance issues in regard to vehicle feedback and driver situational awareness (SA). Three experiments are reported in this paper that describe not only the effects on driver SA of manipulations of vehicle feedback but also illuminate issues concerned with SA measurement methods and contexts. The findings suggest that current trends in vehicle design may contribute little towards a driver’s SA and, in fact, may actually show a generalized trend towards decreasing it. The efficacy of verbal protocol and probe recall SA measurement techniques is noted in terms of observing this effect. On the other hand, a concerning dissociation occurred with findings from a self-report measure of SA. Drivers appear to show a concerning lack of self-awareness of their SA and, indeed, any shortfall in it.     

Assessing the driving distraction effect of vehicle HMI displays using data mining techniques

With the rapid development of human–machine interface (HMI) systems in vehicles, driving distraction caused by HMI displays affects road safety. This study presents a data mining technique to model the four driving distraction indicators: speed deviation, lane departure standard deviation, dwell time, and mean glance time. Driving distraction data was collected on a real-car driving simulator. 3 secondary tasks in 13 mass produced cars were tested by 24 drivers. The random forest algorithm outperformed linear regression, extreme gradient boosting, and multi-layer perceptron as the best model, demonstrating good regression performance as well as good interpretability. The result of random forest showed that the importance of target speed is large for all driving distraction indicators. Among the variables of interaction and user interface design, less step and less on-screen distance of finger movement are efficient for lowering lane departure standard deviation and dwell time. The position of right point is another important variable, and should be between 37 and 47 degrees on a typical sample in this study. A larger angle leads to bigger lane departure, while a smaller angle leads to bigger mean glance time. Most variables of HMI display positioning themselves are not important. This study provides one driving distraction assessment method with a variable impact trend analysis for HMI secondary tasks in an early phase of product development.     

Using a multi-user driving simulator system to explore the patterns of vehicle fleet rear-end collisions occurrence under different foggy conditions and speed limits

Vehicle fleet rear-end collisions (FRECs) are an extremely fatal type of traffic collisions on freeway and they usually occur in foggy weather. This study aimed to explore the patterns of vehicle fleet rear-end collisions occurrence under different foggy conditions and speed limits on freeway. A multi-user driving simulator system was used to conduct the experiment and the driving behavior data were collected from eight participants. The experimental results showed that as the fog density increased, the length of vehicle fleet decreased significantly, and drivers tended to keep a more stable car-following distance. The fog weather and short vehicle gap prompted drivers to react faster and brake harder in respond to the leading vehicle’s brake. In spite of the compensational behaviors, more FRECs were observed under heavy fog condition. Lowering speed limit can significantly reduce the FRECs under foggy conditions. As the speed limits reduced, drivers’ brake response time and speed variance significantly reduced. The study also found that drivers’ brake response time was negatively correlated with their positions in the fleet. Drivers in the front positions of the fleet had a longer response time than drivers in the back positions and thus were more likely to encounter collisions. The study generated a better understanding of drivers’ behavioral pattern in a vehicle fleet and the patterns of vehicle fleet rear-end collisions occurrence. The findings also shed lights on the design of driver assistance system for complex driving situations such as freeway driving under adverse weather.     

Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence

Adaptive cruise control (ACC), a driver assistance system that controls longitudinal motion, has been introduced in consumer cars in 1995. A next milestone is highly automated driving (HAD), a system that automates both longitudinal and lateral motion. We investigated the effects of ACC and HAD on drivers’ workload and situation awareness through a meta-analysis and narrative review of simulator and on-road studies. Based on a total of 32 studies, the unweighted mean self-reported workload was 43.5% for manual driving, 38.6% for ACC driving, and 22.7% for HAD (0% = minimum, 100 = maximum on the NASA Task Load Index or Rating Scale Mental Effort). Based on 12 studies, the number of tasks completed on an in-vehicle display relative to manual driving (100%) was 112% for ACC and 261% for HAD. Drivers of a highly automated car, and to a lesser extent ACC drivers, are likely to pick up tasks that are unrelated to driving. Both ACC and HAD can result in improved situation awareness compared to manual driving if drivers are motivated or instructed to detect objects in the environment. However, if drivers are engaged in non-driving tasks, situation awareness deteriorates for ACC and HAD compared to manual driving. The results of this review are consistent with the hypothesis that, from a Human Factors perspective, HAD is markedly different from ACC driving, because the driver of a highly automated car has the possibility, for better or worse, to divert attention to secondary tasks, whereas an ACC driver still has to attend to the roadway.