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。     

The influence of age on the take-over of vehicle control in highly automated driving

The growing proportion of older drivers in the population plays an increasingly relevant role in road traffic that is currently awaiting the introduction of automated vehicles. In this study, it was investigated how older drivers (⩾60 years) compared to younger drivers (⩽28 years) perform in a critical traffic event when driving highly automated. Conditions of the take-over situation were manipulated by adding a verbal non-driving task (20 questions task) and by variation of traffic density. Two age groups consisting of 36 younger and 36 older drivers drove either with or without a non-driving task on a six-lane highway. They encountered three situations with either no, medium or high traffic density where they had to regain vehicle control and evade an obstacle on the road. Older drivers reacted as fast as younger drivers, however, they differed in their modus operandi as they braked more often and more strongly and maintained a higher time-to-collision (TTC). Deterioration of take-over time and quality caused by increased traffic density and engagement in a non-driving task was on the same level for both age groups. Independent of the traffic density, there was a learning effect for both younger and older drivers in a way that the take-over time decreased, minimum TTC increased and maximum lateral acceleration decreased between the first and the last situation of the experiment. Results highlight that older drivers are able to solve critical traffic events as well as younger drivers, yet their modus operandi differs. Nevertheless, both age groups adapt to the experience of take-over situations in the same way.     

Anticipatory and control processes in the interaction with secondary tasks while driving

The paper describes an experiment where anticipatory processes in the interaction with secondary tasks while driving could be explicitly identified and contrasted to control processes during the engagement in the secondary task. A special experimental set-up in a driving simulator environment was created that allows drivers to deliberately decide whether they want to be distracted or not depending on the driving situation and the expected development of that situation. As indicators for a situation-adaptive interaction with secondary tasks parameters from driving behaviour, secondary task performance and visual behaviour were analyzed. A study with 24 test drivers revealed that drivers are, in general, able to interact with a secondary task in a situation-aware manner. For example, drivers rejected more secondary tasks in already highly demanding situations or tried to delay the beginning of the task. During secondary task performance drivers observed the situational development with short control glances back to the road and adapted their speed. The analysis of driving errors revealed that rejecting a task in an already highly demanding driving situation is an effective strategy to maintain an adequate level of driving safety. However, some critical factors were identified that might hinder the driver from executing such strategies. Several recommendations for supporting the driver on this issue are given.     

Measuring drivers’ takeover performance in varying levels of automation: Considering the influence of cognitive secondary task

Mixed control by driver and automated system will remain in use for decades until fully automated driving is perfected. Thus, drivers must be able to accurately regain control of vehicles in a timely manner when the automated system sends a takeover request (TOR) at its limitation. Therefore, determining the factors that affect drivers’ takeover quality at varying levels of automated driving is important. Previous studies have shown that visually distracting secondary tasks impair drivers’ takeover performance and increase the subjective workload. However, the influence of purely cognitive distracting secondary tasks on drivers’ takeover performance and how this influence varies at different levels of automation are still unknown. Hence, a 5 (driving modes) × 3 (cognitive secondary tasks) factorial design with the within-subject factors was adopted for this driving simulator experiment. The sample consisted of 21 participants. The participants’ subjective workloads were recorded by the NASA-Task Load Index (NASA-TLX). Results showed that compared to manual driving conditions, the drivers’ subjective workloads were significantly reduced in both partially and highly automated driving conditions, even with a TOR, confirming the benefit of the automated driving system in terms of reducing the driving workload. Moreover, the drivers exhibited a lower takeover behavior quality at high levels of automation than manual driving in terms of increased reaction time, abnormal performance, standard deviation of lane position, lane departure probability, and reduced minimum of time to collision. However, at the highly automated driving condition, the drivers’ longitudinal driving safety and ability to follow instructions improved when performing a highly cognitive secondary task. This phenomenon possibly occurred because automated driving conditions lead to an underload phenomenon, and the execution of highly cognitive tasks transfers drivers into moderate load, which helps with the drivers’ takeover performance.     

Even experienced phone users drive worse while texting – A driving simulator study

Several studies have clearly shown that texting on a mobile phone increases crash risk (e.g. Dingus et al., 2006; Victor et al., 2014). However, the frequency of texting while driving still remains high (e.g. Vollrath, Huemer, Teller & Likhacheva, 2016). One reason may be that drivers are convinced that they are able to manage this dual task due to their competency in driving and texting. From a theoretical point of view, this may be true within limits – two well-learned, automatically processed tasks may require so few resources most of the time that interferences may not occur.In order to test this assumption, a study with a standardized driving simulator task (the lane change task, LCT; Mattes, 2003) was done with n = 40 drivers selected for their driving experience and tested for their texting abilities. The effect of driving experience (high vs. low) and texting competence (high vs. low) on driving performance was examined in single (driving only) and dual-task (driving and texting) conditions. Additionally, a subjective assessment of one’s task performance was obtained. Texting on the mobile phone significantly impaired driving performance. While driving experience did not have an influence, the deteriorating effect of texting was significantly less pronounced in highly competent phone users. Interestingly, this objective effect was not found in the subjective assessment. Drivers with a high texting competence felt as impaired as drivers with a low texting competence. This is in line with the finding that even in this simple driving task their performance was still significantly deteriorated as compared to driving, only. Thus, it seems that the reason why people text while driving is not that they are not aware of the performance loss. However, this awareness of the possible risk does not seem sufficient to prevent them from texting while driving.     

Effects of different non-driving-related-task display modes on drivers’ eye-movement patterns during take-over in an automated vehicle

Highly automated vehicles relieve drivers from driving tasks, allowing them to engage in non-driving-related-tasks (NDRTs). However, drivers are required to take over control in certain circumstances due to the limitations of highly automated vehicles. This study focused on drivers’ eye-movement patterns during take-overs when an NDRT (watching videos) was presented via a head-up-display (HUD) and a mobile device display (MDD), compared to no NDRT as the baseline. The experiment was conducted in a high-fidelity driving simulator with real-world driving videos scenarios. Forty-six participants took part in the experiment by completing three drives in three counterbalanced conditions (HUD, MDD and baseline). A take-over-request was issued towards the end of automated driving requesting drivers to stop the NDRT and take over control. Eye-movement data including pupil diameter, blinks, glance duration and number of AOI (Area of Interest) were collected and analysed. The results show that during automated driving, drivers were more engaged in the MDD NDRT with smaller pupil diameter and shorter glance duration on the front scenario compared to the HUD and baseline modes. The number of AOI was reduced during automated driving in both MDD and HUD modes. The take-over-request redirected drivers’ visual attention back to the driving task from NDRT by increasing drivers’ pupil diameter, glance duration and number of AOI. However, the effect of MDD NDRT on pupil diameter and glance duration continued even after the take-over-request when the NDRT was terminated. The study demonstrated HUD is a better display to help maintain drivers’ attention on the road.     

The impact of auditory continual feedback on take-overs in Level 3 automated vehicles

ObjectiveTo implement auditory continual feedback into the interface design of a Level 3 automated vehicle and to test whether gaze behavior and reaction times of drivers improved in take-over situations.BackgroundWhen required to assume manual control in take-over situations, drivers of Level 3 automated vehicles are less likely than conventional drivers to spot potential hazards, and their reaction time is longer. Therefore, it is crucial that the interface of Level 3 automated vehicles will be designed to improve drivers’ performance in take-over situations.MethodIn two experiments, participants drove a simulated route in a Level 3 automated vehicle for 35 min with one imminent take-over event. Participants’ gaze behavior and performance in an imminent take-over event were monitored under one of three auditory interface designs: (1) Continual feedback. A system that provides verbal driving-related feedback; (2) Persistent feedback. A system that provides verbal driving-related feedback and a persistent beep; and (3) Chatter feedback. A system that provides verbal non-driving-related feedback. Also, there was a control group without feedback.ResultsUnder all three auditory feedback designs, the number of drivers' on-road glances increased compared to no feedback, but none of the designs shortened reaction time to the imminent event.ConclusionIncreasing the number of on-road glances during automated driving does not necessarily improve drivers’ attention to the road and their reaction times during take-overs.ApplicationPossible implications for the effectiveness of auditory continual feedback should be considered when designing interfaces for Level 3 automated vehicles.     

Dual-task decrements in driving performance: The impact of task type,working memory,and the frequency of task performance

Driving while carrying out another (secondary) task interferes with performance, though the degree of interference may vary between tasks and individual drivers. In this study, we focused on two potentially interrelated individual difference variables that may play a role in determining dual-task interference: working memory capacity and the driver’s experience with the relevant secondary task. We used a driving simulator to measure interference, comparing single-task performance (driving alone) with driving performance during three secondary tasks: conversing on a handsfree cellphone, texting, and selecting a song on a touchscreen Mp3 player. Drivers also rated the difficulty of driving while carrying out each secondary task. For the individual difference variables, working memory was measured using the Operation Span test (OSPAN), and experience was assessed in terms of self-reported daily driving exposure and exposure to the relevant secondary tasks (frequency, duration). Overall, we found evidence of dual-task interference, though interference varied between tasks; the texting and Mp3 tasks produced significantly more interference than handsfree cellphone conversation. For the texting and Mp3 song selection tasks, interference was apparent in terms of increased steering variability, but for the Mp3 task there was also compensatory slowing, with drivers slowing down while carrying out the task. OSPAN performance and daily driving exposure were both covariates in predicting the amount of dual-task interference. However, our results suggest that in all but two cases, both involving the texting task, the effects of the OSPAN and the driving and secondary task exposure variables were independent rather than interrelated.     

Effects of secondary tasks and display position on glance behavior during partially automated driving

The driving task is becoming increasingly automated, thus changing the driver’s role. Moreover, in-vehicle information systems using different display positions and information processing channels might encourage secondary task engagement. During manual driving scenarios, varying secondary tasks and display positions could influence driver’s glance behavior. However, their impact on the driver’s capability to monitor the partially automated driving system has not yet been determined. The current study assessed both the effects of different secondary tasks (Surrogate Reference Task (SuRT) vs. text reading) and display positions (head-up display (HUD) vs. center console) on driver’s glance behavior during partially automated driving in a simulated car following task. Different automation system failures regarding the lateral and longitudinal control occurred while driving. Furthermore, participants’ reported advantages, disadvantages and preferences regarding the investigated display positions as well as regarding the secondary task engagement during partially automated driving in general. Mixed design ANOVAs revealed that the HUD yielded considerably longer eyes-on display time (total and mean glance durations) than the center console. Moreover, the text reading task resulted in longer total and mean glance durations than the SuRT. Similar to manual driving scenarios, the results showed a consistent effect of display position and secondary task on the driver’s glance behavior. Despite the longer eyes-on display time for the HUD, its proximity to the driving environment might enable a faster identification of and reaction to critical situations (e.g., due to system failures). Participants would prefer the HUD as display position compared to the center console. Regarding secondary task engagement during partially automated driving participants seemed to be aware of the benefits but also of the risks.     

Attention during visual secondary tasks in driving: Adaptation to the demands of the driving task

Visual attention in driving with visual secondary task is compared for two visual secondary tasks. N = 40 subjects completed a 1 h test drive in a motion-base driving simulator. During the drive, participants either solved an externally paced, highly demanding visual task or a self paced menu system task. The secondary tasks were offered in defined critical and non-critical driving situations. Eye movement behavior was analyzed and compared for both tasks. Before starting the secondary tasks, eye movement behavior shows a smaller standard deviation of gaze as well as longer fixation durations for both tasks. The comparison between the two tasks indicates that drivers use the possibilities the self paced task offers: during the secondary task, they monitor the driving scene with longer fixations and show a greater standard deviation of gaze position. Furthermore, independently of the type of secondary task, drivers adapt their eye movement behavior to the demands of the situation. In critical driving situations they direct a larger proportion of glance time to the driving task. Last, the relation between glance behavior and collisions is analyzed. Results indicate that collisions go together with an inadequate distribution of attention during distraction. The results are interpreted regarding the attentional processes involved in driving with visual secondary tasks. Based on the similarities and differences between the two secondary tasks, a cognitive approach is developed which assumes that the control of attention during distraction is based on a mental situational model of the driving situation.     

Effects of explanation-based knowledge regarding system functions and driver’s roles on driver takeover during conditionally automated driving: A test track study

The present study was designed to examine the influence of explanation-based knowledge regarding system functions and the driver’s role in conditionally automated driving (Level 3, as defined in SAE J3016). In particular, we studied how safely and successfully drivers assume control of the vehicle when encountering situations that exceed the automation parameters. This examination was conducted through a test-track experiment. Thirty-two younger drivers (mean age = 37.3 years) and 24 older drivers (mean age = 71.1 years) participated in Experiments 1 and 2, respectively. Adopting a between-participants design, in each experiment the participants were divided into two age- and sex-matched groups that were given differing levels of explanation-based knowledge concerning the system limitations of automated driving. The only information given to the less-informed groups was that, during automated driving, drivers may be required to occasionally assume control of the vehicle. The well-informed groups were given the same information, as well as details regarding the auditory-visual alerts produced by the human–machine interface (HMI) during requests to intervene (RtIs), and examples of situations where RtIs would be issued. Ten and nine RtI events were staged for each participant in Experiment 1 and 2, respectively; the participants performed a non-driving-related task while the automated driving system was functioning. For both experiments it was found that, for all RtI events, more participants in the well-informed groups than the less-informed groups successfully assumed control of the vehicle. These results suggest that, in addition to providing information regarding the possible occurrence of RtIs, explanations of HMI and RtI-related situations are effective for helping both younger and older drivers safely and successfully negotiate such events.     

Effect of electronic device use while driving on cardiovascular reactivity

Motor vehicle collisions are the leading cause of death in people ages 5–34 in the US, and secondary task engagement, such as talking on a cell phone, is a leading contributor to motor vehicle collisions. The negative effects of secondary task engagement on driving performance has become a prominent recent topic of study given the increasing amount of time drivers engage in distracted driving. However, few studies have examined the effects of secondary task engagement while driving on health related outcomes such as cardiovascular reactivity. Cardiovascular reactivity, as measured by heart rate and blood pressure, has been used in previous studies as a means of measuring effort in task engagement as well as a means to predict cardiovascular disease and stroke. This study investigates the effect of secondary task (talking on a cell phone, texting, and driving with no task) while driving in a simulator on cardiovascular reactivity. Using difference scores between baseline (a period of inactivity) and stimulus (driving with no task and driving with secondary tasks), a repeated measures analysis of variance using a mixed model approach was used to determine the effect of secondary task on cardiovascular reactivity. Findings indicated that talking on a cell phone while driving significantly increased cardiovascular reactivity via heart rate and blood pressure compared to driving with no task. Texting while driving did not differ significantly from driving with no task. This study demonstrates the need for more research on the long term effects of secondary tasks while driving on cardiovascular reactivity and for assessing the risks associated with secondary task use while driving on developing cardiovascular disease or stroke.     

Controllability of Partially Automated Driving functions – Does it matter whether drivers are allowed to take their hands off the steering wheel?

Different motor vehicle manufacturers have recently introduced assistance systems that are capable of both longitudinal and lateral vehicle control, while the driver still has to be able to take over the vehicle control at all times (so-called Partial Automation). While these systems usually allow hands-free driving only for short time periods (e.g., 10 s), there has been little research whether allowing longer time periods of hands-off driving actually has a negative impact on driving safety in situations that the automation cannot handle alone. Altogether, two partially automated assistance systems, differing in the permitted hands-off intervals (Hands-off system vs. Hands-on system, n = 20 participants per assistance condition, age 25–70 years) were implemented in the driving simulation with a realistic take-over concept. The Hands-off system is defined by having a permitted hands-off interval of 120 s, while the Hands-on system is defined by a permitted hands-off interval of 10 s. Drivers’ reactions at a functional system limit were tested under conditions of high ecological validity: while driving in a traffic jam, participants unexpectedly encountered a time-critical situation, consisting of a vehicle at standstill that appeared suddenly and required immediate action. A visual-auditory take-over request was issued to the drivers. Regardless of the hands-off interval, all participants brought the vehicle to a safe stop. In spite of a stronger brake reaction with the Hands-on system, no significant differences between assistance levels were found in brake reaction times and the criticality of the situation. The reason for this may be that most of the drivers kept contact with the steering wheel, even in the Hands-off condition. Neither age nor prior experience with ACC was found to impact the results. The study thus demonstrates that permitting longer periods of hands-off driving does not necessarily lead to performance deficits of the driver in the case of take-over situations, if a comprehensive take-over concept is implemented.     

Using an infotainment system while driving – A continuous analysis of behavior adaptations

Despite the fact that drivers are performing a lot of distracting tasks while driving (e.g. usage of infotainment systems) they are usually able to manage difficult situations. Drivers often seem to be able to adapt and effectively regulate their behavior according to the demands of the driving situation. Not much is known about the functional behavior that allows drivers to successfully regulate their intentional demands. The current study aims to investigate these adaptations and provides a methodological approach to do so. 38 participants performed a simulated driving task while using an In-Vehicle Infotainment System. Driving data and activity data for the secondary task were recorded and analyzed continuously over time. Participants permanently adapted their driving behavior and particularly reduced their secondary task activity when approaching critical driving situations. Following that, drivers should be regarded as active managers of their workload capacities, who actively frame a driving situation and adjust their operating behavior to the environment. To measure these adjustments, a continuous analysis of both driving as well as secondary task behavior is essential.     

Modeling cognitive load effects of conversation between a passenger and driver

Cognitive load from secondary tasks is a source of distraction causing injuries and fatalities on the roadway. The Detection Response Task (DRT) is an international standard for assessing cognitive load on drivers’ attention that can be performed as a secondary task with little to no measurable effect on the primary driving task. We investigated whether decrements in DRT performance were related to the rate of information processing, levels of response caution, or the non-decision processing of drivers. We had pairs of participants take part in the DRT while performing a simulated driving task, manipulated cognitive load via the conversation between driver and passenger, and observed associated slowing in DRT response time. Fits of the single-bound diffusion model indicated that slowing was mediated by an increase in response caution. We propose the novel hypothesis that, rather than the DRT’s sensitivity to cognitive load being a direct result of a loss of information processing capacity to other tasks, it is an indirect result of a general tendency to be more cautious when making responses in more demanding situations.     

Urgent and non-urgent takeovers during conditional automated driving on public roads: The impact of different training programmes

Supplying training to drivers that teaches them about automated driving and requests to intervene may help them to build and maintain a mental representation of how automation works and thereby improve takeover performance. We aimed to investigate the effect of different types of training programmes about the functioning of automated driving on drivers’ takeover performance during real driving. Fifty-two participants were split into three groups for training sessions: paper (short notice), video (3-minute tutorial) and practice (short drive). After the training, participants experienced automated driving and both urgent and non-urgent requests to intervene in a Wizard-of-Oz vehicle on public roads. Participants’ takeover time, visual behaviour, mental workload, and flow levels during the requests to intervene were assessed. Our results indicated that in urgent circumstances, participants’ takeover response times were faster in the practice training condition compared to the other training conditions. Nevertheless, the practice training session did not present any other positive effect on drivers’ visual behaviour. This could indicate that prior training, particularly when reinforcing drivers' motor skills, improved their takeover response time at the latest motor stages rather than in the early sensory states. In addition, the analysis of in-vehicle videos revealed that participants’ attention was captured in the first place by the in-vehicle human-machine interface during the urgent request to intervene. This highlights the importance for designers to display information on the HMI in an appropriate way to optimise drivers’ situation awareness in critical situations.     

Distraction while driving: The case of older drivers

As the impairment of older drivers is especially found in perception and attention, one could assume that they are especially prone to distraction effects of secondary tasks performed while driving. The aim of the study was to examine the effect of age on driving performance as well as the compensation strategies of older drivers under distraction. 10 middle-aged and 10 older drivers drove in a simulator with and without a secondary task. To assess driving performance the Lane Change Task (Mattes, 2003) was used. This method aims at estimating driver demand while a secondary task is being performed, by measuring performance degradation on a primary driving-like task in a standardized manner. The secondary task – a self-developed computer-based version of “d2 Test of Attention” was presented both with and without time pressure. The results show that older participants’ overall driving performance (mean deviation from an ideal path) was worse in all conditions as compared to the younger ones. With regard to lane change reaction time both age groups were influenced by distraction in a comparable manner. However, when the lane keeping performance (standard deviation of the lateral position) was examined, the older participants were more affected than the younger ones. This pattern could be explained by compensation strategies of the older drivers. They focused on the most relevant part of the driving task, the lane change manoeuvres and were able to maintain their performance level in a similar way as did younger drivers. The driving performance of the older participants was not additionally impaired when the secondary task imposed time pressure. Overall, subjective rating of driving performance, perceived workload and perceived distraction was found to be similar for both age groups. The observed trends and patterns associated with distraction while driving should contribute to the further research or practical work regarding in-vehicle technologies and older drivers.     

Driving distraction at night: The impact of cell phone use on driving behaviors among young drivers

Currently, young drivers are more likely than other drivers to use cell phones while driving at night, which has become a major cause of road crashes. However, limited attention has been given to distracted nighttime driving. Therefore, the aim of this study was to explore the interaction effect of cell phone use and time of day (daytime and nighttime) on young drivers’ car-following performance. Forty-three young drivers engaged in a driving simulator experiment with a within-subject design that included three distractions (no distraction, talking and texting on a cell phone) and two times of day. This paper applied non-parametric tests to analyze the data and obtained the following results: (1) the standard deviation of lane position (SDLP) did not significantly differ at either time of day under no distraction, but it was significantly higher at night on straight roads and large-radius curves after introducing distractions. In addition, participants drove faster and gave less headway on small-radius curves under both distractions at night; (2) texting significantly increased the SDLP, while there was less lateral variation during the talking tasks than under no distraction on simple road sections; and (3) compared with the experienced drivers, the novice drivers drove faster during the talking tasks on small-radius curves, but there was no significant difference between groups during the texting tasks. These findings provide both theoretical and practical implications for related policy makers to enhance traffic safety.     

Keeping the driver in the loop in conditionally automated driving: A perception-action theory approach

In this paper we investigated if keeping the driver in the perception–action loop during automated driving can improve take-over behavior from conditionally automated driving. To meet this aim, we designed an experiment in which visual exposure (perception) and manual control exposure (action) were manipulated. In a dynamic driving simulator experiment, participants (n = 88) performed a non-driving related task either in a head-up display in the windshield (high visual exposure) or on a head-down display near the gear shift (low visual exposure). While driving, participants were either in an intermittent control-mode with four noncritical take-over situations (high manual control exposure), or in a continuous automation-mode throughout the ride (low manual control exposure). In all conditions, a critical take-over had to be carried out after an approximately 13 min ride. Measurements of take-over behavior showed that only high visual exposure had an effect on hands-on reaction time measurements. Both visual exposure and manual control exposure had small to medium sized main effects on time to system deactivation, the maximum velocity of the steering wheel, and the standard deviation of the steering wheel angle. The combined high visual – and high manual control exposure condition led to 0.55 s faster reaction time and 37% less steering variability in comparison to the worst case low visual – and low manual control exposure condition. Together, results corroborate that maintaining visual exposure and manual control exposure during automated driving can be efficacious and suggest that their positive effects are additive.     

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.