Test procedure for the evaluation of partially automated driving HMI including driver monitoring systems in driving simulation.

The proposed test procedure presents an approach for the evaluation of the usability of partial automated driving HMI including driver monitoring systems in driving simulation. This procedure is based on a definition of requirements that a Level 2 HMI and its included driver monitoring system must fulfill in order to guarantee that the drivers understand their responsibilities of continuously monitoring the driving environment and the status of the partial automated driving system. These requirements are used to define the evaluation criteria that have to be validated in the test as well as the use cases in which these criteria can be assessed. The result is a detailed and comprehensive test guide including the specification of the test drives, the necessary instructions, the test environment and the recruiting criteria for the test sample.•Evaluation of usability aspects of level 2 automated driving HMI including driver monitoring systems•Based on the definition of requirements for L2 HMI•Test guide including the definition of use cases, evaluation criteria and testing conditions in driving simulation.

The impact of speech-based assistants on the driver's cognitive distraction.

Speech-based interfaces can be a promising alternative and/or addition to visual-manual interfaces since they reduce visual-manual distraction while driving. However, there are also findings indicating that speech-based assistants may be a source of cognitive distraction. The aim of this experiment was to quantify drivers' cognitive distraction while interacting with speech-based assistants. Therefore, 31 participants performed a simulated driving task and a detection response task (DRT). Concurrently they either sent text-messages via speech-based assistants (Siri, Google Assistant, or Alexa) or completed an arithmetic task (OSPAN). In a multifactorial approach, following Strayer et al. (2017), cognitive distraction was then assessed through performance in the DRT, the driving speed, the task completion time and self-report measures. The cognitive distraction associated with speech-based assistants was compared to the OSPAN task and a baseline condition without a secondary task. Participants reacted faster and more accurately to the DRT in the baseline condition compared to the speech conditions. The performance in the speech conditions was significantly better than in the OSPAN task. However, driving speed did not significantly differ between the experimental conditions. Results from the NASA-TLX indicate that speech-based tasks were more demanding than the baseline but less demanding than the OSPAN task. The task completion times revealed significant differences between speech-based assistants. Sending messages took longest with the Google Assistant. Referring to the findings by Strayer et al. (2017), we conclude that nowadays speech-based assistants are associated with a rather moderate than high level of cognitive distraction. Nonetheless, we point towards the need to assess the effects of human-machine interaction via speech-based interfaces due to their potential for cognitive distraction.

The box task - a method for assessing in-vehicle system demand.

The use of advanced in-vehicle information systems (IVIS) and other complex devices such as smartphones while driving can lead to driver distraction, which, in turn, increases safety-critical event risk. Therefore, using methods for measuring driver distraction caused by IVIS is crucial when developing new in-vehicle systems. In this paper, we present the setup and implementation of the Box Task combined with a Detection Response Task (BT+DRT) as a tool to assess visual-manual and cognitive distraction effects. The BT+DRT represents a low-cost and easy-to-use method which can be easily implemented by researchers in laboratory settings and which was validated in previous research. Moreover, at the end of this paper we describe the experimental procedure, the data analysis and discuss potential modifications of the method.•The setup and implementation of the Box Task combined with a Detection Response Task (BT+DRT) is described.•The method allows for measuring visual-manual and cognitive distraction of drivers.•The BT+DRT is a cost-effective and easy-to-use method that can be implemented in laboratory settings or driving simulators.

Measuring driver distraction - Evaluation of the box task method as a tool for assessing in-vehicle system demand.

Several tools have been developed over the past twenty years to assess the degree of driver distraction caused by secondary task engagement. A relatively new and promising method in this area is the box task combined with a detection response task (BT + DRT). However, no evaluation regarding the BT's sensitivity currently exists. Thus, the aim of the present study was to evaluate the BT + DRT by comparing its sensitivity to the sensitivity of already established methods. Twenty-nine participants engaged in several artificial and realistic secondary tasks while either performing the BT + DRT, the Lane Change Test (LCT), or driving through a simple course in a simulator. The results showed that the BT parameters (especially the standard deviation of box position and size) were sensitive to differences in demand across the visual-manual secondary tasks. This was comparable to what was found with the LCT. Surprisingly, the BT performance measures were more sensitive than those of the driving simulation task. The BT + DRT also captured cognitive distraction effects with the integration of the DRT. Hence, the BT + DRT could be a cost-effective method to assess in-vehicle system demand. However, further investigations are necessary to better understand the potential of the BT method.

Using European naturalistic driving data to assess secondary task engagement when stopped at a red light.

PROBLEM: Some evidence exists that drivers choose to engage in secondary tasks when the driving demand is low (e.g., when the car is stopped). While such a behavior might generally be considered as rather safe, it could be argued that the associated diversion of attention away from the road still leads to a reduction of situational awareness, which might increase collision risk once the car regains motion. This is especially relevant for texting, which is associated with considerable eyes-off-the-road-time. Nonetheless, it seems that previous research has barely addressed the actual engagement in secondary tasks while waiting at a red light (as compared to just addressing the tasks' mere prevalence). OBJECTIVE: The present study investigated secondary task engagement while stopped at a red light using European naturalistic driving data collected through the UDRIVE project. Attention was given to the whole engagement process, including simple prevalence and the tasks' relation (in terms of start/end) to the red light period. Moreover, given that texting is one of the most problematic forms of distraction, it was characterized in more detail regarding glance behavior. METHOD: Videos of 804 red light episodes from 159 drivers were annotated. Glance behavior was also coded for a sub-set of 75 texting events and their matched baselines. Results, conclusions and practical applications: Drivers engaged in at least one secondary task across almost half of the annotated red light episodes. Drivers who texted while stopped spent most of the time looking at their cell phone. Consequently, drivers might not have been prepared for potentially unexpected events once the light turned green. Further, drivers concluded texting a considerable number of times well after the red light period, which has potential implications for traffic safety.

Test procedure for evaluating the human-machine interface of vehicles with automated driving systems.

Objective: The human-machine interface (HMI) is a crucial part of every automated driving system (ADS). In the near future, it is likely that-depending on the operational design domain (ODD)-different levels of automation will be available within the same vehicle. The capabilities of a given automation level as well as the operator's responsibilities must be communicated in an appropriate way. To date, however, there are no agreed-upon evaluation methods that can be used by human factors practitioners as well as researchers to test this. Methods: We developed an iterative test procedure that can be applied during the product development cycle of ADS. The test procedure is specifically designed to evaluate whether minimum requirements as proposed in NHTSA's automated vehicle policy are met. Results: The proposed evaluation protocol includes (a) a method to identify relevant use cases for testing on the basis of all theoretically possible steady states and mode transitions of a given ADS; (b) an expert-based heuristic assessment to evaluate whether the HMI complies with applicable norms, standards, and best practices; and (c) an empirical evaluation of ADS HMIs using a standardized design for user studies and performance metrics. Conclusions: Each can be used as a stand-alone method or in combination to generate objective, reliable, and valid evaluations of HMIs, focusing on whether they meet minimum requirements. However, we also emphasize that other evaluation aspects such as controllability, misuse, and acceptance are not within the scope of the evaluation protocol.

Using SHRP 2 naturalistic driving data to assess drivers' speed choice while being engaged in different secondary tasks.

INTRODUCTION: The engagement in secondary tasks while driving has been found to result in considerable impairments of driving performance. Texting has especially been suspected to be associated with an increased crash risk. At the same time, there is evidence that drivers use various self-regulating strategies to compensate for the increased demands caused by secondary task engagement. One of the findings reported from multiple studies is a reduction in driving speed. However, most of these studies are of experimental nature and do not let the drivers decide for themselves to (not) engage in the secondary task, and therefore, eliminate other strategies of self-regulation (e.g., postponing the task). The goal of the present analysis was to investigate if secondary task engagement results in speed adjustment also under naturalistic conditions. METHOD: Our analysis relied on data of the SHRP 2 naturalistic driving study. To minimize the influence of potentially confounding factors on drivers' speed choice, we focused on episodes of free flow driving on interstates/highways. Driving speed was analyzed before, during, and after texting, smoking, eating, and adjusting/monitoring radio or climate control; in a total of 403 episodes. RESULTS: Data show some indication for speed adjustment for texting, especially when driving with high speed. However, the effect sizes were small and behavioral patterns varied considerably between drivers. The engagement in the other tasks did not influence drivers' speed behavior significantly. CONCLUSIONS AND PRACTICAL APPLICATIONS: While drivers might indeed reduce speed slightly to accommodate for secondary task engagement, other forms of adaptation (e.g., strategic decisions) might play a more important role in a natural driving environment. The use of naturalistic driving data to study drivers' self-regulatory behavior at an operational level has proven to be promising. Still, in order to obtain a comprehensive understanding about drivers' self-regulatory behavior, a mixed-method approach is required.

Evaluation of in-vehicle HMI using occlusion techniques: experimental results and practical implications.

Despite the usefulness of new on-board information systems one has to be concerned about the potential distraction effects that they impose on the driver. Therefore, methods and procedures are necessary to assess the visual demand that is connected to the usage of an on-board system. The occlusion-method is considered a strong candidate as a procedure for evaluating display designs with regard to their visual demand. This paper reports results from two experimental studies conducted to further evaluate this method. In the first study, performance in using an in-car navigation system was measured under three conditions: static (parking lot), occlusion (shutter glasses), and driving. The results show that the occlusion-procedure can be used to simulate visual requirements of real traffic conditions. In a second study the occlusion method was compared to a global evaluation criterion based on the total task time. It can be demonstrated that the occlusion method can identify tasks which meet this criterion, but are yet irresolvable under driving conditions. It is concluded that the occlusion technique seems to be a reliable and valid method for evaluating visual and dialogue aspects of in-car information systems.