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.

User Evaluation of Passenger Assistance System Concepts on Public Highways

There is ample research on assistance systems for drivers in conventional and automated vehicles. In the past, those systems were developed to increase safety but also to increase driver comfort. Since many common risks have by now been mitigated through such systems, the research and development focus expanded to also include comfort-related assistance. However, the passenger has rarely been taken into account explicitly, although it has been shown that passenger discomfort is a relevant problem. Therefore, this work investigated the potential of passenger assistance systems to reduce such discomfort. Three different passenger assistant system prototypes were tested in a driving study on public highway with N = 19 participants. The systems provided information about parameters related to the performance of the driver and one additionally provided a communicative means of influence. For two passenger assistant systems, it could be shown that they significantly reduced passenger discomfort in at least a subset of the evaluated situations. The majority of participants rated one or multiple of the assistant systems as more comfortable than a ride without assistance. The system providing information about the attentiveness of the driver was most effective in reducing discomfort and was rated as the most helpful system. The results show that explicitly considering the situation of passengers in the design of assistance systems can positively impact their comfort. This can be achieved using information from common systems targeting driver assistance available to the passenger.

Effects of explaining system failures during maneuver coordination while driving manual or automated

To support the coordination of road users in situations like merging or turning left, an advanced driver assistance system for cooperative driving could be helpful whether driving manually or automated. This simulator study investigated the behavior of drivers being confronted with system failures. In two test situations with system failures (loss of communication of the system and change of traffic environment), the system could not complete the coordination properly and the driver was informed about the system failure and the abortion of maneuver coordination. The focus of this study was to analyze the effect of system failures on drivers' trust in the system and whether an explanatory message provided by the system would increase acceptance. Therefore, subjective data as well as gaze and physiological data of 32 participants were analyzed. The results revealed decreased trust in the system after experiencing a system failure, but no long term effect was found. The drivers evaluated the timing, as well as the content, of the explanatory message as appropriate. The explanations were perceived as helpful, but no effect on acceptance was found.

Effect of different alcohol levels on take-over performance in conditionally automated driving

Automated driving systems are getting pushed into the consumer market, with varying degrees of automation. Most often the driver's task will consist of being available as a fall-back level when the automation reaches its limits. These so-called take-over situations have attracted a great body of research, focusing on various human factors aspects (e.g., sleepiness) that could undermine the safety of control transitions between automated and manual driving. However, a major source of accidents in manual driving, alcohol consumption, has been a non-issue so far, although a false understanding of the driver's responsibility (i.e., being available as a fallback level) might promote driving under its influence. In this experiment, N = 36 drivers were exposed to different levels of blood alcohol concentrations (BACs: placebo vs. 0.05% vs. 0.08%) in a high fidelity driving simulator, and the effect on take-over time and quality was assessed. The results point out that a 0.08% BAC increases the time needed to re-engage in the driving task and impairs several aspects of longitudinal and lateral vehicle control, whereas 0.05% BAC did only go along with descriptive impairments in fewer parameters.

Driver compliance to take-over requests with different auditory outputs in conditional automation

Conditionally automated driving (CAD) systems are expected to improve traffic safety. Whenever the CAD system exceeds its limit of operation, designers of the system need to ensure a safe and timely enough transition from automated to manual mode. An existing visual Human-Machine Interface (HMI) was supplemented by different auditory outputs. The present work compares the effects of different auditory outputs in form of (1) a generic warning tone and (2) additional semantic speech output on driver behavior for the announcement of an upcoming take-over request (TOR). We expect the information carried by means of speech output to lead to faster reactions and better subjective evaluations by the drivers compared to generic auditory output. To test this assumption, N=17 drivers completed two simulator drives, once with a generic warning tone ('Generic') and once with additional speech output ('Speech+generic'), while they were working on a non-driving related task (NDRT; i.e., reading a magazine). Each drive incorporated one transition from automated to manual mode when yellow secondary lanes emerged. Different reaction time measures, relevant for the take-over process, were assessed. Furthermore, drivers evaluated the complete HMI regarding usefulness, ease of use and perceived visual workload just after experiencing the take-over. They gave comparative ratings on usability and acceptance at the end of the experiment. Results revealed that reaction times, reflecting information processing time (i.e., hands on the steering wheel, termination of NDRT), were shorter for 'Speech+generic' compared to 'Generic' while reaction time, reflecting allocation of attention (i.e., first glance ahead), did not show this difference. Subjective ratings were in favor of the system with additional speech output.

Driving performance at lateral system limits during partially automated driving

This study investigated driver performance during system limits of partially automated driving. Using a motion-based driving simulator, drivers encountered different situations in which a partially automated vehicle could no longer safely keep the lateral guidance. Drivers were distracted by a non-driving related task on a touch display or driving without an additional secondary task. While driving in partially automated mode drivers could either take their hands off the steering wheel for only a short period of time (10s, so-called 'Hands-on' variant) or for an extended period of time (120s, so-called 'Hands-off' variant). When the system limit was reached (e.g., when entering a work zone with temporary lines), the lateral vehicle control by the automation was suddenly discontinued and a take-over request was issued to the drivers. Regardless of the hands-off interval and the availability of a secondary task, all drivers managed the transition to manual driving safely. No lane exceedances were observed and the situations were rated as 'harmless' by the drivers. The lack of difference between the hands-off intervals can be partly attributed to the fact that most of the drivers kept contact to the steering wheel, even in the hands-off condition. Although all drivers were able to control the system limits, most of them could not explain why exactly the take-over request was issued. The average helpfulness of the take-over request was rated on an intermediate level. Consequently, providing drivers with information about the reason for a system limit can be recommended.

Evaluating distraction of in-vehicle information systems while driving by predicting total eyes-off-road times with keystroke level modeling

Increasingly complex in-vehicle information systems (IVIS) have become available in the automotive vehicle interior. To ensure usability and safety of use while driving, the distraction potential of system-associated tasks is most often analyzed during the development process, either by employing empirical or analytical methods, with both families of methods offering certain advantages and disadvantages. The present paper introduces a method that combines the predictive precision of empirical methods with the economic advantages of analytical methods. Keystroke level modeling (KLM) was extended to a task-dependent modeling procedure for total eyes-off-road times (TEORT) resulting from system use while driving and demonstrated by conducting two subsequent simulator studies. The first study involved the operation of an IVIS by N = 18 participants. The results suggest a good model fit (R(2)Adj. = 0.67) for predicting the TEORT, relying on regressors from KLM and participant age. Using the parameter estimates from study 1, the predictive validity of the model was successfully tested during a second study with N = 14 participants using a version of the IVIS prototype with a revised design and task structure (rPred.-Obs. = 0.58). Possible applications and shortcomings of the approach are discussed.

Cooperative warning systems: The impact of false and unnecessary alarms on drivers' compliance

Cooperative warning systems have a great potential to prevent traffic accidents. However, because of their predictive nature, they might also go along with an increased frequency of incorrect alarms that could limit their effectiveness. To better understand the consequences associated with incorrect alarms, a driving simulator study with N=80 drivers was conducted to investigate how situational context and warning urgency jointly influence drivers' compliance with an unreliable advisory warning system (AWS). The participants encountered several critical urban driving situations and were either assisted by a 100% reliable AWS, a 60% reliable AWS that generated false alarms (without obvious reason) or a 60% reliable AWS that generated unnecessary alarms (with plausible reason). A baseline drive without any assistance was also introduced to the study. The warnings were presented either only visually or visual-auditory. In line with previous research, drivers' compliance and effectiveness of the AWS was reduced by false alarms but not by unnecessary alarms. However, this so-called cry wolf effect (Breznitz, 1984) was only found in the visual-auditory condition, whereas there was no effect of warning reliability in the condition with visual AWS. Furthermore, false but not unnecessary alarms caused the participants to rate the AWS less favourably during a follow-up interview. In spite of these negative effects of false alarms, a reduction in the frequency of safety-critical events (SCEs) and an earlier braking onset were evident in all assisted drives compared with that of non-assisted driving, even when the AWS was unreliable. The results may thus lower concerns about the negative consequences of warning drivers unnecessarily about upcoming traffic conflicts if the reasons of these alarms are comprehensible. From a perspective of designing AWS, we recommend to use less urgent warnings to prevent the cry wolf effect.