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Autonomous vehicles may become an important part of traffic in the next few decades. One of the key factors that contribute to the acceptance of the technology is that the user can understand and to a certain degree predict the behaviour of the automation. One way to communicate the automation's intention and behaviour are graphical human-machine interfaces (HMIs). An online survey was conducted to evaluate five designs for HMIs for turning manoeuvres in automated vehicles. During the survey the participants rated the HMIs based on videos. The overall ratings don't show a clear favourite, however, a separation of the participants regarding their trust in automation and technical affinity indicate preferences regarding the preferred level of abstraction. Furthermore, 97% of the participants agreed that HMIs like the ones presented would help them gain or increase their trust in automated vehicles.

This work aims to create a test bed for analyzing the performance characteristics of any electric vehicle motor via simulation and emulation. Given test bed can emulate the load torque which an electric motor may experience in actual drive conditions. The core part of the test bed comprises of a Permanent Magnet DC (PMDC) generator which is controlled by a special chopper circuit to emulate the mechanical characteristics of any electric vehicle. The vehicle's mechanical characteristics at various speeds is emulated by controlling the special chopper circuit connected to the DC generator which in turn controls torque the test motor will experience as both are connected via same shaft. The mass of the electric vehicle, frictional coefficients, tire radius and other functional elements are input parameters that can be entered by the user. Using these parameters, the load torque that an electric vehicle motor should experience is calculated from electrical vehicle simulation model based on the equations of various load forces acting on the vehicle for a given speed. According to the calculated torque value, chopper circuit of DC generator is controlled to emulate the torque for a given speed. The system can also be fed with a variable drive cycle. To test the effectiveness of test bed, DC motor drive is coupled to the test bed and the performance verified using MATLAB/Simulink.

Turning through oncoming traffic at unsignalized intersections can lead to safety-critical situations contributing to 7.4% of all non-severe vehicle crashes. One of the main reasons for these crashes are human errors in the form of incorrect estimation of the gap size with respect to the Principle Other Vehicle (POV). Vehicle-to-vehicle (V2V) technology promises to increase safety in various traffic situations. V2V infrastructure combined with further integration of sensor technology and human intention prediction could help reduce the frequency of these safety-critical situations by predicting dangerous turning manoeuvres in advance, thus, allowing the POV to prepare an appropriate reaction.

Causal models allow us to reconstruct traffic accidents, predict the likelihood of future accidents and implement counter measures to prevent them. For drivers with impairments like visual field loss, naturalistic data on crash causes is however scarce due to their current prohibition to drive. This paper presents an approach to deriving a causal model for the prediction of crash risks for current non-drivers. The applied use case focuses on a collision with an overlooked crossing vehicle in an intersection. Based on the combination of crash analyses for normal sighted drivers and models of information processing and human errors, a general structural causal model for crash risks in this use case was developed. The application of this model to drivers with visual field loss on the side of the approaching vehicle revealed four causal factors with an increased risk of occurring: faulty anticipation of location and timing of hazards; inadequate guidance of gaze movements; adverse scanning patterns; and cognitive overload. These elevated crash risks can guide the development of assistive technologies for drivers with visual impairments in the future.

We performed a driving simulator study to investigate merging decisions with respect to an interaction partner in time-critical situations. The experimental paradigm was a two-alternative forced choice, where the subjects could choose to merge before human vehicles or highly automated vehicles (HAV). Under time pressure, subjects showed a significantly higher gap acceptance during merging situations when interacting with HAV. This confirmed our original hypothesis that when interacting with HAV, drivers would exploit the HAV's technological advantages and defensive programming in time-critical situations.

This paper presents a new mobile near-infrared functional spectroscopy (fNIRS) device, with digital detectors that can be placed anywhere on the head and fit into standard caps to measure cortical brain activation. The device's functionality was evaluated in two steps, i.e. first, by means of simple pulse measurements and second, in a motor cortex study with nine subjects. In this study, the subjects had to alternate between right and left hands while using hand-held strength trainers. While the signals from the mobile prototype were not yet stable enough across all channels to perform analysis such as statistical parametric mapping, it was able to measure significant brain activation changes over the area of the motor cortex with the mobile prototype when the contralateral hand was activated in four subjects. In contrast, the device was yet unable to measure ipsilateral activities. The problems encountered and possible methods to improve signal acquisition are discussed at the end of the paper.

Driving is a complex task concurrently drawing on multiple cognitive resources. Yet, there is a lack of studies investigating interactions at the brain-level among different driving subtasks in dual-tasking. This study investigates how visuospatial attentional demands related to increased driving difficulty interacts with different working memory load (WML) levels at the brain level. Using multichannel whole-head high density functional near-infrared spectroscopy (fNIRS) brain activation measurements, we aimed to predict driving difficulty level, both separate for each WML level and with a combined model. Participants drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. In half of the time, the course led through a construction site with reduced lane width, increasing visuospatial attentional demands. Concurrently, participants performed a modified version of the n-back task with five different WML levels (from 0-back up to 4-back), forcing them to continuously update, memorize, and recall the sequence of the previous ‘n’ speed signs and adjust their speed accordingly. Using multivariate logistic ridge regression, we were able to correctly predict driving difficulty in 75.0% of the signal samples (1.955 Hz sampling rate) across 15 participants in an out-of-sample cross-validation of classifiers trained on fNIRS data separately for each WML level. There was a significant effect of the WML level on the driving difficulty prediction accuracies [range 62.2–87.1%; χ2(4) = 19.9, p < 0.001, Kruskal–Wallis H test] with highest prediction rates at intermediate WML levels. On the contrary, training one classifier on fNIRS data across all WML levels severely degraded prediction performance (mean accuracy of 46.8%). Activation changes in the bilateral dorsal frontal (putative BA46), bilateral inferior parietal (putative BA39), and left superior parietal (putative BA7) areas were most predictive to increased driving difficulty. These discriminative patterns diminished at higher WML levels indicating that visuospatial attentional demands and WML involve interacting underlying brain processes. The changing pattern of driving difficulty related brain areas across WML levels could indicate potential changes in the multitasking strategy with level of WML demand, in line with the multiple resource theory.

Driving is a complex and cognitively demanding task. It is essential to assess the cognitive state of the driver in order to design cognitive technical systems that can adapt to different driver cognitive states. Our research attempts to assess these states using functional Near Infrared Spectroscopy (fNIRS) by measuring brain activity in a virtual reality driving simulator. However, the fNIRS brain activation measurements could be influenced by muscle activity and we wanted to investigate this phenomenon. For this, we designed a paradigm with two conditions (listen, teeth clench) which show a significant contrast in the influence of muscle activity. We observed that the muscle hemodynamic response can show a higher magnitude of signal change compared to brain hemodynamic response. The muscle hemodynamic response showed an increase in deoxygenated hemoglobin (HbR) whereas the brain hemodynamic response showed a decrease in HbR. Moreover, the dynamics of the brain and muscle hemodynamic response differed. The brain response showed the same latency for oxygenated hemoglobin (HbO) and HbR while the muscle HbR response had a slower latency compared to HbO. We concluded that the fNIRS brain activation measurements could indeed be influenced by muscle activity. We were also able to determine some characteristics of the muscle hemodynamic response.

Driving is a complex and cognitively demanding task. It is important to assess the cognitive state of the driver in order to develop cognitive technical systems that can adapt to different cognitive states of the driver. For this purpose, we have developed a mobile functional near-infrared spectroscopy (mofNIRS) prototype. This paper describes the improvements of this mobile prototype with freely placeable optodes on a subject’s head and the results of an evaluation study. We conducted a motor cortex experiment with four subjects, whereby the mobile prototype was mounted on the right hemisphere and a commercial, stationary fNIRS on the left hemisphere above the motor cortex area. One data set had to be discarded due to incorrect synchronization between both systems. The results of the remaining three subjects are presented and discussed in this paper. Here, we report the results from the time-series and Statistical Parametric Mapping (SPM) analyses, which shows t-values with high differentiability of the Results. Furthermore, both analysis methods show comparable results between the commercial system and the mobile prototype.

Introduction and Aim: Driving is a goal-directed task. During traffic, blocking obstacles occur often eliciting driver frustration, and may result in more risky driving including speeding and aggressive behavior towards other traffic road participants. We envision to use ‘adaptive automation’ [1] to design driver assistance systems that prevent those maladaptive driving behavior by supporting the driver to reduce the current frustration level or increasing the level of automation. For this, it is essential that we can assess driver frustration already at an early stage. As affective states come along with a change in cognitive appraisal and a subjectively experienced feeling, brain activity seems to be a promising indicator of frustration. Thus, we aim to investigate if brain activation fNIRS measurements could be used to assess realistically occurring driver frustration. Methods: Thirteen volunteers participated in a driving simulator study, in which frustration was induced through a combination of time pressure and blocking events. A cover story asked the participants to imagine that they had to deliver a parcel to a client within six minutes with the incentive of gaining 2 € per successful delivery. In the six frustrating scenarios (FRUST), the drives were blocked by heavy traffic, while in the six non-frustrating scenarios (NOFRUST), participants could drive mostly unblocked. FNIRS data were recorded from the frontal and parietal cortices using a 32-channel neuroNIRX-system during the course of the whole experiment. Results: We subjected the individual fNIRS data to an SPM-based GLM analysis which revealed relative concentration increases of oxy-hemoglobin (HbO) and decreases of deoxy-hemoglobin (HbR) during FRUST compared to NOFRUST drives. These changes were found bilaterally in ventrolateral frontal and temporo-occipital channels in the group analysis as well as at the individual level (see Fig. 1 for group level and an example of an individual analysis. Note the much higher t-values in the individual analysis). Discussion & Conclusion: Our results indicate that brain activity patterns of frustrated drivers are discernible from non-frustrated drivers using fNIRS. The revealed brain areas displaying higher activity in the frustrating drives are in line with the relatively scarce literature on frustration-related neuroimaging results and seem to be related to cognitive appraisal, impulse control and emotion regulation processes. Since frustration is a multi-component phenomenon, it could be helpful to measure behavioral and other physiological measures in combination with fNIRS data to acquire a robust assessment of frustration. Acknowledgements: This work was funded by a grant of the Volkswagen Foundation and the Ministry of Science and Culture of Lower Saxony to the Research Centre on Critical Systems Engineering for Socio-Technical Systems. Klas Ihme and Anirudh Unni contributed equally. References [1] Parasuraman, R. (1987). Human-computer monitoring. Human Factors, 29, 695-706

Cognitive overload or underload results in a decrease in human performance which may result in fatal incidents while driving. We envision that driver assistive systems which adapt their functionality to the driver’s cognitive state could be a promising approach to reduce road accidents due to human errors. This research attempts to predict variations of cognitive working memory load levels in a natural driving scenario with multiple parallel tasks and to reveal predictive brain areas. We used a modified version of the n-back task to induce five different working memory load levels (from 0-back up to 4-back) forcing the participants to continuously update, memorize, and recall the previous ‘n’ speed sequences and adjust their speed accordingly while they drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. We measured brain activation using multichannel whole head, high density functional near-infrared spectroscopy (fNIRS) and predicted working memory load level from the fNIRS data by combining multivariate lasso regression and cross-validation. This allowed us to predict variations in working memory load in a continuous time-resolved manner with mean Pearson correlations between induced and predicted working memory load over 15 participants of 0.61 [standard error (SE) 0.04] and a maximum of 0.8. Restricting the analysis to prefrontal sensors placed over the forehead reduced the mean correlation to 0.38 (SE 0.04), indicating additional information gained through whole head coverage. Moreover, working memory load predictions derived from peripheral heart rate parameters achieved much lower correlations (mean 0.21, SE 0.1). Importantly, whole head fNIRS sampling revealed increasing brain activation in bilateral inferior frontal and bilateral temporo-occipital brain areas with increasing working memory load levels suggesting that these areas are specifically involved in workload-related processing.

Introduction and Aim: We envision that driver assistive systems which adapt their functionality to the driver’s cognitive state could be a promising approach to reduce road accidents due to human errors [1]. Workload is an important cognitive state because a cognitive overload or underload results in a decrease in human performance which may result in fatal incidents while driving. Here, we investigate if it’s possible to predict variations of cognitive workload levels (WL) from fNIRS brain activation measurements while driving to inform adaptive assistive systems in future cars. Methods: In our study, we implemented the n-back working memory task with several continuously changing load levels as a speed regulation task into a realistic driving simulation in order to control and manipulate cognitive workload. We introduced five different workload levels (i.e. 0-back to 4-back) and speed signs every 20 seconds while the participant was driving on a highway with concurrent traffic. Depending on the current n-back task, the participant was supposed to remember the previous ‘n’ speed sequences and adjust his speed accordingly. A detailed explanation for the n-back experimental paradigm can be found in [2]. FNIRS data were recorded from the frontal and parietal cortices using a 32-channel neuroNIRX-system from five participants during the course of the whole experiment which lasted for around 30 minutes. Results: We used the multivariate linear regression approach by combining fNIRS data from all channels to predict WL using the elastic net regularization model which combines the L1 and L2 penalties of the lasso and ridge regression techniques to get a continuous estimate of workload over time. Fig.1 depicts a plot of WL induced by the n-back task (red curve) and the WL predicted by the model (blue curve) for an example participant. The correlation between the two curves is almost 0.8 for a 10-fold cross-validation. For the remaining four participants, we achieved a correlation between 0.6 and 0.8 which were all statistically significant (p < 0.05). Fig.1: Ten-fold cross-validated prediction of workload from deoxyhemoglobin fNIRS measurements using multivariate regression analysis in an example participant Discussion & Conclusion: It can be seen from Fig.1 that the predicted WL more or less follows the induced WL and could be used to predict if the WL is either increasing or decreasing. There are certain regions in the blue curve where the model seems to over- or underestimate WL which may be due to the incomplete model which currently neglects WL imposed by the concurrent driving task in the changing traffic situations. As a next step, we plan to introduce some non-linearity into the workload model to get much better prediction rates. Acknowledgements: This work was funded by a grant of the Volkswagen Foundation and the Ministry of Science and Culture of Lower Saxony to the Research Centre on Critical Systems Engineering for Socio-Technical Systems. Anirudh Unni and Klas Ihme contributed equally. References [1] Parasuraman, R. (1987). Human-computer monitoring. Human Factors, 29, 695-706 [2] Unni et al. (2015). Brain activity measured with fNIRS for the prediction of cognitive workload. 6th IEEE Conference on Cognitive Infocommunications, 349-354

Negative affective states such as frustration may lead to aggressive behaviors and have negative consequences on the experienced comfort of drivers. Hence, an affect-aware system which can recognize the current affective state of a driver and react to it appropriately may be able to support the regulation of emotions and to prevent negative consequences. Although research has been reported on the in-car recognition of affective states, very little is known about methods to mitigate frustration using interaction strategies. In this work, we report the results of a workshop with five human factors experts. Three scenarios were discussed and 23 ideas for adaptation strategies generated by the experts. Based on these ideas, we propose four concepts for how to mitigate driver's frustration through affect-aware in-vehicle systems using ambient light patterns. Our goal is to develop, evaluate and demonstrate such systems in the future.

Adapting an automation system to the workload level of a human operator can be beneficial in many situations, e.g. at industrial workplaces or in safety-critical situations like driving and flying an aircraft. However, this requires real-time assessment of workload. We present a model-based approach for online simulation and assessment of cognitive workload, based on analysing the activities of a cognitive architecture during simulation. A driving simulator experiment was used to evaluate the approach. The cognitive workload of participants was manipulated with a variant of the n-back task as secondary task that parametrically varies memory workload. A virtual driver model was created using the cognitive architecture. The model was simulated in the same situations as the human drivers. The activities of the cognitive architecture as indicator of cognitive workload increased with increasing difficulty of the n-back task. To relate model workload to human driver workload, we compared it with neurophysiological workload measures based on functional near-infrared spectroscopy (fNIRS) brain activation as a complementary measure.

Functional near-infrared spectroscopy (fNIRS) is a versatile imagining modality whose popularity is increasing exponentially in the neuroimaging society. Our research attempts to quantify workload in a natural driving scenario with multiple parallel tasks using fNIRS. Nine young adults participated in this study where they drove in a driving simulator for a period of 100 minutes while we continuously recorded fNIRS data. We used an n-back task to induce different workload levels forcing the participants to remember the previous one, two, three or four speed signs and adjust their speed accordingly while they interact with traffic in the virtual reality driving simulator scenario. Our results indicate that measuring the hemodynamic responses from the bilateral prefrontal cortex (PFC) can be used reliably to quantify cognitive workload levels even in more complex naturalistic tasks.