The Detection-Response Task is a method for assessing the attentional effects of cognitive load in a driving environment. Drivers are presented with a sensory stimulus every 3–5 s, and are asked to respond to it by pressing a button attached to their finger. Response times and hit rates are interpreted as indicators of the attentional effect of cognitive load. The stimuli can be visual, tactile and auditory, and are chosen based on the type of in-vehicle system or device that is being evaluated. Its biggest disadvantage is that the method itself also affects the driver’s performance and secondary task completion times. Nevertheless, this is an easy to use and implement method, which allows relevant assessment and evaluation of in-vehicle systems. By following the recommendations and taking into account its limitations, researchers can obtain reliable and valuable results on the attentional effects of cognitive load on drivers.

The introduction of shared autonomous vehicles into the transport system is suggested to bring significant impacts on traffic conditions, road safety and emissions, as well as overall reshaping travel behaviour. Compared with a private autonomous vehicle, a shared automated vehicle (SAV) is associated with different willingness-to-adopt and willingness-to-pay characteristics. An important aspect of future SAV adoption is the presence of other passengers in the SAV—often people unknown to the cotravellers. This study presents a cross-country exploration of user preferences and WTP calculations regarding mode choice between a private non-autonomous vehicle, and private and shared autonomous vehicles. To explore user preferences, the study launched a survey in seven European countries, including a stated-preference experiment of user choices. To model and quantify the effect of travel mode attributes and socio-demographic characteristics, the study employs a mixed logit model. The model results were the basis for calculating willingness-to-pay values for all countries and travel modes, and provide insight into the significant heterogeneous, gender-wise effect of cotravellers in the choice to use an SAV. The study results highlight the importance of analysis of the effect of SAV attributes and shared-ride conditions on the future acceptance and adoption rates of such services.

Autonomous vehicles are expected to take complete control of the driving process, enabling the former drivers to act as passengers only. This could lead to increased sickness as they can be engaged in tasks other than driving. Adopting different sickness mitigation techniques gives us unique types of motion sickness in autonomous vehicles to be studied. In this paper, we report on a study where we explored the possibilities of assessing motion sickness with electrogastrography (EGG), a non-invasive method used to measure the myoelectric activity of the stomach, and its potential usage in autonomous vehicles (AVs). The study was conducted in a high-fidelity driving simulator with a virtual reality (VR) headset. There separate EGG measurements were performed: before, during and after the driving AV simulation video in VR. During the driving, the participants encountered two driving environments: a straight and less dynamic highway road and a highly dynamic and curvy countryside road. The EGG signal was recorded with a proprietary 3-channel recording device and Ag/AgCl cutaneous electrodes. In addition, participants were asked to signalize whenever they felt uncomfortable and nauseated by pressing a special button. After the drive they completed also the Simulator Sickness Questionnaire (SSQ) and reported on their overall subjective perception of sickness symptoms. The EGG results showed a significant increase of the dominant frequency (DF) and the percentage of the high power spectrum density (FSD) as well as a significant decrease of the power spectrum density Crest factor (CF) during the AV simulation. The vast majority of participants reported nausea during more dynamic conditions, accompanied by an increase in the amplitude and the RMS value of EGG. Reported nausea occurred simultaneously with the increase in EGG amplitude. Based on the results, we conclude that EGG could be used for assessment of motion sickness in autonomous vehicles. DF, CF and FSD can be used as overall sickness indicators, while the relative increase in amplitude of EGG signal and duration of that increase can be used as short-term sickness indicators where the driving environment may affect the driver.

Autonomous vehicles are anticipated to play an important role on future mobility offering encouraging solutions to today’s transport problems. However, concerns of the public, which can affect the AVs’ uptake, are yet to be addressed. This study presents relevant findings of an online survey in eight European countries. First, 1639 responses were collected in Spring 2020 on people’s commute, preferred transport mode, willingness to use AVs and demographic details. Data was analyzed for the entire dataset and for vulnerable road users in particular. Results re-confirm the long-lasting discourse on the importance of safety on the acceptance of AVs. Spearman correlations show that age, gender, education level and number of household members have an impact on how people may be using or allowing their children to use the technology, e.g., with or without the presence of a human supervisor in the vehicle. Results on vulnerable road users show the same trend. The elderly would travel in AVs with the presence of a human supervisor. People with disabilities have the same proclivity, however their reactions were more conservative. Next to safety, reliability, affordability, cost, driving pleasure and household size may also impact the uptake of AVs and shall be considered when designing relevant policies.

The ability to measure drivers’ physiological responses is important for understanding their state and behavior under different driving conditions. Such measurements can be used in the development of novel user interfaces, driver profiling, advanced driver assistance systems, etc. In this paper, we present a user study in which we performed an evaluation of two commercially available wearable devices for assessment of drivers’ physiological signals. Empatica’s E4 wristband measures blood volume pulse (BVP), inter-beat interval (IBI), galvanic skin response (GSR), temperature, and acceleration. Bittium’s Faros 360 is an electrocardiographic (ECG) device that can record up to 3-channel ECG signals. The aim of this study was to explore the use of such devices in a dynamic driving environment and their ability to differentiate between different levels of driving demand. Twenty-two participants (eight female, 14 male) aged between 18 and 45 years old participated in the study. The experiment compared three phases: Baseline (no driving), easy driving scenario, and demanding driving scenario. Mean and median heart rate variability (HRV), standard deviation of R–R intervals (SDNN), HRV variables for shorter time frames (standard deviation of the average R–R intervals over a shorter period—SDANN and mean value of the standard deviations calculated over a shorter period—SDNN index), HRV variables based on successive differences (root mean square of successive differences—RMSSD and percentage of successive differences, greater than 50 ms—pNN50), skin temperature, and GSR were observed in each phase. The results showed that motion artefacts due to driving affect the GSR recordings, which may limit the use of wrist-based wearable devices in a driving environment. In this case, due to the limitations of the photoplethysmography (PPG) sensor, E4 only showed differences between non-driving and driving phases but could not differentiate between different levels of driving demand. On the other hand, the results obtained from the ECG signals from Faros 360 showed statistically significant differences also between the two levels of driving demand.