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Automated driving systems (ADS) and a combination of these with advanced driver assistance systems (ADAS) will soon be available to a large consumer population. Apart from testing automated driving features and human–machine interfaces (HMI), the development and evaluation of training for interacting with driving automation has been largely neglected. The present work outlines the conceptual development of two possible approaches of user education which are the owner’s manual and an interactive tutorial. These approaches are investigated by comparing them to a baseline consisting of generic information about the system function. Using a between-subjects design, N = 24 participants complete one training prior to interacting with the ADS HMI in a driving simulator. Results show that both the owner’s manual and an interactive tutorial led to an increased understanding of driving automation systems as well as an increased interaction performance. This work contributes to method development for the evaluation of ADS by proposing two alternative approaches of user education and their implications for both application in realistic settings and HMI testing.

Road safety is a subject of significant concern and substantially affects individuals across the globe. Thus, real-time, and post-trip interventions have gained significant importance in the past few years. This study aimed to analyze different classification techniques and examine their ability to identify dangerous driving behavior based on a dual-approach study. The analysis was based on the investigation of important risk factors such as average speed, harsh acceleration, harsh braking, headway, overtaking, distraction (i.e., mobile phone use), and fatigue. In order to achieve the objective of this study, data were collected through a driving simulator as well as a naturalistic driving study. To that end, four classification algorithms, namely support vector machines, random forest (RFs), AdaBoost, and multilayer perceptron (MLP) neural networks were implemented and compared. In the simulator experiment, RFs and MLPs emerged as the top-performing models with an accuracy of 84% and 82%, respectively, demonstrating its ability to accurately classify driving behavior in a controlled environment. In the naturalistic driving study, RF and AdaBoost maintained robust performance, with high accuracy (i.e., 75% and 76.76% respectively) and balanced precision and recall. The outcomes of this study could provide essential guidance for practitioners and researchers on choosing models for driving behavior classification tasks.

Highly automated vehicles are subject to high expectations, encompassing safety improvements, efficiency in traffic flow and overall higher comfort. However, it is still unclear to what extent automation will be able to meet these expectations. Failures in sensors or erroneous data processing might bring an automated drive to a sudden stop. In such situations, the user of the vehicle could cooperate with the automated vehicle, to bridge system limits and directly continue the drive. How this cooperation process should be implemented and how much the vehicle user wants to be involved in this process is examined. In this paper, we investigate four different cooperation strategies, ranging from low involvement of the user in the driving task (pressing a button to continue the drive) to high involvement (performing the entire driving task with steering wheel and pedals). Participants experienced these strategies in a driving simulator, in which they encountered five inner city traffic scenarios where the automation reached its limits. The cooperation strategies were evaluated along the dimensions comfort, discomfort, mental demand, usability, perceived safety, trust in automation, personal benefit, and personal preference. As a main result, strategies with less involvement show increases in comfort and personal benefit as well as decreases in discomfort and mental demand. Further, perceived safety was rated highly for all strategies. In traffic scenarios perceived as more unsafe, strategies with higher involvement are preferred. This paper offers insights into user involvement in cooperative human machine interfaces, potentially enhancing the application of automated driving in urban traffic.

New driver assistance systems play an important role to rise safety and comfort in todays´ traffic. Those systems should be developed with the needs of the user in mind and tested for the users´ requirements. In this, user acceptance is a central variable of interest – both in scientific and in practical applications of user-centered research on driver assistance systems. In some cases, applied research settings need simplified measurements in order to be efficiently applicable in the driving situations. In the present paper, we explored the applicability and validity of a single-item acceptance measurement (SIAM) for practical study settings covering the attitude towards using new driver assistance systems. To provide a theoretical framing, we tested the one-item measure against the widely used Technology Acceptance Model (TAM) and the van der Laan acceptance scale (VDL) in a driving simulator study. Participants experienced four different complex driving scenarios using a driver assistance system. Acceptance was measured repeatedly throughout the drive. The results supported construct validity for the SIAM, correlating with the VDL. The SIAM further predicted the intention to use the system. Being carefully aware of the psychometric drawbacks of short scales and acknowledging the importance of multi-item scales, the SIAM is promising for efficiently approaching the acceptance of driver assistance systems in applied settings.

Methods used to evaluate the impact of Intelligent Transport System (ITS) services on road safety are usually based on expert assessments or statistical studies. However, commonly used methods are challenging to apply in the planning process of ITS services. This paper presents the methodology of research using surrogate safety measures calculated and calibrated with the use of simulation techniques and a driving simulator. This approach supports the choice of the type of ITS services that are beneficial for traffic efficiency and road safety. This paper presents results of research on the influence of selected scenarios of variable speed limits on the efficiency and safety of traffic on the sections of motorways and expressways in various traffic conditions. The driving simulator was used to estimate the efficiency of lane-keeping by the driver. The simulation traffic models were calibrated using driving simulator data and roadside sensor data. The traffic models made it possible to determine surrogate safety measures (number of conflicts and their severity) in selected scenarios of using ITS services. The presented studies confirmed the positive impact of Variable Speed Limits (VSLs) on the level of road safety and traffic efficiency. This paper also presents recommendations and plans for further research in this area.

During highly automated driving, the passenger is allowed to conduct non-driving related activities (NDRA) and no longer has to act as a fallback at the functional limits of the driving automation system. Previous research has shown that at lower levels of automation, passengers still wish to be informed about automated vehicle behavior to a certain extent. Due to the aim of the introduction of urban automated driving, which is characterized by high complexity, we investigated the information needs and visual attention of the passenger during urban, highly automated driving. Additionally, there was an investigation into the influence of the experience of automated driving and of NDRAs on these results. Forty participants took part in a driving simulator study. As well as the information presented on the human–machine interface (system status, navigation information, speed and speed limit), participants requested information about maneuvers, reasons for maneuvers, environmental settings and additional navigation data. Visual attention was significantly affected by the NDRA, while the experience of automated driving had no effect. Experience and NDRA showed no significant effect on the need for information. Differences in information needs seem to be due to the requirements of the individual passenger, rather than the investigated factors.

Under the general trend of mixed traffic flow, an in-depth understanding of the driving behaviors of traditional vehicles is of great significance for the design of autonomous vehicles and the improvement in the safety and acceptance of autonomous vehicles. This study first obtained microdata on the behaviors of drivers through driving simulation experiments and conducted research in stages. Then, generalized linear mixed-effects models were constructed to study the main effects and interaction effects of driver attributes and traffic conditions on driving behaviors. The data analysis shows that the overall speed of drivers passing through intersections follows a “deceleration acceleration” mode, but the fluctuations are more pronounced at signalized intersections, and the signal control significantly changes the position of the lowest speed when turning left. According to the different signal control and driving tasks, there are significant differences in a driver’s acceleration patterns between the entry and exit stages. A driver’s heart rate fluctuates greatly during the exit phase, especially during straight tasks. Compared with other indicators, the change in the gaze duration is not significant. In addition, interaction effects were observed between driver attributes and traffic conditions, with participants exhibiting different behavioral patterns based on their different attributes. The research results can provide a basis for the design of driving assistance systems and further improve the interactions between autonomous vehicles and traditional vehicles at intersections.

When driving a partially automated vehicle, maintaining situation awareness is essential for users to be better prepared to take over. A primary challenge is maintaining awareness while the user is occupied with another task without tunneling attention towards individual elements. To investigate this, we conducted an experimental study in our driving simulator (n = 20) comparing an indirect LED (light-emitting diode) visualization of relevant objects in the driver’s field of view with a combined condition of an indirect LED + direct HUD (head-up display) visualization. The participants’ situation awareness scores were higher under the combined condition. However, the scores dropped significantly for objects outside the LED + HUD visualization. We conclude that the indirect object indication is not effective in countering tunneling effects from the HUD, and neither does it provide a satisfactory trade-off when deployed on its own, i.e., without direct indication in addition.

Prolonged wakefulness greatly decreases nocturnal driving performance. The development of in-car countermeasures is a future challenge to prevent sleep-related accidents. The aim of this study is to determine whether continuous exposure to monochromatic light in the short wavelengths (blue light), placed on the dashboard, improves night-time driving performance. In this randomized, double-blind, placebo-controlled, cross-over study, 48 healthy male participants (aged 20-50 years) drove 400 km (250 miles) on motorway during night-time. They randomly and consecutively received either continuous blue light exposure (GOLite, Philips, 468 nm) during driving or 2*200 mg of caffeine or placebo of caffeine before and during the break. Treatments were separated by at least 1 week. The outcomes were number of inappropriate line crossings (ILC) and mean standard deviation of the lateral position (SDLP). Eight participants (17%) complained about dazzle during blue light exposure and were removed from the analysis. Results from the 40 remaining participants (mean age ± SD: 32.9±11.1) showed that countermeasures reduced the number of inappropriate line crossings (ILC) (F(2,91.11) = 6.64; p<0.05). Indeed, ILC were lower with coffee (12.51 [95% CI, 5.86 to 19.66], p = 0.001) and blue light (14.58 [CI, 8.75 to 22.58], p = 0.003) than with placebo (26.42 [CI, 19.90 to 33.71]). Similar results were found for SDLP. Treatments did not modify the quality, quantity and timing of 3 subsequent nocturnal sleep episodes. Despite a lesser tolerance, a non-inferior efficacy of continuous nocturnal blue light exposure compared with caffeine suggests that this in-car countermeasure, used occasionally, could be used to fight nocturnal sleepiness at the wheel in blue light-tolerant drivers, whatever their age. More studies are needed to determine the reproducibility of data and to verify if it can be generalized to women. ClinicalTrials.gov NCT01070004.

Vehicle automation is linked to various benefits, such as increase in fuel and transport efficiency as well as increase in driving comfort. However, automation also comes with a variety of possible downsides, e.g., loss of situational awareness, loss of skills, and inappropriate trust levels regarding system functionality. Drawbacks differ at different automation levels. As highly automated driving (HAD, level 3) requires the driver to take over the driving task in critical situations within a limited period of time, the need for an appropriate human–machine interface (HMI) arises. To foster adequate and efficient human–machine interaction, this contribution presents a user-centered, iterative approach for HMI evaluation of highly automated truck driving. For HMI evaluation, a driving simulator study [n = 32] using a dynamic truck driving simulator was conducted to let users experience the HMI in a semi-real driving context. Participants rated three HMI concepts, differing in their informational content for HAD regarding acceptance, workload, user experience, and controllability. Results showed that all three HMI concepts achieved good to very good results in these measures. Overall, HMI concepts offering more information to the driver about the HAD system showed significantly higher ratings, depicting the positive effect of additional information on the driver–automation interaction.