The Effect of Social Cohesion on Interest, Usefulness, and Ease of Use of a Driving Assistance System in Older Adults

This study examined the relationship between social cohesion and the perceived interest in, the usefulness of, and the ease of use of an instructor-based driver assistance system in a sample of older adults. With the aging of the population, the use of technologies to support the driving skills of the elderly is expected, and it is necessary to clarify the conditions under which the elderly will be interested in these advanced technologies. Traditionally, social cohesion has been focused on as a function of instrumental and practical support in the lives of the elderly. Since social cohesion reflects the intention to help each other, it could be an opportunity to provide information on advanced driving skill techniques to older people who are becoming more difficult to drive. As an initial exploration, this study examined whether social cohesion was associated with the interest in, the usefulness of, and the ease of use of an instructor-based driver assistance system in 150 elderly people. The results showed that a greater social cohesion was significantly associated with these evaluations, and that a comprehension of the system also contributed. The possession of a license was significantly associated with interest in the program. These findings are an essential step toward the understanding of the roles of social cohesion and positive perception of advanced technology in older adults.

Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation

This work presents a novel approach to detecting stress differences between experts and novices in Situation Awareness (SA) tasks during maritime navigation using one type of wearable sensor, Empatica E4 Wristband. We propose that for a given workload state, the values of biosignal data collected from wearable sensor vary in experts and novices. We describe methods to conduct a designed SA task experiment, and collected the biosignal data on subjects sailing on a 240° view simulator. The biosignal data were analysed by using a machine learning algorithm, a Convolutional Neural Network. The proposed algorithm showed that the biosingal data associated with the experts can be categorized as different from that of the novices, which is in line with the results of NASA Task Load Index (NASA-TLX) rating scores. This study can contribute to the development of a self-training system in maritime navigation in further studies.

We are all less risky and more skillful than our fellow drivers: Replication and extension of Svenson (1981)

The better-than-average effect refers to the tendency to rate oneself as better than the average person on desirable traits and skills. In a classic study, Svenson (1981) asked participants to rate their driving safety and skill compared to other participants in the experiment. Results showed that the majority of participants rated themselves as far above the median, despite the statistical impossibility of more than 50% of participants being above the median. We report a preregistered, well-powered (total N = 1,203), very close replication and extension of the Svenson (1981) study. Our results indicate that the majority of participants rated their driving skill and safety as above average. We added different response scales as an extension and findings were stable across all three mesaures. Thus, our findings are consistent with the original findings by Svenson (1981). Materials, data, and code are available at https://osf.io/fxpwb/

Racing Driver Modeling Based on Driving Behavior

The driver model is an important link in the research of shared autonomy control. In order to simulate the driver’s handling characteristics in the complex human-vehicle-road closed-loop system, the driver model is required to accomplish the driving operation under specific working conditions. In this paper, a lateral-longitudinal combined racing driver model is designed. The lateral control model adopts the preview model with far and near viewpoints and the dynamic velocity controller is added into the longitudinal control model to obtain the expected speed of the target trajectory. Finally, the racing driver model proposed in this paper is validated through simulation on track conditions of FSAE. In the given conditions, the result shows the racing driver model outperforms the typical driver model in lateral path tracking and the speed of racing driver model is higher than typical model on straight and corners. Meanwhile, the representation of driving skills is a key step to enhance the adaptive control of vehicles in the future. The control parameters can be adjusted according to the driver’s skill information to make the vehicle control system adapt to the driver’s skill level. This paper introduces the method of driving skill recognition based on wavelet transform and Lipschitz singularity detection theory and the preliminary test results prove the feasibility of using this method to characterize the driver’s operating skill level.

Identifikation von Fahrertypen im Kontext des automatisierten Fahrens

ZusammenfassungIm Straßenverkehr existieren unterschiedliche Konzepte zur Identifikation von Fahrertypen, die sich hinsichtlich Fahrverhalten und Einstellung zum Fahren unterscheiden. Im Rahmen der Automatisierung von Fahraufgaben gilt es zu überprüfen, wie diese Konzepte an die Herausforderungen veränderter Mensch-Maschine-Interaktion angepasst werden müssen und ob sich neuartige Fahrertypen identifizieren lassen. Auf Basis bestehender Typisierungen aus der Verkehrspsychologie sowie Erkenntnissen der Automationsforschung werden dazu die Konzepte des „Driving Style“ und „Driving Skill“ weiterentwickelt, um Fahrertypen im Kontext des automatisierten Fahrens zu identifizieren. In einer großzahligen Online-Umfrage wurden drei Fahrertypen identifiziert, die sich insbesondere hinsichtlich ihrer Einstellung zum automatisierten Fahren unterscheiden. In einer experimentellen Studie im Fahrsimulator kann anschließend gezeigt werden, dass diese Fahrertypen die Automation im Fahrzeug jeweils anders erleben und daher differenzierte Ansprüche an diese richten. Insgesamt deuten die Studienergebnisse darauf hin, dass die Akzeptanz des automatisierten Fahrens durch nutzergerechte Technik gefördert werden könnte. Die Ergebnisse dienen dazu, die jeweiligen Fahrertypen, ihre Einstellungen und ihre Nutzungspräferenzen im Kontext des automatisierten Fahrens besser zu verstehen und erste Ansatzpunkte für deren Berücksichtigung in der adaptiven Technikentwicklung zu identifizieren.

Automated Driving License Evaluation

The paper is regarding the automating of driver’s license testing system and updating the results to the person through website and conjointly through registered email. Usually, while driving test the person who requested for license have to be compelled to show his driving skills ahead of the authorities. The person need to operate the vehicle according to several rules. If he fails, he/she are knocked out and have to appear for the driving test next time. The Officials observe mistakes of the applicant physically. The proposed solution for the automation of existing manual test method permits the elimination of intervention of humans and improves the accuracy of driving test thereby going paperless, with Driving Skill Evaluation System. In the proposed system, we have a tendency of taking data from sensor as inputs from hardware simulator and stores into the database. In this system, the person participating in the test are obsereved by sensors. Therefore weather the person is qualified or not is informed to the applicant as well as the authorities. Gradual increase in number of road hazards are due to less practice in driving and illegal driving license given to the unskilled drivers by taking bribe. To beat this drawback, automated driving license test will be advantageous. This solution is introduced for ensuring the quality in approving in license to enhance safety.

The Pilot Study of the Hazard Perception Test for Evaluation of the Driver’s Skill Using Virtual Reality

Background: Although various technologies are used to evaluate driving skill, there are some limitations such as the limited range of the monitor and the possible risk of causing cybersickness. The purpose of this study is to investigate differences in the hazard perception and cybersickness experienced between novice and experienced drivers measured in a VR hazard perception test with a head-mounted display (HMD). Methods: The novice (n = 32) and the experienced drivers (n = 36) participated in the hazard perception test through the VR of an HMD. Results: The total number of identified hazards was 1071 in the novice drivers and 1376 in the experienced drivers. Two of the hazards appeared to be only identifiable through the HMD. A chi-square test revealed that experienced drivers were more likely to identify the hazards than the novice drivers (p < 0.05). The novice drivers appeared to identify “hazard prediction of the current behavior of other road users” more than other hazard types, unlike the experienced group. The Simulator Sickness Questionnaire scores indicated no significant difference in the different age or gender groups (p > 0.05). Conclusion: Our results suggest that the VR hazard perception test may be useful for evaluating patients’ driving skills.

Determinants of Sleepiness at Wheel and Missing Accidents in Patients With Obstructive Sleep Apnea

Study ObjectivesMotor-vehicle crashes are frequent in untreated OSA patients but there is still uncertainty on prevalence as well as physiological or clinical determinants of sleepiness at the wheel (SW) in OSA patients. We assessed determinants of SW or sleepiness related near-miss car accident (NMA) in a group of non-professional drivers with OSA.MethodsA 237 consecutive, treatment-naïve PSG-diagnosed OSA patients (161 males, 53.1 ± 12.6 years) were enrolled. Self-reported SW was assessed by positive answer to the question, “Have you had episodes of falling asleep while driving or episodes of drowsiness at wheel that could interfere with your driving skill in the last year?” Occurrence of NMA in the last 3 years was also individually recorded. Habitual self-reported average sleep time was collected.ResultsSW was found in 41.3% of patients but one-quarter of patients with SW did not report excessive daytime sleepiness. Predictors of SW were the following subjective factors: Epworth sleepiness scale score (ESS-OR 1.26; IC 1.1–1.4; p &lt; 0.0001), depressive symptoms (BDI-OR 1.2; IC 1.06–1.18; p &lt; 0.0001) and level of risk exposure (annual mileage-OR 1.9; IC 1.15–3.1; p = 0.007). NMAs were reported by 9.7% of patients, but more frequently by SW+ than SW– (22.4% vs. 0.7%; χ2 31, p &lt; 0.0001). The occurrence of NMAs was significantly associated to ESS, BDI, habitual sleep duration and ODI (R2 = 0.41).ConclusionSW is not predicted by severity of OSA. Evaluation of risk exposure, assessment of depressive symptoms, and reported NMA should be included in the clinical evaluation, particularly in patients with reduced habitual sleep time and severe nocturnal hypoxia.