Objective: To explore driving performance and driving safety in patients with cervical dystonia (CD) on a simulated lane tracking, intersections and highway ride and to compare it to healthy controls. Design: This study was performed as an explorative between groups comparison. Participants: Ten CD patients with idiopathic CD, 30 years or older, stable on botulinum toxin treatment for over a year, holding a valid driver's license and being an active driver were compared with 10 healthy controls, matched for age and gender. Main outcome measures: Driving performance and safety, measured by various outcomes from the simulator, such as the standard deviation of the lateral position on the road, rule violations, percentage of line crossings, gap distance, and number of collisions. Fatigue and driving effort were measured with the Borg CR-10 scale and self-perceived fitness to drive was assessed with Fitness to Drive Screening. Results: Except for a higher percentage of line crossings on the right side of the road by controls (median percentage 2.30, range 0.00-37.00 vs. 0.00, range 0.00-9.20, p = 0.043), no differences were found in driving performance and driving safety during the simulator rides. Fatigue levels were significantly higher in CD patients just before (p = 0.005) and after (p = 0.033) the lane tracking ride (patients median fatigue levels before 1.5 (range 0.00-6.00) and after 1.5 (range 0.00-7.00) vs. controls median fatigue levels before and after 0.00 (no range). No significant differences were found on self-perceived fitness to drive. Conclusion: In patients with CD there were no indications that driving performance or driving safety were significant different from healthy controls in a simulator. Patients reported higher levels of fatigue both before and after driving compared to controls in accordance with the non-motor symptoms known in CD.
USE conference paper.Ever since the mid-1970s a multitude of studies linking corporate sustainability performance (CSP) measures and financial performance measures have been conducted. Until today a plethora of corporate sustainability performance measures heve been developed. A universally accepted CSP definition of construct does not (yet) exist. Since we don't exactley know what CSP entails, CSP measures should (at least) be considered conceptually flawed for that matter. These measures may measure CSP, but it cannot e excluded that other (overarching) phenomena are measured. There are leads suggesting that CSP measures are reflections or representations of corporate culture, suggesting that corporate culture drives FP. If so, managers should not focus on increasing CSP to boost FP, but create a high culture for sustainability If corporate culture drives financial performance, the investment community can also benefit through improving its decision making processes by including CSP measures that reflect corporate culture.
In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
