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.
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Met dit document wil ik de lezer een nieuwe invalshoek tonen op mobiliteit (Driving Guidance) en een andere benadering van automotive hbo onderwijs. De wereld om ons heen verandert en deze nieuwe wereld zal een ander type automotive ingenieur eisen. Dit is een korte weergave van een lezing voor de MBO-raad onderafdeling docenten automotive (Onderstructuur Btg MCT). De presentatie is gehouden op Miniconferentie Onderstructuur Btg MCT op 23 april 2010 bij Innovam te Nieuwegein. Kort worden trends op wereldniveau geschetst waarna wordt afgedaald naar het niveau van mijn werkplek. Het pad verloopt via niveau van Nederland, Regio Eindhoven, Helmond en tenslotte eindigt het pad bij Lectoraat Automotive Control. Als voorbeeld wordt het project Cooperative Driving getoond. Parallel aan de schets van werkzaamheden wordt besproken wat de veranderingen zijn in automotive onderwijs. Traditioneel komt automotive vanuit de invalshoek werktuigbouwkunde. De nieuwe opleidingen automotive HBO en WO zijn meer gericht op de drie componenten werktuigbouwkunde, elektrotechniek en ICT.
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Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best current precise localization system based on the Global Navigation Satellite System (GNSS) can not always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Laser range finder and stereo vision have been successfully used for obstacle detection, mapping and localization to solve the autonomous driving problem. Unfortunately, Light Detection and Ranging (LIDARs) are very expensive sensors and stereo vision requires powerful dedicated hardware to process the cameras information. In this context, this article presents a low-cost architecture of sensors and data fusion algorithm capable of autonomous driving in narrow two-way roads. Our approach exploits a combination of a short-range visual lane marking detector and a dead reckoning system to build a long and precise perception of the lane markings in the vehicle’s backwards. This information is used to localize the vehicle in a map, that also contains the reference trajectory for autonomous driving. Experimental results show the successful application of the proposed system on a real autonomous driving situation.
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This article delves into the acceptance of autonomous driving within society and its implications for the automotive insurance sector. The research encompasses two different studies conducted with meticulous analysis. The first study involves over 600 participants involved with the automotive industry who have not yet had the opportunity to experience autonomous driving technology. It primarily centers on the adaptation of insurance products to align with the imminent implementation of this technology. The second study is directed at individuals who have had the opportunity to test an autonomous driving platform first-hand. Specifically, it examines users’ experiences after conducting test drives on public roads using an autonomous research platform jointly developed by MAPFRE, Universidad Carlos III de Madrid, and Universidad Politécnica de Madrid. The study conducted demonstrates that the user acceptance of autonomous driving technology significantly increases after firsthand experience with a real autonomous car. This finding underscores the importance of bringing autonomous driving technology closer to end-users in order to improve societal perception. Furthermore, the results provide valuable insights for industry stakeholders seeking to navigate the market as autonomous driving technology slowly becomes an integral part of commercial vehicles. The findings reveal that a substantial majority (96% of the surveyed individuals) believe that autonomous vehicles will still require insurance. Additionally, 90% of respondents express the opinion that policies for autonomous vehicles should be as affordable or even cheaper than those for traditional vehicles. This suggests that people may not be fully aware of the significant costs associated with the systems enabling autonomous driving when considering their insurance needs, which puts the spotlight back on the importance of bringing this technology closer to the general public.
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Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best localization systems based on GNSS cannot always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Recent works have shown the advantage of using maps as a precise, robust, and reliable way of localization. Typical approaches use the set of current readings from the vehicle sensors to estimate its position on the map. The approach presented in this paper exploits a short-range visual lane marking detector and a dead reckoning system to construct a registry of the detected back lane markings corresponding to the last 240 m driven. This information is used to search in the map the most similar section, to determine the vehicle localization in the map reference. Additional filtering is used to obtain a more robust estimation for the localization. The accuracy obtained is sufficiently high to allow autonomous driving in a narrow road. The system uses a low-cost architecture of sensors and the algorithm is light enough to run on low-power embedded architecture.
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IntroductionThe driving pressure (ΔP) has an independent association with outcome in patients with acute respiratory distress syndrome (ARDS). INTELLiVENT-Adaptive Support Ventilation (ASV) is a closed-loop mode of ventilation that targets the lowest work and force of breathing.AimTo compare transpulmonary and respiratory system ΔP between closed-loop ventilation and conventional pressure controlled ventilation in patients with moderate-to-severe ARDS.MethodsSingle-center randomized cross-over clinical trial in patients in the early phase of ARDS. Patients were randomly assigned to start with a 4-h period of closed-loop ventilation or conventional ventilation, after which the alternate ventilation mode was selected. The primary outcome was the transpulmonary ΔP; secondary outcomes included respiratory system ΔP, and other key parameters of ventilation.ResultsThirteen patients were included, and all had fully analyzable data sets. Compared to conventional ventilation, with closed-loop ventilation the median transpulmonary ΔP with was lower (7.0 [5.0–10.0] vs. 10.0 [8.0–11.0] cmH2O, mean difference − 2.5 [95% CI − 2.6 to − 2.1] cmH2O; P = 0.0001). Inspiratory transpulmonary pressure and the respiratory rate were also lower. Tidal volume, however, was higher with closed-loop ventilation, but stayed below generally accepted safety cutoffs in the majority of patients.ConclusionsIn this small physiological study, when compared to conventional pressure controlled ventilation INTELLiVENT-ASV reduced the transpulmonary ΔP in patients in the early phase of moderate-to-severe ARDS. This closed-loop ventilation mode also led to a lower inspiratory transpulmonary pressure and a lower respiratory rate, thereby reducing the intensity of ventilation.Trial registration Clinicaltrials.gov, NCT03211494, July 7, 2017. https://clinicaltrials.gov/ct2/show/NCT03211494?term=airdrop&draw=2&rank=1.
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Het onderzoek in het artikel is geïnspireerd door de casus 'platooning' uit de Grand Cooperative Driving Challenge. Er is een PreScan®/Sumulink® model opgesteld met daarin twee auto's. De voorste auto volgt een vastgesteld snelheidsprofiel, de tweede auto volgt de eerste auto waarbij de tweede auto de snelheid van de eerste meet met behulp van een AIR-sensor. De besturing van het gaspedaal in beide auto's vindt plaats met Fuzzy Logic Control in plaats van met een klassieke regelaar. Concluderend mag worden gesteld dat in dit verkennend onderzoek gebleken is dat de Fuzzy Logic Control techniek in principe werkt.
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Presentation for logistics professionals (industry and government) on the factors that drive firms' decisions on distribution channel layout and distribution centre locations.
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