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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <title>Customer Data Driven PHEV Refuel Distance Modeling and Estimation</title>
      <link>https://trid.trb.org/View/1461972</link>
      <description><![CDATA[Plug-in hybrid electric vehicles (PHEV) have an EV mode driving range which can cover a portion of customer daily driving. This EV mode range affects the refuel frequency substantially compared with conventional vehicle. For a conventional vehicle, daily driving pattern, tank size and fuel economy are the factors affecting the refuel frequency. While for a PHEV, EV range is another factor would affect the results substantially. Traditional method of label range can’t represent real world driving range between fill-ups for PHEV well. How to accurately predict the PHEV refuel distance taking into account real world customer driving patterns and PHEV parameters become critical for PHEV system design and optimization. This paper presents real world big customer data based PHEV refuel distance estimation modeling. The target is to estimate PHEV refuel distance given several specific parameters such as EV range, hybrid mode fuel economy, tank size etc. A big EuroFOT data set is used for the analysis and model development. Then a linear model is developed based on sensitivity analysis. The estimation results are compared with the NHTS data based estimation, and validated with the real world PHEV data. Finally an Neural Network based estimation model is proposed to further capture the non-linearity in the model and improve accuracy.       ]]></description>
      <pubDate>Tue, 01 Aug 2017 10:08:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1461972</guid>
    </item>
    <item>
      <title>Wirksamkeitsanalyse von Fahrerassistenzsystemen in Bezug auf die Verkehrssicherheit</title>
      <link>https://trid.trb.org/View/1459100</link>
      <description><![CDATA[Im Rahmen der Arbeit wurde eine Methodik zur Untersuchung der Wirkung eines Fahrerassistenzsystems auf die Verkehrssicherheit entwickelt, implementiert und angewendet. Die Implementierung und Anwendung der Methodik erfolgten für ein Beispielsystem zwecks der Validierung der Methodik. Gegenüber den bisher existierenden Methodiken zeichnet sich die hier entwickelte Methodik durch die verbundene Betrachtung der Wirkung in einzelnen Fahrsituationen sowie der Wirkung in größeren Verkehrsszenarien aus. Ausgehend von der Analyse der Stärken und Schwächen bisheriger Ansätze zur Wirksamkeitsanalyse wurden die Anforderungen an die im Rahmen der Arbeit entwickelte Methodik definiert. Anschließend wurde die Methodik, die sich in fünf Hauptprozessschritte gliedert, erläutert. Im ersten Schritt der Methodik erfolgt die allgemeine Beschreibung des Verkehrsgeschehens sowie die Implementierung einer Datenbank für kritische Fahrsituationen, die Situationen aus dem euroFOT-Feldversuch enthält. Auf Basis dieser Daten wurden die zu untersuchenden relevanten Fahrsituationen und Verkehrssituationen beschrieben. Die relevanten Fahrsituationen basieren auf der Kritikalität sowie der Auftrittshäufigkeit innerhalb der zu betrachtenden Menge an Fahrsituationen. Die Beschreibung der relevanten Fahrsituationen sowie der Verkehrssituationen bildeten den Ausgangspunkt für die durchgeführten Simulationen. Im Rahmen der Arbeit wurde die entwickelte Methodik auf ein fiktives automatisches Notbremssystem angewendet. In der Simulation wurde bei einer vollständigen Marktdurchdringung eine Reduktion der Unfälle in Höhe von 30,95 % ermittelt. (A)]]></description>
      <pubDate>Thu, 30 Mar 2017 09:18:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1459100</guid>
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    <item>
      <title>Drivers anticipate lead-vehicle conflicts during automated longitudinal control: Sensory cues capture driver attention and promote appropriate and timely responses</title>
      <link>https://trid.trb.org/View/1439946</link>
      <description><![CDATA[Adaptive Cruise Control (ACC) has been shown to reduce the exposure to critical situations by maintaining a safe speed and headway. It has also been shown that drivers adapt their visual behavior in response to the driving task demand with ACC, anticipating an impending lead vehicle conflict by directing their eyes to the forward path before a situation becomes critical. The purpose of this paper is to identify the causes related to this anticipatory mechanism, by investigating drivers’ visual behavior while driving with ACC when a potential critical situation is encountered, identified as a forward collision warning (FCW) onset (including false positive warnings). This paper discusses how sensory cues capture attention to the forward path in anticipation of the FCW onset. The analysis used the naturalistic database EuroFOT to examine visual behavior with respect to two manually-coded metrics, glance location and glance eccentricity, and then related the findings to vehicle data (such as speed, acceleration, and radar information). Three sensory cues (longitudinal deceleration, looming, and brake lights) were found to be relevant for capturing driver attention and increase glances to the forward path in anticipation of the threat; the deceleration cue seems to be dominant. The results also show that the FCW acts as an effective attention-orienting mechanism when no threat anticipation is present. These findings, relevant to the study of automation, provide additional information about drivers’ response to potential lead-vehicle conflicts when longitudinal control is automated. Moreover, these results suggest that sensory cues are important for alerting drivers to an impending critical situation, allowing for a prompt reaction.]]></description>
      <pubDate>Wed, 21 Dec 2016 11:34:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1439946</guid>
    </item>
    <item>
      <title>Incident Detection Based on Vehicle CAN-Data Within the Large Scale Field Operational Test “euroFOT”</title>
      <link>https://trid.trb.org/View/1363492</link>
      <description><![CDATA[The euroFOT project is the first large-scale Field Operational Test (FOT) of multiple Advanced Driver Assistance Systems (ADAS) in Europe. It will evaluate the impact of ADAS on safety, traffic efficiency, environment, driver behaviour and user acceptance in real life situations with normal drivers by means of collected data from instrumented vehicles. By offering valuable information for the short- and long-term impact of ADAS the euroFOT project aims to encourage the deployment of ADAS. Altogether, about 1000 vehicles equipped with different ADAS technologies will take part in the field operational test. The FOT is coordinated by five Vehicle Management Centers (VMC) and carried out at various operation sites across six European countries (France, Germany, Italy, Netherlands, Sweden and United Kingdom). Within this paper the approach and the requirements for implementing a reliable and automated incident detection process by means of CAN-data for assessing the impact of ADAS at the German1-VMC are presented.]]></description>
      <pubDate>Wed, 05 Aug 2015 09:38:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1363492</guid>
    </item>
    <item>
      <title>Behavioral Changes and User Acceptance of Adaptive Cruise Control (ACC) and Forward Collision Warning (FCW): Key Findings Within an European Naturalistic Field Operational Test</title>
      <link>https://trid.trb.org/View/1360845</link>
      <description><![CDATA[In the euroFOT project multiple Advanced Driver Assistance Systems (ADAS) were tested within a  large-scale Field Operational Test (FOT) in Europe. Main objective of the project was the impact assessment of different ADAS on safety, traffic efficiency, environment, driver behaviour and user-acceptance in real life situations with normal drivers. The needed data was gathered by means of  instrumented vehicles. Altogether, about 1000 vehicles from different manufacturers and with different advanced driver assistance systems took part in the FOT. The Institute of Automotive Engineering (ika) of the RWTH Aachen University analysed the effects of Adaptive Cruise Control (ACC) usage in  combination with Forward Collision Warning (FCW) under normal driving conditions of 100  passenger cars. The results of the data analysis show positive effects on traffic safety and fuel consumption. In terms of traffic safety a reduction in number of incidents, harsh braking and critical  time-headways were determined. These reductions can be attributed to changed distance behaviour of  the drivers.]]></description>
      <pubDate>Tue, 28 Jul 2015 15:56:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/1360845</guid>
    </item>
    <item>
      <title>Naturalistic Driving Data. Re-Analyse von Daten aus dem EU-Projekt euroFOT</title>
      <link>https://trid.trb.org/View/1330974</link>
      <description><![CDATA[Naturalistic Driving Studies (NDS) gewinnen immer mehr an Verbreitung. Durch die Datenerhebung im Feld waehrend alltaeglicher Fahrten von Normalfahrern haben sie den Vorteil, dass die aufgezeichneten Daten das normale Fahrverhalten der Probanden abbilden. Aufgrund der grossen Menge an anfallenden Daten sowie verschiedener methodischer Probleme stellt die Auswertung von NDS-Daten allerdings Anforderungen, die sich so bei anderen Untersuchungsansaetzen nicht ergeben. Fahrdaten, die von circa 100 Fahrern ueber einen Zeitraum von drei Monaten erhoben wurden, werden hinsichtlich drei inhaltlicher Fragestellungen ausgewertet. Im ersten Themengebiet wird das Fahrverhalten mittels globaler Parameter, wie der mittleren Geschwindigkeit oder des praeferierten Abstands beschrieben. Es ergeben sich Hinweise auf stabile Unterschiede zwischen den Fahrern, die sich im Sinne von Fahrstil beziehungsweise Fahrertyp interpretieren lassen. Im zweiten Themengebiet wird die Haeufigkeit von Telefonieren ueber die Freisprecheinrichtung ermittelt. Fahrer sprechen im Schnitt rund 10% der Fahrzeit ueber die Freisprecheinrichtung, das meiste davon sind ausgehende Telefonate. Ausserdem wird untersucht, ob es waehrend der Telefonate zu Aenderungen im Fahrverhalten kommt. Es ergeben sich Hinweise auf eine aktive Kompensation der zusaetzlichen Beanspruchung durch die Fahrer. Diese erfolgt allerdings weniger auf der Regelebene sondern vielmehr durch die Auswahl laengerer, wenig beanspruchender Fahrsituationen (zum Beispiel im Stau). Die inhaltlichen Ergebnisse zu den beiden ersten Themengebieten werden im jeweiligen Kapitel detailliert dargestellt. Die Analysen zu den ersten beiden Themengebieten zeigen, dass NDS-Daten gut geeignet sind, normales Fahrverhalten zu beschreiben. Es zeigt sich aber auch, dass die Varianz in den Daten durch die Kontrolle situativer Einflussfaktoren wie zum Beispiel der Strassenklasse nur schwer in den Griff zu kriegen ist. Deswegen wird einer neuer manoeverbasierter Auswerteansatz entwickelt und implementiert. Nun werden die Daten zuerst in miteinander vergleichbare Handlungseinheiten - die Fahrmanoever - untergliedert. Diese sind die Basis fuer alle weiteren Auswertungen. Im dritten Themengebiet, der Erfassung kritischer Fahrsituationen, wird der manoeverbasierte Auswerteansatz zum ersten Mal angewandt. Es wird exemplarisch gezeigt, dass durch die Auswertung miteinander vergleichbarer Handlungseinheiten gut interpretierbare Ergebnisse moeglich sind. So werden die verwendeten Kriterien zur Detektion kritischer Fahrsituationen in Bezug auf verschiedene Fahrmanoever geprueft und moegliche Schwachstellen aufgedeckt. Ausserdem entstehen durch die Unterteilung in Fahrmanoever sinnvolle Einheiten, die die Berechnung von Risikoparametern erlauben. Das Ergebnis, dass Telefonieren das Risiko fuer kritische Fahrsituationen im Folgefahren reduziert, stimmt mit den aus der Literatur bekannten Ergebnissen ueberein. Insgesamt wird mit den durchgefuehrten Auswertungen gezeigt, dass mit NDS insbesondere normales Fahren beschrieben und zum Beispiel auf situative Einfluesse hin untersucht werden kann. Auch die Analyse seltener Ereignisse, wie beispielsweise kritische Fahrsituationen, ist prinzipiell moeglich. Es sind hierbei aber bereits bei der Datenerhebung bestimmte Voraussetzungen zu erfuellen. Um aus NDS verstaendliche und gut interpretierbare Ergebnisse zu erhalten, ist es allerdings noetig, neue Auswerteansaetze zu entwickeln. Ansonsten laeuft man Gefahr, das Potenzial der in NDS extrem aufwendig erhobenen Daten nicht auszuschoepfen. Basierend auf den durchgefuehrten inhaltlichen Analysen werden am Ende des Berichts Vor- und Nachteile von NDS diskutiert und es werden Empfehlungen fuer zukuenftige Datenerhebungen und -auswertungen gegeben. (A)]]></description>
      <pubDate>Mon, 05 Jan 2015 13:02:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/1330974</guid>
    </item>
    <item>
      <title>Automated detection and classification process for critical incidents and driving events by means of vehicle data</title>
      <link>https://trid.trb.org/View/1323129</link>
      <description><![CDATA[Data from field operational tests is more and more used to analyze the impact of advanced driver assistance systems. Due to the high amount of collected data the analysis of this data is time-consuming. Within this paper an automated detection process for recognition of relevant driving situations based on vehicle data has will be presented. By means of automation the analysis can be conducted within a reasonable time period. Moreover the algorithm for detection of the relevant situations have been defined and tested, in order to ensure a reliable detection. The automated recognition process was applied for data processing and analysis of the gathered data within the euroFOT Field Operational Test (FOT). The euroFOT project is the first large-scale Field Operational Test (FOT) of multiple Advanced Driver Assistance Systems (ADAS) in Europe. Main objective of the project was the impact assessment of different ADAS on safety, traffic efficiency, environment, driver behaviour and user-acceptance in real life situations with normal drivers. The needed data was gathered by means of instrumented vehicles. Altogether, about 1000 vehicles from different manufacturers and with different advanced driver assistance systems took part in the FOT. The FOT was coordinated by five so called Vehicle Management Centers (VMC) and carried out at various operation sites across six European countries (France, Germany, Italy, Netherlands, Sweden and United Kingdom). Within this paper the approach and the requirements for implementing a reliable and automated incident and event detection process by means of CAN-data for assessing the impact of ADAS are presented.]]></description>
      <pubDate>Wed, 24 Sep 2014 16:49:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/1323129</guid>
    </item>
    <item>
      <title>Impact evaluation of speed regulation systems using naturalistic driving data: the EuroFOT example</title>
      <link>https://trid.trb.org/View/1320193</link>
      <description><![CDATA[The goal of this paper is to present the work done by IFSTTAR and CEESAR during the first large scale field operational test in Europe: the EuroFOT project. During the project duration, the FESTA methodology has been applied in real conditions for the first time, in order to evaluate 8 different mature driving assistance systems. The French partners were in charge of performing an impact assessment of the speed regulation system, which is a bundle of two different functions: speed limiter, and cruise control. In this paper, we present the overall process of performing such an evaluation using naturalistic driving data coming from 35 drivers from Paris region. We provide the reader with technical, practical, and methodological aspects, with a focus to the lessons learned. The general conclusion takes profit from the results to determine the best practice for evaluating the impacts of a longitudinal driving assistance system.]]></description>
      <pubDate>Thu, 14 Aug 2014 10:17:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1320193</guid>
    </item>
    <item>
      <title>How do drivers interact with navigation systems in real life conditions?: Results of a field-operational-test on navigation systems</title>
      <link>https://trid.trb.org/View/1313554</link>
      <description><![CDATA[As part of the project euroFOT, the impact and usage of navigation systems was studied in a Field-Operational Test (FOT). The usage and handling of two HMI-solutions for navigation systems – one was nomadic and the other integrated – were investigated during daily drives. For N = 99 drivers, data was recorded whenever drivers used their vehicles during a three month period. During these three months, drivers used an integrated navigation system for a month and a nomadic device for a month. In the third month, they did not use a navigation system at all (baseline). Drivers preferred system handling in low demanding driving situations, like standstill or at very low speeds. If system handling occurred while the vehicle was moving, then an adaption of speed and following distance was observed. No increase of critical driving situations, like very close distances, could be found during system inputs. Results indicated that drivers were cautious when they interacted with the navigation systems. They adapted their system handling to the demands of driving and there is no indication that driving safety was jeopardized. These results help to gain a better understanding of how experimental results on driver distraction relate to unobserved driver behavior during daily drives.]]></description>
      <pubDate>Thu, 24 Jul 2014 15:18:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/1313554</guid>
    </item>
    <item>
      <title>Recognising safety critical events: Can automatic video processing improve naturalistic data analyses?</title>
      <link>https://trid.trb.org/View/1278843</link>
      <description><![CDATA[New trends in research on traffic accidents include Naturalistic Driving Studies (NDS). NDS are based on large scale data collection of driver, vehicle, and environment information in real world. NDS data sets have proven to be extremely valuable for the analysis of safety critical events such as crashes and near crashes. However, finding safety critical events in NDS data is often difficult and time consuming. Safety critical events are currently identified using kinematic triggers, for instance searching for deceleration below a certain threshold signifying harsh braking. Due to the low sensitivity and specificity of this filtering procedure, manual review of video data is currently necessary to decide whether the events identified by the triggers are actually safety critical. Such reviewing procedure is based on subjective decisions, is expensive and time consuming, and often tedious for the analysts. Furthermore, since NDS data is exponentially growing over time, this reviewing procedure may not be viable anymore in the very near future.  This study tested the hypothesis that automatic processing of driver video information could increase the correct classification of safety critical events from kinematic triggers in naturalistic driving data. Review of about 400 video sequences recorded from the events, collected by 100 Volvo cars in the euroFOT project, suggested that drivers’ individual reaction may be the key to recognize safety critical events. In fact, whether an event is safety critical or not often depends on the individual driver. A few algorithms, able to automatically classify driver reaction from video data, have been compared. The results presented in this paper show that the state of the art subjective review procedures to identify safety critical events from NDS can benefit from automated objective video processing. In addition, this paper discusses the major challenges in making such video analysis viable for future NDS and new potential applications for NDS video processing. As new NDS such as SHRP2 are now providing the equivalent of five years of one vehicle data each day, the development of new methods, such as the one proposed in this paper, seems necessary to guarantee that these data can actually be analysed.]]></description>
      <pubDate>Mon, 23 Dec 2013 07:52:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/1278843</guid>
    </item>
    <item>
      <title>Ergebnisse der Wirkungsanalyse von Adaptive Cruise Control (ACC) und Forward Collision Warning (FCW) im Rahmen eines Feldversuchs</title>
      <link>https://trid.trb.org/View/1261351</link>
      <description><![CDATA[Ziel des euroFOT-Projekts ist die Bereitstellung von Informationen ueber die positiven Kurz- und Langzeiteffekte zur Foerderung der Weiterentwicklung von Fahrerassistenzsystemen (FAS). Im Rahmen des euroFOT Feldversuchs (Field Operation Test, FOT) wurden Daten von etwa 1.000 Fahrzeugen mit verschiedenen FAS erhoben. Mit Hilfe der erhobenen Daten wurde die Wirkungsanalyse von acht verschiedenen FAS im realen Verkehr untersucht. Adaptive Cruise Control (ACC) und Forward Collision Warning (FCW) zeigen einen positiven Effekt auf die Verkehrssicherheit, die Verkehrseffizienz, den Kraftstoffverbrauch sowie das Fahrerverhalten und die Fahrerakzeptanz. ABSTRACT in ENGLISH: The euroFOT project is the first large-scale field operational test (FOT) of multiple advanced driver assistance systems (ADAS) in Europe. It aimed at evaluating the impacts of ADAS in real life situations with normal drivers, by means of collected data from instrumented vehicles. By offering valuable information for the short and long-term impact of ADAS the project aims to encourage the deployment of ADAS. Altogether, about 1,000 vehicles from different manufacturers and with different advanced driver assistance systems took part in the field operational test. The results of the data analysis show positive effects of Adaptive Cruise Control (ACC) and Forward Collision Warning (FCW) on traffic safety, traffic efficiency, fuel consumption, environment, driver behavior and user acceptance.]]></description>
      <pubDate>Wed, 11 Dec 2013 09:39:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/1261351</guid>
    </item>
    <item>
      <title>Investigation of driver sleepiness in FOT data: final report of the project SleepEYE II, part 2</title>
      <link>https://trid.trb.org/View/1265285</link>
      <description><![CDATA[SleepEYE was a collaborative project between Smart Eye, Volvo Cars and VTI (the Swedish National Road and Transport Research Institute) within the competence centre Virtual Prototyping and Assessment by Simulation (ViP).  The project was carried out during the years 2009&ndash;2011, and included development of a camera-based system for driver impairment detection and development of a driver sleepiness classifier adapted for driving simulators.  A continuation project called SleepEYE II was initiated in 2011.  This project has included three work packages: 1) simulator validation with respect to driver sleepiness, 2) assessment of driver sleepiness in the euroFOT database using the camera system that was developed in SleepEYE, and 3) further development and refinement of the camera system.  The present report documents the work that has been undertaken in work packages 2 and 3 of SleepEYE II.]]></description>
      <pubDate>Thu, 17 Oct 2013 10:01:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1265285</guid>
    </item>
    <item>
      <title>Results and Lessons Learned of a Subjective Field Operational Test on the Lane Departure Warning Function</title>
      <link>https://trid.trb.org/View/1255241</link>
      <description><![CDATA[The paper presents the preliminary results and the lessons learned from the Italian Field Operational Test on the Lane Departure Warning (LDW) function that is being carried out within the European project euroFOT. The FOT has deployed a large scale subjective test involving a sample of 570 drivers and using a wide and differentiated set of self-reported questionnaires about system usage and impact. The purpose of the FOT is to investigate the subjective aspects about LDW system users’ acceptance and the perceived impact of the LDW system on safety, driving behaviour and transport-related aspects. Results are expected to accurately depict the actual impact of this function based on subjective data.]]></description>
      <pubDate>Wed, 28 Aug 2013 12:32:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1255241</guid>
    </item>
    <item>
      <title>Recognizing Safetycritical Events from Naturalistic Driving Data</title>
      <link>https://trid.trb.org/View/1255159</link>
      <description><![CDATA[New trends in research on traffic accidents comprehend Naturalistic Driving Studies (NDS). NDS are based on large-scale data collection of driver, vehicle, and environment information in real traffic. NDS datasets have proven to be extremely valuable for the analysis of safety-critical events such as crashes and near crashes. However, finding safety-critical events in NDS data may be difficult and time consuming. Safety-critical events are currently individuated using kinematic triggers (e.g., searching for deceleration below a certain threshold signifying harsh braking). Due to the low sensitivity and specificity of this filtering procedure, manual review of video data is – to date – necessary to decide whether the events individuated by the triggers are actually safety-critical. Such reviewing procedure is based on subjective decisions, is time consuming, and is often tedious for the analysts. This study tested the hypothesis that automatic processing of driver video information could increase the correct classification of safety-critical events from kinematic triggers in naturalistic driving data. Review of about 400 videos from the triggered events collected by 100 Volvo cars in the euroFOT project, suggested that driver's individual reaction may be the key to discriminate safety-critical events. In fact, whether an event is safety-critical or not often depends on the individual driver. A few algorithms, able to automatically classify driver reaction from video data, have been compared. The results presented in this paper show that the state-of-the-art subjective review procedures to individuate safety-critical events from NDS can benefit from automated objective video analysis. In addition, this paper discusses the major challenges in making such a video analysis viable for future NDS.]]></description>
      <pubDate>Mon, 29 Jul 2013 12:58:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/1255159</guid>
    </item>
    <item>
      <title>Wirkungsanalyse von Abstandsregelung und Abstandswarnung</title>
      <link>https://trid.trb.org/View/1252534</link>
      <description><![CDATA[Innerhalb des 7. Rahmenprogramms der Europaeischen Kommission wurde der erste gross angelegte Feldversuch zur Untersuchung der Wirkung von Fahrerassistenzsystemen (FAS) durch verschiedene Projektpartner in Europa gestartet. Im euroFOT-Feldversuch wurden acht verschiedene FAS getestet und ihre Wirkung auf Fahrverhalten, -sicherheit, Verkehrseffizienz und Kraftstoffverbrauch bewertet. Insgesamt wurden etwa 1.000 Fahrzeuge mit verschiedenen FAS im Rahmen des Feldversuchs eingesetzt. Neben der Umsetzung der kompletten Prozesskette zur Datenerhebung und -verarbeitung wurde am Institut fuer Kraftfahrzeuge der RWTH Aachen University eine Wirkungsanalyse fuer die Abstandsregelung (Adaptive Cruise Control, ACC) und die Abstandswarnung (Forward Collision Warning, FCW) an 100 Pkw durchgefuehrt. Die Ergebnisse der Wirkungsanalyse zeigen positive Effekte auf die Verkehrssicherheit, das Fahrerverhalten, die Fahrerakzeptanz und den Kraftstoffverbrauch.]]></description>
      <pubDate>Mon, 08 Jul 2013 10:20:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/1252534</guid>
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