| 1. |
- Bärgman, Jonas, 1972, et al.
(författare)
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On data security and analysis platforms for analysis of naturalistic driving data
- 2011
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Ingår i: Proceedings of the 8th European Congress and Exhibition on Intelligent Transport Systems and Services, June 2011, Lyon.
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Konferensbidrag (refereegranskat)abstract
- Studies involving naturalistic driving data, of which Naturalistic Field Operational Tests (N-FOTs) are a subset, are becoming increasingly important for understanding the factors influencing accident causation as well as for the development and evaluation of active safety systems. The methodology project FESTA developed a handbook on how to plan and implement FOTs. This handbook has been extensively used as a guideline in the euroFOT project. However, “the devil is in the details” when implementing e.g. the platforms for data security and analysis in projects which deal with analysis of large amounts of sensitive naturalistic driving data, such as euroFOT. That is, although a guideline such as FESTA is used, how the details are implemented is what makes the implementation a success or not. This paper is a case description of the implementation of the data security and analysis platform used for euroFOT (and other naturalistic data projects) at the SAFER Vehicle and Traffic Safety Centre. The paper covers aspects ranging from physical access to analysis rooms and corresponding digital access, via the platforms for pre-processing of data, to the platforms for information extraction for hypothesis analysis and statistics. The considerations in the design and choice of these platforms include subjects (drivers) privacy concerns, industry commercial concerns, as well as the needs and requirements from the analysis.
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| 2. |
- Bärgman, Jonas, 1972, et al.
(författare)
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The UDrive dataset and key analysis results
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- UDrive is a large European naturalistic driving study, sponsored by the European Commission (FP7).Nineteen partners across Europe have come together and, along with stakeholders, defined researchquestions, developed data acquisition, collected and managed data, and finally, performed a first analysis onthe UDrive dataset with respect to driver/rider behaviour related to traffic safety and the environment (ecodriving).This document presents key results of the UDrive analysis performed in UDrive Sub-project 4: Data analysis.It also describes the UDrive dataset and, in brief, how we got here.
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| 3. |
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| 4. |
- Carlsson, Anna K, 1966, et al.
(författare)
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Skadereducerande Effekt av Uppvärmda Trottoarer, Gång- och Cykelstråk
- 2018
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Nearly two out of three fall crashes with seriously injured pedestrians occur in the winter (December-March), i.e. twice as many pedestrians are seriously injured during these four months compared to the rest of the year. During the winter period (December-March) almost nine out of ten fall crashes are caused in the traffic environment due to slipping on ice/snow.
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| 5. |
- Carlsson, Anna K, 1966, et al.
(författare)
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Skadereducerande Effekt av Uppvärmda Trottoarer, Gång- och Cykelstråk - En Inledande Studie
- 2019
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Nästan två av tre fallolyckor med allvarligt skadade fotgängare sker under vintern; dubbelt så många fotgängare skadas allvarligt under dessa fyra månader jämfört med årets övriga. Under vinterperioden orsakas nästan nio av tio fallolyckor i trafikmiljö av halka pga is/snö. De skador som drabbar fotgängare (och cyklister) på halt underlag behöver i allmänhet betydligt längre vårdtid jämfört med om det inte varit halt. Det finns alltså en stor skadereducerande potential i förbättrad halkbekämpning. Syftet med studien är att: - Kartlägga uppvärmda ytor i stadsmiljön för de största städerna i Sverige - Analysera den skadereducerande effekten av uppvärmda ytor för fotgängare, och (om möjligt) cyklister.
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| 6. |
- Carsten, Oliver, et al.
(författare)
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Driver Distraction and Inattention
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In UDRIVE, the major focus of the work on driver inattention and distraction has been focused on obtaining a better understanding of whether and how drivers manage their secondary task activities — when they choose to engage, what tasks they select, whether they adjust their activity to different situations and whether they are willing to surrender secondary task activities as the primary task of driving becomes more demanding. In other words, the focus is on self-regulation, on how drivers manage their secondary task activity in the context of the dynamics of the traffic and road situation. That management includes the determination not to engage in such tasks in the first place or only to engage in some particular activities. NDS are particularly suited to such an investigation, since experimental studies in driving simulators and even on test tracks tend to suffer from an instruction effect, in that participants are typically instructed to carry out an activity at a given moment. Thus such experimental studies provide insight into how driver attention, driver information processing and driving performance are affected by secondary tasks, but are less useful when research is focused on driver management of task activity
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| 7. |
- Cieślik, Ilona, et al.
(författare)
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Improving the effectiveness of active safety systems to significantly reduce accidents with vulnerable road users - the project PROSPECT (Proactive Safety for Pedestrians and Cyclists)
- 2019
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Ingår i: 26th ESV Conference Proceedings.
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Konferensbidrag (refereegranskat)abstract
- Accidents involving Vulnerable Road Users (VRU) are still a very significant issue for road safety. ́PROactive Safety for PEdestrians and CyclisTs ́ is a collaborative research project funded by the European Commission. The objective of PROSPECT was to improve significantly the effectiveness of active VRU safety systems compared to those currently on the market by: (i) expanding the scope of urban scenarios addressed (ii) improving the overall Autonomous Emergency Braking (AEB) and Autonomous Emergency Steering (AES) system performance (iii) proposing extensive validation methodologies for consumer testing, simulation and acceptance studies with tools for testing. Concepts for sensors and control systems were shown in three vehicle demonstrators and a mobile driving simulator and tested with novel VRU dummy specimen. Those systems address the well-known barriers of current AEB systems such as limited sensors field-of-view, fuzzy path prediction, unreliable intent recognition and slow reaction times for the actuation. User acceptance tests with the participation of drivers were also crucial in PROSPECT for the success of all active safety systems. Driving simulator studies were then used in a controlled and repeatable environment for the collection of data regarding the interaction between the driver and the safety function. Finally, project consortium implemented a novel benefit estimation methodology that includes an assessment of the combined effect of active and passive safety measures of PROSPECT-like systems.
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| 8. |
- Díaz Fernández, P., et al.
(författare)
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Description of same-direction car-to-bicycle crash scenarios using real-world data from Sweden, Germany, and a global crash database
- 2022
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 168
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Tidskriftsartikel (refereegranskat)abstract
- The overall number of traffic crashes is decreasing, but the number of crashes incurring cyclist injuries is not decreasing at the same pace. Of all car-to-bicycle crashes, same-direction crashes are among the ones with the highest risk of a serious-to-fatal injury. In this study, car-to-bicycle crashes occurring when a passenger car and a bicycle are both traveling in the same direction and on the same road (without a physically separated lane) from four different real-world crash databases were investigated. The focus was on analyzing pre-crash factors such as speed and light conditions, as well as other factors such as impact configurations and cyclist injuries. Three main crash scenarios were identified among the crashes that were studied. The most common one (comprising 65%) was CS1: “continued same-direction” with no intention of turning by either road user. The other two scenarios were CS2: “the bicycle crosses the vehicle's path by turning” (16%) and CS3: “the car crosses the bicycle's path by turning” (19%). The CS1 crashes were divided into three overtaking phases: approaching and steering, passing, and returning, representing 42–44%, 41–44%, and 12–17%, respectively, of the CS1 scenario. The three crash scenarios varied in car and bicycle speeds, road type, and weather and light conditions, as well as in impact points and cyclist injuries. The analysis of different same-direction crash scenarios and overtaking phases in this study offers a novel view of same-direction crashes, providing relevant information for the design of methods for the evaluation of crash avoidance and injury mitigation measures for these scenarios.
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| 9. |
- Dotzauer, Mandy, et al.
(författare)
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Risk factors, crash causation and everyday driving
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The UDRIVE Naturalistic Driving Study (NDS) is the first large scale European project to observe driving behaviour directly in the field. Its goal was to identify risky driving behaviour, understand what drivers commonly do (everyday driving), why and when driver’s attention is diverted from the road (distracted driving), focus on power two wheelers (PTW), pedestrians and cyclists as they are road users that are exceptionally exposed to crashes (vulnerable road users; VRU) and learn about the properties of sustainable driving behaviour (eco-driving). One of the advantages of this project is the unique opportunity to observe critical events while they occur, and with the help of the records, go back in time and investigate what may have caused them. Drivers from France (FR), Germany (GE), Poland (PL), Spain (SP), the Netherlands (NL), and the UK volunteered to participate in this study. Mechanics equipped their vehicles with cameras and sensors and thus created a fleet of 200 vehicles. This included 120 cars (FR, GE, PL, NL, and UK), 40 scooters (SP), and 40 trucks (NL). The data was collected in a box, referred to as data acquisition system (DAS), which was installed in the trunk of the vehicles. It recorded videos of seven to eight cameras, CAN, GPS and acceleration data. The data was collected between a time period of 12 to 21 months, accumulating 87 871 hours of data. This deliverable reports the results of normal and risky driving behaviour while the findings of the other research topics will be reported in respective deliverables
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| 10. |
- Dozza, Marco, 1978, et al.
(författare)
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Modelling Interaction between Cyclists and Automobiles - Final Report
- 2020
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The MICA project modelled driver behaviour, focusing on the approaching phase of an overtaking manoeuvre, when a driver moved toward a cyclist while facing oncoming traffic (Euro NCAP test protocols inspired this scenario.). The model predicts the probability for drivers to brake or steer as they approach the cyclists to perform an accelerative (overtake after the oncoming traffic has passed) or flying (overtake before the oncoming traffic has passed) manoeuvre, respectively. This model has been integrated into a smart collision-avoidance system, that provides early (and yet acceptable) warnings and interventions. A virtual assessment estimated the safety benefits of the smart collision-avoidance system using UDRIVE naturalistic data. Our analyses show that the new smart collision-avoidance system can significantly reduce fatalities and severe injuries when compared to traditional collision-avoidance systems, with the new collision warning alone promising a reduction of fatalities by 53-96% and a reduction of serious injuries by 43-93%. This work has been carried out by three PhD students and is now continuing in the MICA2 project. The main deliverables of the project were: 1) a unique dataset collected on the airfield in Vårgårda where participants interacted with two robots, 2) a new modelling framework that helps to identify interaction on a scenario basis, 3) a novel driver model, which can predict overtaking strategy in real-time, 4) a smart collision-avoidance system which uses the driver model to generate warnings and automated interventions, and 5) a safety benefit analysis, proving the potential for the new collision-avoidance systems to save lives and reduce injuries from naturalistic European data. Nine scientific contributions describe MICA’s results: one licentiate thesis, two podium presentations to the International Cycling Safety Conference (2018 and 2019, respectively), one conference paper submitted to the Transport Research Arena 2020, and five journal papers. MICA highlighted that: 1) Modelling the interaction between the overtaking vehicle and the oncoming vehicle is an essential step to increase overtaking safety. 2) The approaching phase of an overtaking manoeuvre is not necessarily the riskiest; the most significant margin for improving safety may lay in developing systems that support the drivers in the returning phase. 3) In the approaching phase of an overtaking manoeuvre, the potential safety benefits from automated emergency steering (a system not addressed in MICA) is substantial. 4) As an overtaking manoeuvre develops from the approaching to the steering, passing, and returning phase, vehicle kinematics and proximities become more critical, challenging active safety systems and calling for new passive safety solutions. 5) More experimental data, collected in more critical situations than what was possible in MICA, is needed to address overtaking safety properly. New methodologies, such as augmented reality and virtual reality, offer the best opportunities to collect such data without ethical concerns. 6) More naturalistic data is needed to validate our driver models and the new systems that we started developing in MICA. 7) Interaction among road users is complex and models of vulnerable road-user behaviour are also needed to make robust predictions. As we move from an overtaking scenario to a crossing scenario, this aspect will become even more crucial. MICA2, a new FFI project including Volvo Cars, Autoliv, Veoneer, Viscando, if, VTI, and Chalmers, will now address these issues.
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