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| 2. |
- Andersson, Ronnie, 1975, et al.
(författare)
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EP2819891 B1
- 2016
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Patent (övrigt vetenskapligt/konstnärligt)
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| 3. |
- Andersson, Ronnie, 1975, et al.
(författare)
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US 9,493,137
- 2016
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Patent (övrigt vetenskapligt/konstnärligt)
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| 4. |
- Andersson, Ronnie, 1975, et al.
(författare)
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US20150048605
- 2015
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Patent (övrigt vetenskapligt/konstnärligt)
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| 5. |
- Bärgman, Jonas, et al.
(författare)
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Quantitative Driver Behavior Modelling forActive Safety Assessment Expansion (QUADRAE)
- 2021
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In-vehicle technologies are essential for vehicle safety. This project, Quantitative Driver Behavior Modeling for Active Safety Assessment Expansion (QUADRAE), addresses two crucial components of the technology development process: driver models and simulation methodology. Together, they have provided the industrial partners with state-of-the-art tools for system development and testing, facilitating the development of innovative technologies to improve traffic safety. The main objectives of the project were to:develop and validate models of driver behavior needed in current and future simulation tools for virtual testing of active safety and automationcarry out prioritized virtual tests to estimate the safety benefit of a system, tune system parameters, and explore potential outcomes in scenarios when the system is activelearn more about the best methods for performing virtual testing using driver modelsAs a result of the project, the partners now have an established virtual simulation framework using Predictive Processing (PP) as a general paradigm for modeling driver behavior. The modeling, based on the latest knowledge and ideas about human behavior in driving, draws on extensive research using volunteer drivers as study participants. Data from both controlled experiments and naturalistic driving were used to develop and validate the models. These models are already being used by the industry partners as part of their virtual safety assessment toolchain, to develop advanced driver support systems. The data will continue to be used by the project partners in industry and academia to develop future driver models (which will, in turn, foster improved driver support systems).
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| 6. |
- 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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| 7. |
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| 8. |
- Pelliccione, Patrizio, 1975, et al.
(författare)
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Architecting cars as constituents of a system of systems
- 2016
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Ingår i: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450363990 ; , s. 1-7
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Konferensbidrag (refereegranskat)abstract
- Future transportation systems will be a heterogeneous mix of items with varying connectivity and interoperability. A mix of new technologies and legacy systems will co-exist to realize a variety of scenarios involving not only connected cars but also road infrastructures, pedestrians, cyclists, etc. Future transportation systems can be seen as a System of Systems (SoS), where each constituent system - one of the units that compose an SoS - can act as a standalone system, but the cooperation among the constituent systems enables new emerging and promising scenarios. In this paper we investigate how to architect cars so that they can be constituents of future transportation systems. This work is realized in the context of two Swedish projects coordinated by Volvo Cars and involving some universities and research centers in Sweden and many suppliers of the OEM, including Autoliv, Arccore, Combitech, Cybercom, Knowit, Prevas, ÅF-Technology, Semcom, and Qamcom.
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| 9. |
- Rasch, Alexander, 1991, et al.
(författare)
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How do oncoming traffic and cyclist lane position influence cyclist overtaking by drivers?
- 2020
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 142
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Tidskriftsartikel (refereegranskat)abstract
- Overtaking cyclists is challenging for drivers because it requires a well-timed, safe interaction between the driver, the cyclist, and the oncoming traffic. Previous research has investigated this manoeuvre in different experimental environments, including naturalistic driving, naturalistic cycling, and simulator studies. These studies highlight the significance of oncoming traffic—but did not extensively examine the influence of the cyclist’s position within the lane. In this study, we performed a test-track experiment to investigate how oncoming traffic and position of the cyclist within the lane influence overtaking. Participants overtook a robot cyclist, which was controlled to ride in two different lateral positions within the lane. At the same time, an oncoming robot vehicle was controlled to meet the participant’s vehicle with either 6 or 9 s time-to-collision. The order of scenarios was randomized over participants. We analysed safety metrics for the four different overtaking phases, reflecting drivers’ safety margins to rear-end, head-on, and side-swipe collisions, in order to investigate the two binary factors: 1) time gap between ego vehicle and oncoming vehicle, and 2) cyclist lateral position. Finally, the effects of these two factors on the safety metrics and the overtaking strategy (either flying or accelerative depending on whether the overtaking happened before or after the oncoming vehicle had passed) were analysed. The results showed that, both when the cyclist rode closer to the centre of the lane and when the time gap to the oncoming vehicle was shorter, safety margins for all potential collisions decreased. Under these conditions, drivers—particularly female drivers—preferred accelerative over flying manoeuvres. Bayesian statistics modelled these results to inform the development of active safety systems that can support drivers in safely overtaking cyclists.
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