| 1. |
- Amin, Khabat, et al.
(author)
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Injury Reducing Effect of GSHP-Heated Pedestrian Paths
- 2024
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In: International Ground Source Heat Pump Association-Research Conference. ; , s. 227-235
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Conference paper (peer-reviewed)abstract
- In Sweden, falls amongst pedestrians during wintertime, due to slipping on ice and snow, is a costly and growing problem. Using data on pedestrian falls from four Swedish cities, the injury-reducing effect of heated surfaces was studied. The results indicate that heated surfaces have a significant injury-reducing effect especially in cities with more ice and snow. Currently, district heating is used as a heat source and at an increasing cost. By using GSHP systems as a heat source, the cost could be considerably lowered, and in this way secure the further use and expansion of heated pedestrian paths.
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| 2. |
- Díaz Fernández, P., et al.
(author)
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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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In: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 168
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Journal article (peer-reviewed)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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| 3. |
- Dozza, Marco, 1978, et al.
(author)
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Modelling Interaction between Cyclists and Automobiles - Final Report
- 2020
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Reports (other academic/artistic)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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| 4. |
- Kolk, Harald, et al.
(author)
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Overall SAFE-UP impact (Deliverable 5.6)
- 2023
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Reports (other academic/artistic)abstract
- This deliverable shows the effectiveness of the SAFE-UP technologies with respect to the scenarios in which the technologies are being assessed, the larger categories of accident type (e.g., car-to-pedestrian crashes), and all fatalities or killed or severely injured in road traffic within the EU. It was found that, when adding an in-lane evasion functionality to a generic AEB and V2X communication to increase the vehicle sensing capabilities, an additional 8 to 16% of killed or severely injured pedestrians or cyclists can be avoided in scenarios where the VRU crosses the street, and 5 to 16% of the fatalities for cyclist crossing scenarios, even though the AEB is already very effective and avoids the majority of cases. Furthermore, it was shown that using the improved restraint systems developed in SAFE-UP and including an AEB in reclined sitting positions does not increase the injury risk in comparison to state-of-the-art restraint systems without AEB, thus allowing passengers to assume the reclined sitting position.
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| 5. |
- Kovaceva, Jordanka, 1980, et al.
(author)
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A comparison of computational driver models using naturalistic and test-track data from cyclist-overtaking manoeuvres
- 2020
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In: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 75, s. 87-105
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Journal article (peer-reviewed)abstract
- The improvement of advanced driver assistance systems (ADAS) and their safety assessment rely on the understanding of scenario-dependent driving behaviours, such as steering to avoid collisions. This study compares driver models that predict when a driver starts steering away to overtake a cyclist on rural roads. The comparison is among four models: a threshold model, an accumulator model, and two models inspired by a proportional-integral and proportional-integral-derivative controller. These models were tested and cross-applied using two different datasets: one from a naturalistic driving (ND) study and one from a test-track (TT) experiment. Two perceptual variables, expansion rate (the horizontal angular expansion rate of the image of the lead road user on the driver’s retina) and inverse tau (the ratio between the image’s expansion rate and its horizontal optical size), were tested as input to the models. A linear cost function is proposed that can obtain the optimal parameters of the models by computationally efficient linear programming. The results show that the models based on inverse tau fitted the data better than the models that included expansion rate. In general, the models fitted the ND data reasonably well, but not as well the TT data. For the ND data, the models including an accumulative component outperformed the threshold model. For the TT data, due to the poorer fit of the models, more analysis is required to determine the merit of the models. The models fitted to TT data captured the overall pattern of steering onsets in the ND data rather well, but with a persistent bias, probably due to the drivers employing a more cautious strategy in TT. The models compared in this paper may support the virtual safety assessment of ADAS so that driver behaviour may be considered in the design and evaluation of new safety systems.
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| 6. |
- Kovaceva, Jordanka, 1980, et al.
(author)
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Identification of aggressive driving from naturalistic data in car-following situations
- 2020
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In: Journal of Safety Research. - : Elsevier BV. - 0022-4375. ; 73, s. 225-234
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Journal article (peer-reviewed)abstract
- Introduction: Aggressive driving has been associated as one of the causes for crashes, sometimes with very serious consequences. The objective of this study is to investigate the possibility of identifying aggressive driving in car-following situations on motorways by simple jerk metrics derived from naturalistic data. Method: We investigate two jerk metrics, one for large positive jerk and the other for large negative jerk, when drivers are operating the gas and brake pedal, respectively. Results: The results obtained from naturalistic data from five countries in Europe show that the drivers from different countries have a significantly different number of large positive and large negative jerks. Male drivers operate the vehicle with significantly larger number of negative jerks compared to female drivers. The validation of the jerk metrics in identifying aggressive driving is performed by tailgating (following a leading vehicle in a close proximity) and by a violator/non-violator categorization derived from self-reported questionnaires. Our study shows that the identification of aggressive driving could be reinforced by the number of large negative jerks, given that the drivers are tailgating, or by the number of large positive jerks, given that the drivers are categorized as violators. Practical applications: The possibility of understanding, classifying, and quantifying aggressive driving behavior and driving styles with higher risk for accidents can be used for the development of driver support and coaching programs that promote driver safety and are enabled by the vast collection of driving data from modern in-vehicle monitoring and smartphone technology.
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| 7. |
- Kovaceva, Jordanka, 1980, et al.
(author)
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Impact assessment methodology update (Deliverable 5.8)
- 2023
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Reports (other academic/artistic)abstract
- This deliverable is describing the final methodology for safety benefit assessment in the SAFE-UP project. The assessment method for each safety system (Demo 1-4) highly depends on the developed systems and their ability to be assessed virtually and/or physically. When possible, combinations of both approaches are considered.
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| 8. |
- Kovaceva, Jordanka, 1980
(author)
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Methods and models for safety benefit assessment of advanced driver assistance systems in car-to-cyclist conflicts
- 2022
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Doctoral thesis (other academic/artistic)abstract
- To help drivers avoid or mitigate the severity of crashes, advanced driver assistance systems (ADAS) can be designed to provide warnings or interventions. Prospective safety assessment of ADAS is important to quantify and optimise their safety benefit. Such safety assessment methods include, for example, virtual simulations and test-track testing. Today, there are many components of virtual safety assessment simulations with models or methods that are missing or can be substantially improved. This is particularly true for simulations assessing ADASs that address crashes involving cyclists—a crash type that is not decreasing at the same rate as the overall number of road crashes in Europe. The specific methodological gaps that this work addresses are: a) computational driver models for car-to-cyclist overtaking, b) algorithms for model fitting and efficient calculation of ADAS intervention time, and c) a method for merging data from different data sources into the safety assessment. Specifically, for a), different driver models for everyday driver behaviour while overtaking cyclists in a naturalistic driving setting were derived and compared. For b), computationally efficient algorithms to fit driver models to data and compute ADAS intervention time were developed for different types of vehicle models. The algorithms can be included in ADAS both for offline use in virtual assessment simulations and online real-time use in in-vehicle ADAS. Lastly, for c), a method was developed that uses Bayesian statistics to combine results from different data sources, e.g., simulations and test-track data, for ADAS safety benefit assessment. In addition to presenting five peer-reviewed scientific publications, which address these issues, this compilation thesis discusses the use of different data sources; introduces the fundamentals of Bayesian inference, linear programming, and numerical root-finding algorithms; and provides the rationale for methodological choices made, where relevant. Finally, this thesis describes the relationships among the publications and places them into context with existing literature. This work developed driver models for the virtual simulations and methods for the reliable estimation of the prospective safety benefit, which together have the potential to improve the design and the evaluation of ADAS in general, and ADAS for the car-to-cyclist overtaking scenario in particular.
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| 9. |
- Kovaceva, Jordanka, 1980, et al.
(author)
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On the evaluation of visual nudges to promote safe cycling: Can we encourage lower speeds at intersections?
- 2022
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In: Traffic Injury Prevention. - : Informa UK Limited. - 1538-957X .- 1538-9588. ; 23
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Journal article (peer-reviewed)abstract
- Crashes between cars and cyclists at urban intersections are common, and their consequences are often severe. Typical causes for this type of crashes included the excessive speed of the cyclist as well as car drivers failing to see the cyclist. Measures that decrease the cyclists’ speed may lead to safer car-cyclist interactions. This study aimed to investigate the extent to which cyclists may approach intersections at a lower speed when nudged to do so. Visual flat-stripe nudges were placed on bicycle lanes in the proximity of uncontrolled intersections (with a history of car-cyclist crashes) in two locations in Gothenburg, Sweden. This specific nudge was the one obtaining the best results from a previous study that tested different nudges in controlled experiments. Video data from the intersections were recorded with a site-based video recording system both before (baseline), and after (treatment), the nudge was installed. The video data was processed to extract trajectory and speed for cyclists. The baseline and treatment periods were equivalent in terms of day of the week, light, and weather conditions. Furthermore, two treatment periods were recorded to capture the effect of the nudge over time in one of the locations. Leisure cyclists showed lower speeds in treatment than in baseline for both locations. Commuters were less affected by the nudge than leisure cyclists. This study shows that visual nudges to decrease cyclist speed at intersections are hard to evaluate in the wild because of the many confounders. We also found that the effect of visual nudges may be smaller than the effect of environmental factors such as wind and demographics, making their evaluation even harder. The observed effect of speed might not be very high, but the advantage both in terms of cyclist acceptance and monetary cost makes an investment in the measure very low risk. This study informs policymakers and road authorities that want to promote countermeasures to intersection crashes and improve the safety of cyclists at urban intersections.
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| 10. |
- Kovaceva, Jordanka, 1980, et al.
(author)
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On the importance of driver models for the development and assessment of active safety: A new collision warning system to make overtaking cyclists safer
- 2022
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In: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 165
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Journal article (peer-reviewed)abstract
- The total number of road crashes in Europe is decreasing, but the number of crashes involving cyclists is not decreasing at the same rate. When cars and bicycles share the same lane, cars typically need to overtake them, creating dangerous conflicts—especially on rural roads, where cars travel much faster than cyclists. In order to protect cyclists, advanced driver assistance systems (ADAS) are being developed and introduced to the market. One of them is a forward collision warning (FCW) system that helps prevent rear-end crashes by identifying and alerting drivers of threats ahead. The objective of this study is to assess the relative safety benefit of a behaviour-based (BB) FCW system that protects cyclists in a car–to–cyclist overtaking scenario. Virtual safety assessments were performed on crashes derived from naturalistic driving data. A series of driver response models was used to simulate different driver reactions to the warning. Crash frequency in conjunction with an injury risk model was used to estimate the risk of cyclist injury and fatality. The virtual safety assessment estimated that, compared to no FCW, the BB FCW could reduce cyclists’ fatalities by 53–96% and serious injuries by 43–94%, depending on the driver response model. The shorter the driver’s reaction time and the larger the driver’s deceleration, the greater the benefits of the FCW. The BB FCW also proved to be more effective than a reference FCW based on the Euro NCAP standard test protocol. The findings of this study demonstrate the BB FCW’s great potential to avoid crashes and reduce injuries in car–to–cyclist overtaking scenarios, even when the driver response model did not exceed a comfortable rate of deceleration. The results suggest that a driver behaviour model integrated into ADAS collision threat algorithms can provide substantial safety benefits.
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