| 1. |
- Bálint, András, 1982, et al.
(författare)
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Multitasking additional-to-driving: Prevalence, structure, and associated risk in SHRP2 naturalistic driving data
- 2020
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 137
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Tidskriftsartikel (refereegranskat)abstract
- Objective This paper 1) analyzes the extent to which drivers engage in multitasking additional-to-driving (MAD) under various conditions, 2) specifies odds ratios (ORs) of crashing associated with MAD, and 3) explores the structure of MAD. Methods Data from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) was analyzed to quantify the prevalence of MAD in normal driving as well as in safety-critical events of various severity level and compute point estimates and confidence intervals for the corresponding odds ratios estimating the risk associated with MAD compared to no task engagement. Sensitivity analysis in which secondary tasks were re-defined by grouping similar tasks was performed to investigate the extent to which ORs are affected by the specific task definitions in SHRP2. A novel visual representation of multitasking was developed to show which secondary tasks co-occur frequently and which ones do not. Results MAD occurs in 11 % of control driving segments, 22 % of crashes and near-crashes (CNC), 26 % of Level 1–3 crashes and 39 % of rear-end striking crashes, and 9 %, 16 %, 17 % and 28 % respectively for the same event types if MAD is defined in terms of general task groups. The most common co-occurrences of secondary tasks vary substantially among event types; for example, “Passenger in adjacent seat – interaction” and “Other non-specific internal eye glance” tend to co-occur in CNC but tend not to co-occur in control driving segments. The odds ratios of MAD using SHRP2 task definitions compared to driving without any secondary task and the corresponding 95 % confidence intervals are 2.38 (2.17–2.61) for CNC, 3.72 (3.11–4.45) for Level 1–3 crashes and 8.48 (5.11–14.07) for rear-end striking crashes. The corresponding ORs using general task groups to define MAD are slightly lower at 2.00 (1.80–2.21) for CNC, 3.03 (2.48–3.69) for Level 1–3 crashes and 6.94 (4.04–11.94) for rear-end striking crashes. Conclusions The number of secondary tasks that the drivers were engaged in differs substantially for different event types. A graphical representation was presented that allows mapping task prevalence and co-occurrence within an event type as well as a comparison between different event types. The ORs of MAD indicate an elevated risk for all safety-critical events, with the greatest increase in the risk of rear-end striking crashes. The results are similar independently of whether secondary tasks are defined according to SHRP2 or general task groups. The results confirm that the reduction of driving performance from MAD observed in simulator studies is manifested in real-world crashes as well.
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| 2. |
- Imberg, Henrik, 1991, et al.
(författare)
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Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples
- 2022
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets and measurement-constrained experiments. However, traditional subsampling methods often suffer from the lack of information available at the design stage. We propose an active sampling strategy that iterates between estimation and data collection with optimal subsamples, guided by machine learning predictions on yet unseen data. The method is illustrated on virtual simulation-based safety assessment of advanced driver assistance systems. Substantial performance improvements were observed compared to traditional sampling methods.
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| 3. |
- Kolk, Harald, et al.
(författare)
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Overall SAFE-UP impact (Deliverable 5.6)
- 2023
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Rapport (övrigt vetenskapligt/konstnärligt)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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| 4. |
- Kovaceva, Jordanka, 1980, et al.
(författare)
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Impact assessment methodology update (Deliverable 5.8)
- 2023
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Rapport (övrigt vetenskapligt/konstnärligt)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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| 5. |
- Mensa, Genis, et al.
(författare)
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Passive safety system assessment results (Deliverable 5.4)
- 2023
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In order to determine the SAFE-UP passive safety systems effectiveness, representative test cases for the relevant accident scenarios were tested in both virtual and physical environments. In this deliverable, occupant monitoring activities, simulations with VIVA+ on different seating positions and restraint systems, a safety benefit analysis on passive safety systems and sled test activities with a THOR-reclined are included.
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| 6. |
- Parera, Nuria, et al.
(författare)
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Active safety system assessment result (Deliverable 5.3)
- 2023
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This deliverable presents the main testing results carried out in WP5 to validate and assess the active safety systems technologies developed in the SAFE-UP project. The technologies are implemented in three car demonstrators.
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| 7. |
- Pipkorn, Linda, 1991, et al.
(författare)
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Driver response to take-over requests in real traffic
- 2023
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Ingår i: IEEE Transactions on Human-Machine Systems. - 2168-2291 .- 2168-2305. ; 53:5, s. 823-833
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Tidskriftsartikel (refereegranskat)abstract
- Existing research on control-transitions from automated driving (AD) to manual driving mainly stems from studies in virtual settings. There is a need for studies conducted in real settings to better understand the impacts of increasing vehicle automation on traffic safety. This study aims specifically to understand how drivers respond to take-over requests (TORs) in real traffic by investigating the associations between 1) where drivers look when receiving the TOR, 2) repeated exposure to TORs, and 3) the drivers’ response process. In total, thirty participants were exposed to four TORs after about 5–6 min of driving with AD on public roads. While in AD, participants could choose to engage in non-driving-related tasks (NDRTs).When they received the TOR, for 38% of TORs, participants were already looking on path. For those TORs where drivers looked off path at the time of the TOR, the off-path glance was most commonly towards an NDRT item. Then, for 72% of TORs (independent on gaze direction), drivers started their response process to the TOR by looking towards the instrument cluster before placing their hands on the steering wheel and their foot on the accelerator pedal, and deactivating automation. Both timing and order of these actions varied among participants, but all participants deactivated AD within 10 s from the TOR. The drivers’ gaze direction at the TOR had a stronger association with the response process than the repeated exposure to TORs did. Drivers can respond to TORs in real traffic. However, the response should be considered as a sequence of actions that requires a certain amount of time.
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| 8. |
- Schindler, Ron, 1991, et al.
(författare)
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Making a few talk for the many – Modeling driver behavior using synthetic populations generated from experimental data
- 2021
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 162
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Tidskriftsartikel (refereegranskat)abstract
- Understanding driver behavior is the basis for the development of many advanced driver assistance systems, and experimental studies are indispensable tools for constructing appropriate driver models. However, the high cost associated with testing is a serious obstacle in collecting large amounts of experimental data. This paper presents a methodology that can improve the reliability of results from experimental studies with a limited number of participants by creating a virtual population. Specifically, a methodology based on Bayesian inference has been developed, that generates synthetic cases that adhere to various real-world constraints and represent possible variations of the observed experimental data. The application of the framework is illustrated using data collected during a test-track experiment where truck drivers performed a right turn maneuver, with and without a cyclist crossing the intersection. The results show that, based on the speed profiles of the dataset and physical constraints, the methodology can produce synthetic speed profiles during braking that mimic the original curves but extend to other realistic braking patterns that were not directly observed. The models obtained from the proposed methodology have applications for the design of active safety systems and automated driving demonstrating thereby that the developed framework has great promise for the automotive industry.
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| 9. |
- Wu, Jian, et al.
(författare)
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Modeling Lead-Vehicle Kinematics for Rear-End Crash Scenario Generation
- 2024
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- The use of virtual safety assessment as the primary method for evaluating vehicle safety technologies has emphasized the importance of crash scenario generation. One of the most common crash types is the rear-end crash, which involves a lead vehicle and a following vehicle. Most studies have focused on the following vehicle, assuming that the lead vehicle maintains a constant acceleration/deceleration before the crash. However, there is no evidence for this premise in the literature. This study aims to address this knowledge gap by thoroughly analyzing and modeling the lead vehicle’s behavior as a first step in generating rear-end crash scenarios. Accordingly, the study employed a piecewise linear model to parameterize the speed profiles of lead vehicles, utilizing two rear-end pre-crash/near-crash datasets. These datasets were merged and categorized into multiple sub-datasets; for each one, a multivariate distribution was constructed to represent the corresponding parameters. Subsequently, a synthetic dataset was generated using these distribution models and validated by comparison with the original combined dataset. The results highlight diverse lead-vehicle speed patterns, indicating that a more accurate model, such as the proposed piecewise linear model, is required instead of the conventional constant acceleration/deceleration model. Crashes generated with the proposed models accurately match crash data across the full severity range, surpassing existing lead-vehicle kinematics models in both severity range and accuracy. By providing more realistic speed profiles for the lead vehicle, the model developed in the study contributes to creating realistic rear-end crash scenarios and reconstructing real-life crashes.
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