| 11. |
- Imberg, Henrik, 1991, et al.
(författare)
-
Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples
- 2022
-
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.
|
|
| 12. |
- Kolk, Harald, et al.
(författare)
-
Overall SAFE-UP impact (Deliverable 5.6)
- 2023
-
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.
|
|
| 13. |
- Kovaceva, Jordanka, 1980, et al.
(författare)
-
Impact assessment methodology update (Deliverable 5.8)
- 2023
-
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.
|
|
| 14. |
|
|
| 15. |
|
|
| 16. |
- Mensa, Genis, et al.
(författare)
-
Passive safety system assessment results (Deliverable 5.4)
- 2023
-
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.
|
|
| 17. |
- Parera, Nuria, et al.
(författare)
-
Active safety system assessment result (Deliverable 5.3)
- 2023
-
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.
|
|
| 18. |
- Pipkorn, Linda, 1991, et al.
(författare)
-
Driver response to take-over requests in real traffic
- 2023
-
Ingår i: IEEE Transactions on Human-Machine Systems. - 2168-2291 .- 2168-2305. ; 53:5, s. 823-833
-
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.
|
|
| 19. |
- Rasch, Alexander, 1991, et al.
(författare)
-
When is it Safe to Complete an Overtaking Maneuver? Modeling Drivers' Decision to Return After Passing a Cyclist
- 2024
-
Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016.
-
Tidskriftsartikel (refereegranskat)abstract
- For cyclists, being overtaken represents a safety risk of possibly being side-swiped or cut in by overtaking drivers. For drivers, such maneuvers are challenging—not only do they need to decide when to initiate the maneuver, but they also need to time their return well to complete the maneuver. In the presence of oncoming traffic, the problem of completing an overtaking maneuver extends to balancing head-on with side-swipe collision risks. Active safety systems such as blind-spot or forward-collision warning systems, or, more recently, automated driving features, may assist drivers in avoiding such collisions and completing the maneuver successfully. However, such systems must interact carefully with the driver and prevent false-positive alerts that reduce the driver’s trust in the system. In this study, we developed a driver-behavior model of the drivers’ return onset in cyclist-overtaking maneuvers that could improve such a safety system. To provide cumulative evidence about driver behavior, we used data from two different sources: test track and naturalistic driving. We developed Bayesian survival models for the two datasets that can predict the probability of a driver returning, given time-varying inputs about the current situation. We evaluated the models in an in-sample and out-of-sample evaluation. Both models showed that drivers use the displacement of the cyclist to time their return decision, which is accelerated if an oncoming vehicle is present and close. We discuss how the models could be integrated into an active-safety system to improve driver acceptance.
|
|
| 20. |
- Schindler, Ron, 1991, et al.
(författare)
-
Making a few talk for the many – Modeling driver behavior using synthetic populations generated from experimental data
- 2021
-
Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 162
-
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.
|
|
