Generating representative test scenarios: The FUSE for Representativity (fuse4rep) process model for collecting and analysing traffic observation data

Scenario-based testing is a pillar of assessing the effectiveness of automated driving systems (ADSs). For data-driven scenario-based testing, representative traffic scenarios need to describe real road traffic situations in compressed form and, as such, cover normal driving along with critical and accident situations originating from different data sources. Nevertheless, in the choice of data sources, a conflict often arises between sample quality and depth of information. Police accident data (PD) covering accident situations, for example, represent a full survey and thus have high sample quality but low depth of information. However, for local video-based traffic observation (VO) data using drones and covering normal driving and critical situations, the opposite is true. Only the fusion of both sources of data using statistical matching can yield a representative, meaningful database able to generate representative test scenarios. For successful fusion, which requires as many relevant, shared features in both data sources as possible, the following question arises: How can VO data be collected by drones and analysed to create the maximum number of relevant, shared features with PD? To answer that question, we used the Find–Unify–Synthesise–Evaluation (FUSE) for Representativity (FUSE4Rep) process model.We applied the first (“Find”) and second (“Unify”) step of this model to VO data and conducted drone-based VOs at two intersections in Dresden, Germany, to verify our results. We observed a three-way and a four-way intersection, both without traffic signals, for more than 27 h, following a fixed sample plan. To generate as many relevant information as possible, the drone pilots collected 122 variables for each observation (which we published in the ListDB Codebook) and the behavioural errors of road users, among other information. Next, we analysed the videos for traffic conflicts, which we classified according to the German accident type catalogue and matched with complementary information collected by the drone pilots. Last, we assessed the crash risk for the detected traffic conflicts using generalised extreme value (GEV) modelling. For example, accident type 211 was predicted as happening 1.3 times per year at the observed four-way intersection. The process ultimately facilitated the preparation of VO data for fusion with PD. The orientation towards traffic conflicts, the matched behavioural errors and the estimated GEV allowed creating accident-relevant scenarios. Thus, the model applied to VO data marks an important step towards realising a representative test scenario database and, in turn, safe ADSs

Use Information You Have Never Observed Together: Data Fusion as a Major Step Towards Realistic Test Scenarios

Scenario-based testing is a major pillar in the development and effectiveness assessment of automated driving systems. Thereby, test scenarios address different information layers and situations (normal driving, critical situations and accidents) by using different databases. However, the systematic combination of accident and / or normal driving databases into new synthetic databases can help to obtain scenarios that are as realistic as possible. This paper shows how statistical matching (SM) can be applied to fuse different categorical accident and traffic observation databases. Hereby, the fusion is demonstrated in two use cases, each featuring several fusion methods. In use case 1, a synthetic database was generated out of two accident data samples, whereby 78.7% of the original values could be estimated correctly by a random forest classifier. The same fusion using distance-hot-deck reproduced only 67% of the original values, but better preserved the marginal distributions. A real-world application is illustrated in use case 2, where accident data was fused with over 23,000 car trajectories at one intersection in Germany. We could show that SM is applicable to fuse categorical traffic databases. In future research, the combination of hotdeck- methods and machine learning classifiers needs to be further investigated

Videodaten in der Verkehrsforschung – besser auffind- und nachnutzbar dank der neuen Ontologie ListDB Onto

Handreichung zur Ontologie ListDB Onto als wichtiger Baustein für die Interoperabilität zwischen verschiedenen Videodaten in der Verkehrsforschung

Videodaten in der Verkehrsforschung – besser auffind- und nachnutzbar dank der neuen Ontologie ListDB Onto

Handreichung zur Ontologie ListDB Onto als wichtiger Baustein für die Interoperabilität zwischen verschiedenen Videodaten in der Verkehrsforschung

Videodaten in der Verkehrsforschung – besser auffind- und nachnutzbar dank der neuen Ontologie ListDB Onto

Handreichung zur Ontologie ListDB Onto als wichtiger Baustein für die Interoperabilität zwischen verschiedenen Videodaten in der Verkehrsforschung

The cortisol awakening response in infants: Ontogeny and associations with development-related variables

The cortisol awakening response (CAR) is a frequently used measure in psychoneuroendocrinological research, however, some of its more fundamental aspects still require attention. An important question in this respect concerns the ontogeny of the CAR. Data from two recent reports suggest that the CAR may only emerge relatively late during child development (≥16 months of age). However, as both enquiries did not use objective means of verifying participant adherence or infants’ awakening times, it is unclear whether methodological factors may have contributed to these results. Here, we report data from a study on 33 infants aged 2–12 months with close care being taken to ensure the accuracy of sampling times by using wrist actigraphy and electronic monitoring containers. Salivary cortisol levels were assessed at 0 and 30min post-awakening over three study days. Results revealed evidence for a significant CAR (≥2.5nmol/L) in 32 (out of 33) infants and on a total 86.9% of study days, with a marked magnitude of the CAR across infants (mean estimated increase=12.54nmol/L). In addition, the cortisol level on awakening and the CAR were found to be associated with different aspects of infant's physical and sleep-related development as well as with their weight and body mass index (BMI) at birth. Contrary to previous reports, the current results thus indicate that the ontogeny of the CAR occurs at an early stage of development and that it is present from as early as two months of life. The data also suggest that post-awakening cortisol secretion may undergo considerable changes during the first year of life associated with different aspects of infant development

Effect of a naturalistic prospective memory-related task on the cortisol awakening response in young children

Activation of prospective memory (PM) representations following awakening has been proposed to modulate expression of the cortisol awakening response (CAR). However, experimental testing of this hypothesis is still missing. This study examined the effect of a naturalistic PM-related manipulation on the CAR in a sample of 35 preschool-aged children. The CAR was assessed on two study days (0 and 30min post-awakening) using objective verification of awakening and sampling times. Children had to remember to perform a naturalistic PM-related task (reminding their parents about a gift) on the experimental day while there was no intervention on the control day (counterbalanced order). Results revealed an increased CAR on the experimental day (mean±SD increase: 9.97±7.05nmol/L) compared to the control day (mean±SD increase: 5.45±8.14nmol/L; p=.022). Our findings concur with the notion that expression of the CAR is modulated by post-awakening anticipatory processes involving activation of PM representations