Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Reality

One core challenge in the development of automated vehicles is their capability to deal with a multitude of complex trafficscenarios with many, hard to predict traffic participants. As part of the iterative development process, it is necessary to detect criticalscenarios and generate knowledge from them to improve the highly automated driving (HAD) function. In order to tackle this challenge,numerous datasets have been released in the past years, which act as the basis for the development and testing of such algorithms.Nevertheless, the remaining challenges are to find relevant scenes, such as safety-critical corner cases, in these datasets and tounderstand them completely.Therefore, this paper presents a methodology to process and analyze naturalistic motion datasets in two ways: On the one hand, ourapproach maps scenes of the datasets to a generic semantic scene graph which allows for a high-level and objective analysis. Here,arbitrary criticality measures, e.g. TTC, RSS or SFF, can be set to automatically detect critical scenarios between traffic participants.On the other hand, the scenarios are recreated in a realistic virtual reality (VR) environment, which allows for a subjective close-upanalysis from multiple, interactive perspectives.Comment: Accepted for publication at ICITE 202

SHOW Deliverable 10.1: Simulation scenarios and tools

This document identifies all simulation tools which are used by all partners participating in Work Package 10 of the SHOW project. Their applications range from vehicle level of shared CCAVs up to mobility level, and they are used to enrich all field experiment results of the SHOW pilots. In addition, a relation of tools to application areas and to SHOW pilots is presented. Furthermore, multiple simulation scenarios are introduced, which define the used tools to evaluate the scenario, their expected results as well as the addressed KPIs from A9.4. After a short presentation of the SHOW sites that are investigated in simulation in this WP, the simulation plans of all participating partners are presented and linked to at least one of the pilot sites. Additionally, data inputs that are required from the SHOW sites are stated

SHOW Deliverable 10.2:Pilot guiding simulation results

This deliverable describes the first pool of simulation results covering nine representative pilot sites of SHOW. The initial aim of this deliverable was to provide the first pool of simulation results fed by pre-demo utilizing input from the pre-demo evaluation round and to revise the data inputs required from SHOW sites during real-life demo activities. Since output data from the pre-demo phase is not yet available, a modified approach was used for the simulations. On the one hand, special attention was paid to ensuring that the simulations were very closely coordinated with the pre-demos. This was achieved by having the same partners from the simulations involved in the pre-demos in all nine selected sites, avoiding communication risks between pre-demos and simulations. Secondly, the exact same data that functioned as input for the pre-demos was used as input for the simulations. These are e.g. HD maps of the surveyed target routes or existing data of the target vehicles from other projects. This enabled very realistic simulations to be achieved, although the output of the pre-demos is still pending and will be incorporated into the follow-up deliverable. From the simulation results for the nine sites (Aachen, Brainport, Graz, Karlsruhe, Linkoping, Madrid, Salzburg, Tampere and Trikala), it was found that the introduction of automated shuttles leads to an increase in delay times due to slow shuttle speeds (e.g. in Trikala, Linköping, Karlsruhe, Brainport), while safety aspects should be taken into account, as shown in Karlsruhe and Tampere. Furthermore, passenger behaviour and comfort are of great importance for the successful introduction of automated services, which was first observed in Graz. However, in Madrid, Trikala and Salzburg it was shown that increasing the penetration rate and the area of operation of the automated shuttle leads to a reduction in delays and travel time as well as an increase in speed. The results presented provide a very good basis for the further expansion of the simulations in the subsequent deliverables and to derive the impact assessment important in WP13. Output from the pre-demos will be added to the simulations in the next iteration that is reported within D10.3 in 2022