Anomalieerkennung in Straßenverkehrsdaten einer urbanen Kreuzung

In der Verkehrswissenschaft wird angestrebt, die Anzahl von Verkehrsunfällen zu reduzieren, indem das Verhalten der Verkehrsteilnehmer untersucht wird. Da angenommen wird, dass außergewöhnliche, anomale Situationen im Straßenverkehr das Unfallrisiko erhöhen, sollten diese vorrangig analysiert werden. Zur Detektion solcher Situationen können Verfahren der Anomalieerkennung angewandt werden. Daher wird mit dieser Arbeit der Forschungsfrage nachgegangen, welche Anomalien in welchem Umfang in einem Straßenverkehrsdatensatz einer urbanen Kreuzung vorliegen. Im zu untersuchenden Datensatz von 430.000 Trajektorien werden besonders die Positionsdaten der als PKW klassifizierten Linksabbieger untersucht, da hier aufgrund der Interaktion mit den entgegenkommenden Fahrzeugen die meisten anomalen Situationen erwartet werden. Die in diesen Daten erfassten Anomalien werden nach deren Ursache in fehlerhafte und außergewöhnliche Daten unterschieden. In dieser Arbeit wird sich auf die Erkennung von Anomalien, die auf außergewöhnlichen Daten basieren, konzentriert. Damit wird das Verhalten von Verkehrsteilnehmern analysiert, anstatt mit der Analyse von fehlerhaften Daten die Qualität des Datensatzes zu bewerten. Neben der Einteilung von Anomalien nach deren Ursache, erfolgt auch eine Unterscheidung nach deren Art in distanz-, dichte- und richtungsbasierte Anomalien. Für die Erkennung außergewöhnlicher Daten werden fünf Verfahren angewandt, mit denen alle Arten von Anomalien detektiert werden können. Eines der Verfahren basiert auf der Hausdorff-Metrik, drei davon auf der Repräsentation der Daten in einem diskreten euklidischen Raum und das fünfte auf der Anwendung von Gaußschen Mischmodellen. Mit der Hausdorff-Metrik werden vor allem Anomalien identifiziert, bei denen die Position eines Verkehrsteilnehmers deutlich von der erwarteten Route abweicht. Mit der Repräsentation der Daten in einem diskreten euklidischen Raum kann die Anzahl der Trajektorien pro Zelle ermittelt werden, sodass Trajektorien danach bewertet werden können, wie viele andere Trajektorien an deren Position aufgezeichnet werden. Für die Anomalieerkennung in den Werten der Positionsänderung eines Verkehrsteilnehmers, stellt die gemessene Bewegungsrichtung eine bessere Datengrundlage dar als die Übergangswahrscheinlichkeiten zwischen den Zellen. Im Vergleich zu den zuvor genannten Verfahren werden mit den Gaußschen Mischmodellen auch Fahrzeuge als Anomalie klassifiziert, die im Stehen von den Kameras erfasst werden. Aus den Ergebnissen wird geschlussfolgert, dass die angewandten Verfahren es dem Anwender ermöglichen, Situationen die nach verschiedenen Kriterien als außergewöhnlich gelten, aus einem Datensatz zu ermitteln. Die Anzahl der Anomalien in einem Datensatz hängt vom Anwender der Anomalieerkennung ab, der den Schwellenwert festlegt, ab dem eine Trajektorie als anomal zu klassifizieren ist

Detection and Analysis of Critical Interactions in Illegal U-turns at an Urban Intersection

Before Advanced Driver Assistance Systems (ADAS) can guide vehicles through real-world traffic, it has to be ensured that they will operate reliably in normal, but particularly in rare and critical situations such as traffic conflicts or near misses under all circumstances and conditions. To test the ADAS functions in rare critical situations, this study aims to gather knowledge about such situations i.e. detect them at an urban intersection, analyze the road user behavior and describe relevant kinematic patterns based on an aggregated long-term analysis. To limit the number of possible situations, we focus on interactions between illegally U-turning motorized road users (MRU) and vulnerable road users (VRU). Since trajectory and video data of traffic violations are rare, the relevant trajectories of MRUs and VRUs need to be identified first. Therefore, virtual loops are employed, which are placed at the expected starts and ends of the trajectories. All trajectories that intersect both, the start and end loop, are extracted from the dataset. Then, the resulting trajectories have to be evaluated regarding driving paths, interaction, and criticality. For this purpose, the surrogate measure of safety "post encroachment time" (PET) is applied. Afterward, available scene videos are used to evaluate the PET-triggered situations as critical or uncritical encounters. Finally, descriptive and inferential statistical methods are applied to kinematic data of those trajectory pairs to identify relevant behavioral patterns of the road users. The examined dataset was recorded at the Application Platform for Intelligent Mobility Research Intersection of the German Aerospace Center in Brunswick, Germany. Applying the beforementioned methodology to the dataset yielded the detection of relevant interactions. The kinematic patterns of the interactions that were assessed as critical close encounters were further analyzed to derive situational patterns. Based on this analysis it can be shown that the reason for critical situations was that the U-turning MRU had to leave the intersection. Thus, we can validate that the road safety for vehicles leaving the intersection in an unallowed direction can become critical. To understand these situations in detail they are described in the following. The U-turning MRUs use the lane of the left turning vehicle and have to let the oncoming traffic pass before they can execute their turning maneuver. While the median U-turn curve radius is 7.6 m other curve radii vary between 2.8 and 22.3 m. Some U-turning vehicles that enter the intersection during the red phase of the VRU are waiting so long for the oncoming vehicles to pass that the traffic light for the VRUs is already switching to green when the U-turning vehicle leaves the intersection. Based on the PET-triggered situations and their video scenes we could identify and evaluate critical U-turn situations. Our analysis showed, that these situations occur when the vehicles had to wait a long time at the intersection and had to leave it at a time when the traffic lights gave the right of way to the VRUs that were crossing the lane. In a conclusion, tailored preventive measures such as vehicle-to-infrastructure communication could reduce criticality in such U-turn situations because the vehicles would then be aware of the traffic light state. The research leading to these results is funded by the German Federal Ministry for Economic Affairs and Climate Action within the project Methoden und Maßnahmen zur Absicherung von KI basierten Wahrnehmungsfunktionen für das automatisierte Fahren (KI-Absicherung). The authors would like to thank the consortium for the successful cooperation

Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving

Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users\u27 movement and behavior patterns. Based on these predictions - and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users - the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy

Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving

Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns. Based on these predictions — and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users — the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy