Multi-Object Tracking with Interacting Vehicles and Road Map Information

In many applications, tracking of multiple objects is crucial for a perception of the current environment. Most of the present multi-object tracking algorithms assume that objects move independently regarding other dynamic objects as well as the static environment. Since in many traffic situations objects interact with each other and in addition there are restrictions due to drivable areas, the assumption of an independent object motion is not fulfilled. This paper proposes an approach adapting a multi-object tracking system to model interaction between vehicles, and the current road geometry. Therefore, the prediction step of a Labeled Multi-Bernoulli filter is extended to facilitate modeling interaction between objects using the Intelligent Driver Model. Furthermore, to consider road map information, an approximation of a highly precise road map is used. The results show that in scenarios where the assumption of a standard motion model is violated, the tracking system adapted with the proposed method achieves higher accuracy and robustness in its track estimations

Environment Perception Framework Fusing Multi-Object Tracking, Dynamic Occupancy Grid Maps and Digital Maps

Autonomously driving vehicles require a complete and robust perception of the local environment. A main challenge is to perceive any other road users, where multi-object tracking or occupancy grid maps are commonly used. The presented approach combines both methods to compensate false positives and receive a complementary environment perception. Therefore, an environment perception framework is introduced that defines a common representation, extracts objects from a dynamic occupancy grid map and fuses them with tracks of a Labeled Multi-Bernoulli filter. Finally, a confidence value is developed, that validates object estimates using different constraints regarding physical possibilities, method specific characteristics and contextual information from a digital map. Experimental results with real world data highlight the robustness and significance of the presented fusing approach, utilizing the confidence value in rural and urban scenarios

Systematische Untersuchung von Radar Tracking-Algorithmen

In modernen Fahrzeugen werden vermehrt Fahrerassistenzsysteme zur Steigerung der Sicherheit und des Komforts eingesetzt. In Zukunft werden als Folge des technologischen Fortschritts die Anzahl und Komplexität dieser Systeme weiter zunehmen. Für das Testen und die Freigabe hochautomatisierter Fahrfunktionen besteht die Forderung, neue Qualitätsstandards und Methoden zu entwickeln. Zu diesem Zweck haben sich 17 Projektpartner aus der deutschen Automobilindustrie zu dem Verbundprojekt “Projekt zur Etablierung von generell akzeptierten Gütekriterien, Werkzeugen und Methoden sowie Szenarien und Situationen (PEGASUS) zusammengeschlossen. Das Ziel des Projekts ist die Entwicklung eines einheitlichen Vorgehens im Bereich Test und Erprobung. Darüber hinaus wird europaweit im “ENABLE-S3“-Projekt an bereichsübergreifenden virtuellen Plattformen für die Validierung und Verifizierung hochautomatisierter Funktionen geforscht. Der Gültigkeitsbereich dieser Plattformen erstreckt sich von der Luftfahrt, über die Automobilindustrie, die Landwirtschaft, das Gesundheitswesen bis zur Schifffahrt und die Bahnindustrie. Das Fachgebiet Fahrzeugtechnik Darmstadt (FZD) ist an beiden Projekten beteiligt. Für das PEGASUS-Projekt werden Validierungsmethoden von Simulationsmodellen für aktive Sensoren entwickelt. Im Zuge dessen wird das Modell eines Radarsensors erarbeitet, das eine hohe Modellgüte aufweist. Für die Verwendung der simulierten Radarsignale in nachgelagerten Fahrerassistenzsystemen ist die Detektion und zeitliche Verfolgung von statischen und dynamischen Objekten innerhalb der Signale erforderlich. Dies gelingt mithilfe sogenannter Tracking-Algorithmen. Zurzeit steht dem Fachgebiet FZD kein solcher Algorithmus zur Verfügung. In dieser Thesis wird daher die Analyse und Entwicklung eines Tracking-Algorithmus beschrieben. Für die Implementierung wird zunächst eine Analyse des aktuellen Stands der Entwicklungen von Tracking-Verfahren durchgeführt. Im Anschluss wird ein Open Source Tracking-Algorithmus implementiert, der eine Identifizierung und Untersuchung seiner Schlüsselparameter zulässt. Für die Verwendung des Tracking-Algorithmus mit Signalen eines realen Radarsensors wird eine Schnittstelle zwischen beiden Bausteinen definiert. Um einen belastbaren Vergleich der Leistungsfähigkeit verschiedener Tracking-Algorithmen zu erhalten, wird eine Analyse bekannter Kriterien und Metriken durchgeführt. Mithilfe dieser Methoden werden die Einflüsse von Variationen der Schlüsselparameter auf die Qualität der Tracking-Ergebnisse untersucht. Gleichzeit werden Konzepte zur Erstellung von Testfällen für Tracking-Algorithmen dargelegt. Zusammen mit den Metriken wird damit eine Bewertungsgrundlage für die Qualität von Tracking-Ergebnissen präsentiert. Die Untersuchungen des implementierten Algorithmus zeigen, dass dieser die geforderten Funktionen eines Tracking-Verfahrens erfüllt. Durch die Auswertung der Tracking-Ergebnisse bei Variation der Schlüsselparameter werden die Einflüsse einzelner Parameter auf die Tracking-Qualität deutlich. Während dieser Analyse zeigt sich, dass die gewählten Metriken und Testfälle eine systematische Untersuchung der Tracking-Ergebnisse zulassen. Sie sind folglich auch für zukünftige Einschätzungen der Tracking-Qualität weiterer Algorithmen einsetzbar

A Comprehensive Mapping and Real-World Evaluation of Multi-Object Tracking on Automated Vehicles

Multi-Object Tracking (MOT) is a field critical to Automated Vehicle (AV) perception systems. However, it is large, complex, spans research fields, and lacks resources for integration with real sensors and implementation on AVs. Factors such those make it difficult for new researchers and practitioners to enter the field. This thesis presents two main contributions: 1) a comprehensive mapping for the field of Multi-Object Trackers (MOTs) with a specific focus towards Automated Vehicles (AVs) and 2) a real-world evaluation of an MOT developed and tuned using COTS (Commercial Off-The-Shelf) software toolsets. The first contribution aims to give a comprehensive overview of MOTs and various MOT subfields for AVs that have not been presented as wholistically in other papers. The second contribution aims to illustrate some of the benefits of using a COTS MOT toolset and some of the difficulties associated with using real-world data. This MOT performed accurate state estimation of a target vehicle through the tracking and fusion of data from a radar and vision sensor using a Central-Level Track Processing approach and a Global Nearest Neighbors assignment algorithm. It had an 0.44 m positional Root Mean Squared Error (RMSE) over a 40 m approach test. It is the authors\u27 hope that this work provides an overview of the MOT field that will help new researchers and practitioners enter the field. Additionally, the author hopes that the evaluation section illustrates some difficulties of using real-world data and provides a good pathway for developing and deploying MOTs from software toolsets to Automated Vehicles

A variational approach to simultaneous multi-object tracking and classification

Object tracking and classification serve as basic components for the different perception tasks of autonomous robots. They provide the robot with the capability of class-aware tracking and richer features for decision-making processes. The joint estimation of class assignments, dynamic states and data associations results in a computationally intractable problem. Therefore, the vast majority of the literature tackles tracking and classification independently. The work presented here proposes a probabilistic model and an inference procedure that render the problem tractable through a structured variational approximation. The framework presented is very generic, and can be used for various tracking applications. It can handle objects with different dynamics, such as cars and pedestrians and it can seamlessly integrate multi-modal features, for example object dynamics and appearance. The method is evaluated and compared with state-of-the-art techniques using the publicly available KITTI dataset

Multitarget tracking and terrain-aided navigation using square-root consider filters

Filtering is a term used to describe methods that estimate the values of partially observed states, such as the position, velocity, and attitude of a vehicle, using current observations that are corrupted due to various sources, such as measurement noise, transmission dropouts, and spurious information. The study of filtering has been an active focus of research for decades, and the resulting filters have been the cornerstone of many of humankind\u27s greatest technological achievements. However, these achievements are enabled principally by the use of specialized techniques that seek to, in some way, combat the negative impacts that processor roundoff and truncation error have on filtering. Two of these specialized techniques are known as square-root filters and consider filters. The former alleviates the fragility induced from estimating error covariance matrices by, instead, managing a factorized representation of that matrix, known as a square-root factor. The latter chooses to account for the statistical impacts a troublesome system parameter has on the overall state estimate without directly estimating it, and the result is a substantial reduction in numerical sensitivity to errors in that parameter. While both of these techniques have found widespread use in practical application, they have never been unified in a common square-root consider framework. Furthermore, consider filters are historically rooted to standard, vector-valued estimation techniques, and they have yet to be generalized to the emerging, set-valued estimation tools for multitarget tracking. In this dissertation, formulae for the square-root consider filter are derived, and the result is extended to finite set statistics-based multitarget tracking tools. These results are used to propose a terrain-aided navigation concept wherein data regarding a vehicle\u27s environment is used to improve its state estimate, and square-root consider techniques provide the numerical stability necessary for an onboard navigation application. The newly developed square-root consider techniques are shown to be much more stable than standard formulations, and the terrain-aided navigation concept is applied to a lunar landing scenario to illustrate its applicability to navigating in challenging environments --Abstract, page iii