Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving

Adverse weather conditions and occlusions in urban environments result in impaired perception. The uncertainties are handled in different modules of an automated vehicle, ranging from sensor level over situation prediction until motion planning. This paper focuses on motion planning given an uncertain environment model with occlusions. We present a method to remain collision free for the worst-case evolution of the given scene. We define criteria that measure the available margins to a collision while considering visibility and interactions, and consequently integrate conditions that apply these criteria into an optimization-based motion planner. We show the generality of our method by validating it in several distinct urban scenarios

Foresighted digital twin for situational agent selection in production control

As intelligent Data Acquisition and Analysis in Manufacturing nears its apex, a new era of Digital Twins is dawning. Foresighted Digital Twins enable short- to medium-term system behavior predictions to infer optimal production operation strategies. Creating up-to-the-minute Digital Twins requires both the availability of real-time data and its incorporation and serve as a stepping-stone into developing unprecedented forms of production control. Consequently, we regard a new concept of Digital Twins that includes foresight, thereby enabling situational selection of production control agents. One critical element for adequate system predictions is human behavior as it is neither rule-based nor deterministic, which we therefore model applying Reinforcement Learning. Owing to these ever-changing circumstances, rigid operation strategies crucially restrain reactions, as opposed to circumstantial control strategies that hence can outperform traditional approaches. Building on enhanced foresights we show the superiority of this approach and present strategies for improved situational agent selection

Considering Human Factors in Risk Maps for Robust and Foresighted Driver Warning

Driver support systems that include human states in the support process is an active research field. Many recent approaches allow, for example, to sense the driver's drowsiness or awareness of the driving situation. However, so far, this rich information has not been utilized much for improving the effectiveness of support systems. In this paper, we therefore propose a warning system that uses human states in the form of driver errors and can warn users in some cases of upcoming risks several seconds earlier than the state of the art systems not considering human factors. The system consists of a behavior planner Risk Maps which directly changes its prediction of the surrounding driving situation based on the sensed driver errors. By checking if this driver's behavior plan is objectively safe, a more robust and foresighted driver warning is achieved. In different simulations of a dynamic lane change and intersection scenarios, we show how the driver's behavior plan can become unsafe, given the estimate of driver errors, and experimentally validate the advantages of considering human factors

Intelligent production control for time-constrained complex job shops

Im Zuge der zunehmenden Komplexität der Produktion wird der Wunsch nach einer intelligenten Steuerung der Abläufe in der Fertigung immer größer. Sogenannte Complex Job Shops bezeichnen dabei die komplexesten Produktionsumgebungen, die deshalb ein hohes Maß an Agilität in der Steuerung erfordern. Unter diesen Umgebungen sticht die besonders Halbleiterfertigung hervor, da sie alle Komplexitäten eines Complex Job-Shop vereint. Deshalb ist die operative Exzellenz der Schlüssel zum Erfolg in der Halbleiterindustrie. Diese Exzellenz hängt ganz entscheidend von einer intelligenten Produktionssteuerung ab. Ein Hauptproblem bei der Steuerung solcher Complex Job-Shops, in diesem Fall der Halbleiterfertigung, ist das Vorhandensein von Zeitbeschränkungen (sog. time-constraints), die die Transitionszeit von Produkten zwischen zwei, meist aufeinanderfolgenden, Prozessen begrenzen. Die Einhaltung dieser produktspezifischen Zeitvorgaben ist von größter Bedeutung, da Verstöße zum Verlust des betreffenden Produkts führen. Der Stand der Technik bei der Produktionssteuerung dieser Dispositionsentscheidungen, die auf die Einhaltung der Zeitvorgaben abzielen, basiert auf einer fehleranfälligen und für die Mitarbeiter belastenden manuellen Steuerung. In dieser Arbeit wird daher ein neuartiger, echtzeitdatenbasierter Ansatz zur intelligenten Steuerung der Produktionssteuerung für time-constrained Complex Job Shops vorgestellt. Unter Verwendung einer jederzeit aktuellen Replikation des realen Systems werden sowohl je ein uni-, multivariates Zeitreihenmodell als auch ein digitaler Zwilling genutzt, um Vorhersagen über die Verletzung dieser time-constraints zu erhalten. In einem zweiten Schritt wird auf der Grundlage der Erwartung von Zeitüberschreitungen die Produktionssteuerung abgeleitet und mit Echtzeitdaten anhand eines realen Halbleiterwerks implementiert. Der daraus resultierende Ansatz wird gemeinsam mit dem Stand der Technik validiert und zeigt signifikante Verbesserungen, da viele Verletzungen von time-constraints verhindert werden können. Zukünftig soll die intelligente Produktionssteuerung daher in weiteren Complex Job Shop-Umgebungen evaluiert und ausgerollt werden

Situation-based Risk Evaluation and Behavior Planning

The presented dissertation addresses the problem of risk evaluation and behavior planning for future intelligent Advanced Driver Assistance Systems (ADAS). For this purpose, a novel framework for situation-based risk evaluation and behavior planning, targeting highly automated driving, is presented. After properly sensing the current scene, including the current road topology and other traffic participants, the proposed framework first estimates and predicts the future behavior of all involved entities comprising a situation classification and trajectory prediction step. This is then followed by the generation of the own future behavior in a behavior planning step which is based on an evaluation of possible ego behavior alternatives in terms of risk and utility considerations. The future behavior is planned in a way to find a tradeoff between the expected future risk and utility. Inner-city traffic scenarios in particular are usually complex and of high uncertainty, considering measurements as well as behavioral decisions. To reduce the complexity, similar behavior alternatives are clustered and represented by prototypical behavior patterns using so-called situations. A novel situation classification approach is proposed to estimate how good a situation matches with the actual behaviors. This approach is based on a comparison of the prototypically predicted trajectories of the considered situations with the actual measured trajectories. For this purpose a novel measure for spatio-temporal trajectory similarity, based on the evaluation of longitudinal and lateral spatio-temporal distance, is derived. The situation classification system is used to detect incorrect and critical traffic behaviors, especially in scenarios with a disregard of right-of-way. Evaluating the system using real-world crash cases reveals that it is able to warn the driver reliably of an upcoming crash, with sufficient time to initiate a suitable evasive behavior. For the prediction of situation-dependent prototypical scene evolution patterns, the interaction-aware Foresighted Driver Model (FDM) is applied in a forward simulation of a sensed scene under different situation-dependent behavioral assumptions. The proposed FDM is a novel, time continuous driver model for the simulation and prediction of freeway and urban traffic. Based on the general risk evaluation and behavior planning framework developed in this thesis, the driver model equations are introduced from the assumption that a driver tries to balance predictive risk (e.g. due to possible collisions along its route) with utility (e.g. the time required to travel, smoothness of ride, etc.). For this purpose, a computationally inexpensive, approximate risk model targeting only risk maxima and a gradient descent-based behavior generation is applied. It is shown, how such a model can be used to simulate and predict driving behavior with a similar performance compared to full behavior planning models. The FDM is applicable to a wide range of different scenarios, e.g. intersection or highway-accessing scenarios, with the consideration of an arbitrary number of traffic entities. Thus, the FDM generalizes and reaches beyond state-of-the-art driver models. Complex traffic situations require the estimation of future behavior alternatives in terms of predictive risks. Risk assessment has to be driven from the knowledge that the acting scene entity requires to evaluate the own future behavior. Based on the predicted future dynamics of traffic scene entities, an approach is presented, where a continuous, probabilistic model for future risk is used to build so-called predictive risk maps. These maps indicate how risky a certain ego behavior will be at different future times, so that they can be used to directly plan the best possible future behavior. The behavior in complex scenarios differs strongly, depending on the actually occurring situation. However, sensory measurements of the ego- and other involved entities' states as well as the prediction of possible future states are generally of high uncertainty. As a consequence, the current driving situation can only be approximated. Additionally, a situation can change very quickly, e.g. if a traffic participant suddenly changes its behavior. In this thesis an approach is proposed, how to plan a safe, but still efficient future behavior under consideration of multiple possible situations with different occurrence probabilities. In several traffic scenarios comprising simulated as well as recorded real-world data, it is shown that the approach generates an efficient behavior for situations which are likely to occur, while generating a plan B to safely deal with improbable but risky situations