Matthias Schreier

Modeling and adaptive control of a quadrotor

In this paper, we propose two variants of adaptive state space controllers for attitude stabilization and self-tuning of a four-rotor aerial robot, a quadrotor. First of all, the use of a Model Identification Adaptive Controller (MIAC) is proposed in terms of combining a recursive least-squares estimator with exponential forgetting with an integral discrete-time state space controller. Furthermore, a continuous-time Model Reference Adaptive Control (MRAC) scheme based on Lyapunov theory is applied to the simplified dynamics of a quadrotor, which guarantees global asymptotic stability for at least linear overall systems. The effectiveness of the suggested adaptive methods is demonstrated in simulations with a quaternion-based nonlinear dynamic model of a quadrotor derived in this work. The results are compared to a designed nonadaptive integral state space controller.

PRORETA 3: An Integrated Approach to Collision Avoidance and Vehicle Automation

Zusammenfassung The article describes first results of the research project PRORETA 3 that aims at the development of an integral driver assistance system for collision avoidance and automated vehicle guidance based on a modular system architecture. For this purpose, relevant information is extracted from a dense environment model and fed into a potential field-based trajectory planner that calculates reference signals for underlying vehicle controllers. In addition, the driver is supported by a human-machine interface. Abstract Der Beitrag beschreibt erste Ergebnisse des Forschungsprojektes PRORETA 3, das die Entwicklung eines integralen Fahrerassistenzsystems zur Kollisionsvermeidung und automatisierten Fahrzeugführung auf Basis einer modularen Systemarchitektur anstrebt. Hierzu werden relevante Informationen aus einem dichten Umfeldmodell extrahiert und in einem potentialfeldbasierten Trajektorienplaner verarbeitet, der Führungsgrößen für unterlagerte Fahrzeugregler generiert. Zusätzlich unterstützt eine Mensch-Maschine-Schnittstelle den Fahrer zielgerichtet bei der Fahrzeugführung.

Bayesian, maneuver-based, long-term trajectory prediction and criticality assessment for driver assistance systems

We propose a Bayesian trajectory prediction and criticality assessment system that allows to reason about imminent collisions of a vehicle several seconds in advance. We first infer a distribution of high-level, abstract driving maneuvers such as lane changes, turns, road followings, etc. of all vehicles within the driving scene by modeling the domain in a Bayesian network with both causal and diagnostic evidences. This is followed by maneuver-based, long-term trajectory predictions, which themselves contain random components due to the immanent uncertainty of how drivers execute specific maneuvers. Taking all uncertain predictions of all maneuvers of every vehicle into account, the probability of the ego vehicle colliding at least once within a time span is evaluated via Monte-Carlo simulations and given as a function of the prediction horizon. This serves as the basis for calculating a novel criticality measure, the Time-To-Critical-Collision-Probability (TTCCP) - a generalization of the common Time-To-Collision (TTC) in arbitrary, uncertain, multi-object driving environments and valid for longer prediction horizons. The system is applicable from highly-structured to completely non-structured environments and additionally allows the prediction of vehicles not behaving according to a specific maneuver class.

Robust free space detection in occupancy grid maps by methods of image analysis and dynamic B-spline contour tracking

We propose a new method for free space detection and description for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. The detection is based on successive morphological image processing steps that are applied to an occupancy grid map-based environment representation acquired by an automotive radar sensor. The boundary of the found free space segment is traced and serves as a virtual measurement for a time-variant Kalman Filter in order to estimate and track the control points of a two-dimensional B-spline closed free space contour over time. In contrast to existing free space detection methods, the proposed solution incorporates knowledge about the vehicles dimensions and does not exclude free space that is not directly in the line of sight, but mapped beforehand, as well as free space behind obstacles. Furthermore, the algorithm shows advantages in terms of an intuitive control over spatial and temporal smoothness of the solution as well as an inherent robustness due to model-based filtering. Moreover, the control points of the B-spline curve are proposed as a new low-dimensional representation of drivable free space of arbitrary shape. The effectiveness of the algorithm is demonstrated in real traffic scenarios.

An Integrated Approach to Maneuver-Based Trajectory Prediction and Criticality Assessment in Arbitrary Road Environments

This paper describes an integrated Bayesian approach to maneuver-based trajectory prediction and criticality assessment that is not limited to specific driving situations. First, a distribution of high-level driving maneuvers is inferred for each vehicle in the traffic scene via Bayesian inference. For this purpose, the domain is modeled in a Bayesian network with both causal and diagnostic evidences and an additional trash maneuver class, which allows the detection of irrational driving behavior and the seamless application from highly structured to nonstructured environments. Subsequently, maneuver-based probabilistic trajectory prediction models are employed to predict each vehicles configuration forward in time. Random elements in the designed models consider the uncertainty within the future driving maneuver execution of human drivers. Finally, the criticality time metric time-to-critical-collision-probability (TTCCP) is introduced and estimated via Monte Carlo simulations. The TTCCP is a generalization of the time-to-collision (TTC) in arbitrary uncertain multiobject driving environments and valid for longer prediction horizons. All uncertain predictions of all maneuvers of every vehicle are taken into account. Additionally, the criticality assessment considers arbitrarily shaped static environments, and it is shown how parametric free space (PFS) maps can advantageously be utilized for this purpose.

From grid maps to Parametric Free Space maps — A highly compact, generic environment representation for ADAS

We propose a highly compact, generic representation of the driving environment, so-called Parametric Free Space (PFS) maps, specifically suitable for future Advanced Driver Assistance Systems (ADAS) and bring them into line with existing metric representations known from mobile robotics. PFS maps combine a closed contour description of arbitrarily shaped outer free space boundaries with a representation of inner free space boundaries, respectively objects, by geometric primitives. A real-time capable algorithm is presented that obtains the representation by building upon an intermediate occupancy grid map-based environment representation generated from an automotive radar and a stereo camera. The proposed representation preserves all relevant information contained in a grid map - thus remaining function-independent - in a much more compact way, which is considered particularly important for the transmission via low data-rate automotive communication interfaces. Examples are shown in real traffic scenarios.

Compact Representation of Dynamic Driving Environments for ADAS by Parametric Free Space and Dynamic Object Maps

We present a novel parametric representation of general dynamic driving environments. It is particularly suitable for near-future Advanced Driver Assistance Systems due to its compactness, inherent consistency between static and dynamic entities, suppression of irrelevant details, as well as its sensor-independent, real-time capable generation. By building upon a common occupancy grid map-based environment representation, cells belonging to dynamic objects are simultaneously extracted, classified, and tracked in an object-based manner via an Interacting-MultipleModel-Unscented-Kalman-Probabilistic-Data-Association (IMM-UK-PDA) filter and cleared from the grid. The remaining static environment grid is subsequently processed by methods from the image analysis domain, followed by an Information Filter-based contour tracking of relevant free space boundaries to create a spatiotemporally smooth compact Parametric Free Space (PFS) map. The PFS map represents the static part of the traffic scene with modest bandwidth requirements. The system runs in real time on an experimental vehicle equipped with an automotive radar and a stereo camera and is evaluated within real traffic environments.

Grid Mapping in Dynamic Road Environments: Classification of Dynamic Cell Hypothesis via Tracking

We propose a method capable of acquiring an occupancy grid map-based representation of the local, static driving environment around an intelligent vehicle in the presence of dynamic objects. These corrupt the representation due to violating the underlying static-world assumptions of common grid mapping algorithms and are therefore detected and filtered from the map. For this purpose, a subsequent step is suggested that identifies, clusters and merges dynamic cell hypothesis in a novel way. Thereafter, an Interacting-Multiple-Model-Unscented-Kalman-Probabilistic-Data-Association (IMM-UK-PDA) tracker is used to classify of whether cell movements behave consistently with possible movement characteristics of real dynamic objects or are just generated by noise or newly observed static environment. In opposition to many other approaches, the method explicitly combines information of newly occupied and free areas, completes the shape of only partly visible dynamic objects and uses an advanced object tracking scheme to clean the grid from dynamic object corruptions. The method is evaluated with grids generated by an automotive radar and stereo camera in real traffic environments.

A high-level road model information fusion framework and its application to multi-lane speed limit inference

We propose a high-level road model information fusion framework to combine regulatory traffic elements, e.g. traffic signs, with lane geometry and digital map information for robust inference of lane-specific traffic rules. In this process, special care is given to adequately consider incomplete, uncertain, and inconsistent information sources with i) spatial, ii) existence, and iii) attribute uncertainties. First, Bayesian networks are employed for logical lane assignment of traffic elements under incorporation of traffic regulation knowledge and soft position relation evidences. The position relations are estimated via Monte Carlo simulations by taking spatial lane geometry and existence uncertainties into account. Second, Dempster-Shafer theory is used not only for fusing simultaneously detected traffic signs based on a novel belief mass transfer over adjacent lanes to recover from false sign classifications but also for traffic situation-dependent, lane-specific fusion of digital map attributes with sensor-inferred attributes. The framework is applied to the task of multi-lane speed limit inference, which gives lane-specific speed limit information in form of belief mass functions and runs in real-time on an experimental vehicle.

Automated Detection of Construction Sites on Motorways

Construction sites may not be supported by near-future highly-automated driving systems. Therefore, the online detection of such situations well in advance is necessary to hand over control to the driver in time. This paper introduces an approach for online construction site detection on motorways by combining a set of uncertain cues in a probabilistic way. This allows to reason about the existence and location of construction sites in front of the ego vehicle via inference in a designed Bayesian network. The system runs in real-time on a prototype vehicle and is evaluated in real-world scenarios.