Model Predictive Control of Highway Emergency Maneuvering and Collision Avoidance

Autonomous emergency maneuvering (AEM) is an active safety system that automates safe maneuvers to avoid imminent collision, particularly in highway driving situations. Uncertainty about the surrounding vehicles’ decisions and also about the road condition, which has significant effects on the vehicle’s maneuverability, makes it challenging to implement the AEM strategy in practice. With the rise of vehicular networks and connected vehicles, vehicles would be able to share their perception and also intentions with other cars. Therefore, cooperative AEM can incor- porate surrounding vehicles’ decisions and perceptions in order to improve vehicles’ predictions and estimations and thereby provide better decisions for emergency maneuvering. In this thesis, we develop an adaptive, cooperative motion planning scheme for emergency maneuvering, based on the model predictive control (MPC) approach, for vehicles within a ve- hicular network. The proposed emergency maneuver planning scheme finds the best combination of longitudinal and lateral maneuvers to avoid imminent collision with surrounding vehicles and obstacles. To implement real-time MPC for the non-convex problem of collision free motion planning, safety constraints are suggested to be convexified based on the road geometry. To take advantage of vehicular communication, the surrounding vehicles’ decisions are incorporated in the prediction model to improve the motion planning results. The MPC approach is prone to loss of feasibility due to the limited prediction horizon for decision-making. For the autonomous vehicle motion planning problem, many of detected ob- stacles, which are beyond the prediction horizon, cannot be considered in the instantaneous de- cisions, and late consideration of them may cause infeasibility. The conditions that guarantee persistent feasibility of a model predictive motion planning scheme are studied in this thesis. Maintaining the system’s states in a control invariant set of the system guarantees the persis- tent feasibility of the corresponding MPC scheme. Specifically, we present two approaches to compute control invariant sets of the motion planning problem; the linearized convexified ap- proach and the brute-force approach. The resulting computed control invariant sets of these two approaches are compared with each other to demonstrate the performance of the proposed algorithm. Time-variation of the road condition affects the vehicle dynamics and constraints. Therefore, it necessitates the on-line identification of the road friction parameter and implementation of an adaptive emergency maneuver motion planning scheme. In this thesis, we investigate coopera- tive road condition estimation in order to improve collision avoidance performance of the AEM system. Each vehicle estimates the road condition individually, and disseminates it through the vehicular network. Accordingly, a consensus estimation algorithm fuses the individual estimates to find the maximum likelihood estimate of the road condition parameter. The performance of the proposed cooperative road condition estimation has been validated through simulations

Recognizing Features in Mobile Laser Scanning Point Clouds Towards 3D High-definition Road Maps for Autonomous Vehicles

The sensors mounted on a driverless vehicle are not always reliable for precise localization and navigation. By comparing the real-time sensory data with a priori map, the autonomous navigation system can transform the complicated sensor perception mission into a simple map-based localization task. However, the lack of robust solutions and standards for creating such lane-level high-definition road maps is a major challenge in this emerging field. This thesis presents a semi-automated method for extracting meaningful road features from mobile laser scanning (MLS) point clouds and creating 3D high-definition road maps for autonomous vehicles. After pre-processing steps including coordinate system transformation and non-ground point removal, a road edge detection algorithm is performed to distinguish road curbs and extract road surfaces followed by extraction of two categories of road markings. On the one hand, textual and directional road markings including arrows, symbols, and words are detected by intensity thresholding and conditional Euclidean clustering. On the other hand, lane markings (lines) are extracted by local intensity analysis and distance thresholding according to road design standards. Afterwards, centerline points in every single lane are estimated based on the position of the extracted lane markings. Ultimately, 3D road maps with precise road boundaries, road markings, and the estimated lane centerlines are created. The experimental results demonstrate the feasibility of the proposed method, which can accurately extract most road features from the MLS point clouds. The average recall, precision, and F1-score obtained from four datasets for road marking extraction are 93.87%, 93.76%, and 93.73%, respectively. All of the estimated lane centerlines are validated using the “ground truthing” data manually digitized from the 4 cm resolution UAV orthoimages. The results of a comparison study show the better performance of the proposed method than that of some other existing methods

Kontextunterstützte Fahrstreifenschätzung - Szeneninterpretation durch Lernen der räumlichen Beziehungen zwischen Bildmerkmalen und Fahrstreifen

Advanced driver assistance systems and automatic driving are becoming more and more important in the market of personal mobility. By increasing traffic safety and allowing the driver to use the traveling time for other activities, the generation of intelligent vehicles creates a new definition of mobility in the future. To extend the limitations of systems for fully automated driving, research institutions and companies are trying to master more complex vehicle environments like urban traffic. While current approaches for camera-based vehicle environment perception use traditional image segmentation and object detection techniques, this work presents a big step towards comprehensive scene understanding. For this purpose, powerful machine learning methods are applied to learning the spatial relations between several types of features in the camera image and the vehicle trajectory. The registration of these spatial relations for all features in a video frame leads to a distribution map which allows the matching of a lane model. Additionally, the context of the current vehicle environment is determined by extracting global image features. Several possibilities for improving the lane detection performance with additional context information are analyzed, and the combination of global and local lane or lane border detection methods is proposed. It is shown that many different types of features within the vehicle environment provide important information about the lane and that, by modeling the spatial relations between features and the trajectory of the vehicle, its lane can be detected. It is also shown that knowledge about the current scene context can be used to improve the lane detection performance.Fahrerassistenzsysteme und automatisches Fahren gewinnen im Bereich der Mobilität mehr und mehr an Bedeutung. Durch erhöhte Sicherheit und die Möglichkeit einer anderweitigen Nutzung der Reisezeit wird die Entwicklung intelligenter Fahrzeuge die Mobilität der Zukunft neu definieren. Um die Grenzen der Systeme für vollautomatisches Fahren zu erweitern, versuchen Forschungseinrichtungen und Firmen bereits, neben gut strukturierten Autobahn-Szenarien auch komplexere Fahrzeugumgebungen zu meistern, wie z.B. den Stadtverkehr. Während derzeitige Methoden zur kamerabasierten Fahrzeugumfelderfassung traditionelle Bildsegmentierungs- und Objekterkennungstechniken verwenden, stellt diese Arbeit einen großen Schritt in Richtung umfassenden Szenenverstehens dar. Zu diesem Zweck werden leistungsfähige Methoden des maschinellen Lernens eingesetzt, um die räumlichen Beziehungen zwischen diversen Merkmalen im Kamerabild und der Fahrzeugtrajektorie zu lernen. Das Zusammenführen der räumlichen Beziehungen aller gefundenen Merkmale eines Kamerabildes resultiert in einer sogenannten Verteilungskarte, in die ein Fahrstreifenmodell eingepasst werden kann. Des Weiteren wird mit Hilfe globaler Bildmerkmale der Kontext der aktuellen Szene bestimmt. Es werden verschiedene Möglichkeiten untersucht, mit Hilfe der gewonnenen Kontextinformation die Fahrstreifenerkennung zu verbessern, und es wird erläutert, wie ein solches globales Verfahren mit einer lokalen Methode zur Fahrstreifen- bzw. Fahrstreifenbegrenzungserkennung kombiniert werden kann. Es wird gezeigt, dass viele verschiedene Arten von Merkmalen in der Fahrzeugumgebung wichtige Informationen über die Lage des Fahrstreifens liefern und dass dieser Fahrstreifen im Bild detektiert werden kann, indem dessen räumliche Beziehungen zu diesen Merkmalen modelliert werden. Außerdem wird gezeigt, wie zusätzliches Wissen über den aktuellen Kontext die Qualität der Fahrstreifenerkennung erhöhen kann

Road terrain detection for Advanced Driver Assistance Systems

Kühnl T. Road terrain detection for Advanced Driver Assistance Systems. Bielefeld: Bielefeld University; 2013

On Compositional Hierarchical Models for holistic Lane and Road Perception in Intelligent Vehicles

This work is a contribution to the vision based perception of multi lane roads of urban intersections. Given multiple input features the proposed probabilistic hierarchical model infers the lane structure as well as the location of stoplines and the turn directions of individual lanes. Thereby, it expresses prior expectations on the road topology using weak probabilistic constraints which allows for the detection of parallel lanes as well as splitting and merging lanes