Integrating Vehicle Slip and Yaw in Overarching Multi-Tiered Automated Vehicle Steering Control to Balance Path Following Accuracy, Gracefulness, and Safety

Balancing path following accuracy and error convergence with graceful motion in steering control is challenging due to the competing nature of these requirements, especially across a range of operating speeds and conditions. This paper demonstrates that an integrated multi-tiered steering controller considering the impact of slip on kinematic control, dynamic control, and steering actuator rate commands achieves accurate and graceful path following. This work is founded on multi-tiered sideslip and yaw-based models, which allow derivation of controllers considering error due to sideslip and the mapping between steering commands and graceful lateral motion. Observer based sideslip estimates are combined with heading error in the kinematic controller to provide feedforward slip compensation. Path following error is compensated by a continuous Variable Structure Controller (VSC) using speed-based path manifolds to balance graceful motion and error convergence. Resulting yaw rate commands are used by a backstepping dynamic controller to generate steering rate commands. A High Gain Observer (HGO) estimates sideslip and yaw rate for output feedback control. Stability analysis of the output feedback controller is provided, and peaking is resolved. The work focuses on lateral control alone so that the steering controller can be combined with other speed controllers. Field results provide comparisons to related approaches demonstrating gracefulness and accuracy in different complex scenarios with varied weather conditions and perturbations

Situation Assessment for Advanced Driver Assistance Systems

Die Arbeit behandelt drei Fragestellungen: Zunächst liegt auf der Erarbeitung einer funktionalen Architekturdetaillierung für die Situationsanalyse. Hierfür wird ihre die Stellung der Situationsanalyse eines fortschrittlichen Fahrerassistenzsystems aufgezeigt, um im Anschluss die funktionale Architektur innerhalb der Situationsanalyse aufzuschlüsseln. Der zweite Teil der Arbeit beinhaltet die Vorstellung eines neuen Algorithmus zur Berechnung der Grenzen des kollisionsfrei erreichbaren Raumes. Er bildet den zentralen Kern der Situationsanalyse einer aktiven Gefahrenbremsung. Der Algorithmus zeichnet sich dadurch aus, dass er sowohl beliebig strukturierte statische Hindernisse als auch dynamische Verkehrsteilnehmer berücksichtigt. Zudem beachtet er deren Interaktionsbeziehungen und die Aufmerksamkeit des Fahrers. Im Zuge der Modellierung fließen auch neue Erkenntnisse über den Einfluss der Breite einer zu durchfahrenden Lücke ein, die aus einer Studie stammen. Kommt der Algorithmus zum Ergebnis, dass der kollisionsfrei erreichbare Raum vollständig durch Hindernisse begrenzt ist - es also keine Ausweichmöglichkeit gibt - so ist ein wesentliches Kriterium für eine automatische Notbremse erfüllt. Auf mehreren Präsentationen war diese echtzeitfähig implementierte Situationsanalyse elementarer Bestandteil der vorgestellten Technik, mit der unfallvermeidende Bremsungen bis in den Stillstand auch oberhalb von 60 km /h möglich werden. Der dritte Teil der Dissertation adressiert eine bislang noch ungelöste Herausforderung: Die Erkennung einer Einfädelsituation aus der Perspektive eines involvierten Fahrzeuges. Auf die Modellierung der Merkmale als auch des Klassifikators wird ausführlich eingegangen. Im Zuge dessen wird das im Forschungsbereich der Fahrerassistenz noch unbekannte Klassifikationsverfahren "'Scenario Based Random Forest"' vorgestellt und beurteilt. Abschließend kann gezeigt werden, dass der Erkennungsalgorithmus in 92% der Fälle zum richtigen Ergebnis führt.This work addresses three issues in the research area of situation analysis for advanced driver assistance systems. The focus of the first part is on the development of a detailed functional architecture for situation analysis. Therefore, the situation analysis' embedding in the overall system is shown. The functional architecture within the situation analysis is developed afterwards. The second part of this work introduces a new algorithm for calculating the borders of the collision-free reachability area. This algorithm forms the central piece of the situation analysis of an active hazard braking function. It considers arbitrary structured static obstacles, dynamic road users, their interaction relationships as well as the driver's state of attention. The influence of narrow gap's widths between obstacles in the context of evasion manoevers were addressed in a study. If the result of the computation indicates that the collision-free reachability area is completely limited by obstacles - in other words that no collision free track exists - an essential criterion for the use of an automatic emergency brake is met. The results were shown with a real vehicle using such an active hazard braking system. It demonstrated warrantable full stop collision avoiding braking interventions even at differential speeds above 60km/h. The third part of the thesis addresses the detection of convoy merging situations from the perspective of an involved vehicle. Within the development of such a classification algorithm, the method ``Scenario based random forest'' is introduced. This thesis describes the modeling of the situation and hence the features used for the classification as well as the training of the classifier in detail. The results show, that the convoy merging situations are classified correctly in 92% of given samples. All presented results were obtained by using real-world sensor data

A Game Theory Based Model of Human Driving with Application to Autonomous and Mixed Driving

In this work, I consider the development of a driver model to better understand human drivers’ various behaviors in the upcoming mixed situation of human drivers and autonomous vehicles. For this, my current effort focuses on modeling the driver’s decisions and corresponding driving behaviors. First, I study an individual driver’s reasoning process through game theoretic investigation. The driver decision model is modeled as the Stackelberg game, which is based on the backward information propagation. In the driver decision model, I focus on the drivers’ insensible desires and corresponding unwanted traffic situations. With the comparison of the model and the field data, it is shown that the model reproduces the relationship between the driver’s inattentiveness and collisions in the real world. Next, the driving behavior control is presented. I propose a human-like predictive perception model of potential collision with an adjacent vehicle. The model is based on hybrid systematic approach. In turn, with the predictive perceptions, a driving safety controller is designed based on model predictive control. The model shows adequate predictive responses against the other vehicles with respect to the driver’s rationality. In sum, I present a driver model that corresponds to and predicts traffic situations according to a human driver’s irrationality factor. This model shows a meaningful similarity to the real-world crashes and predictive behaviors according to the driver’s irrationality

Adaptive routing of autonomous vehicles using neighborhood traffic

As research on autonomous vehicles increases, automotive manufacturers and researchers are developing coordination techniques to enable safe passage of vehicles through intersections. These techniques are called Autonomous Intersection Management (AIM). Even though AIM techniques improve intersection throughput, they do not effectively reduce congestion. Real life urban roads comprise of a networks of multiple intersections. In such scenarios, communicating traffic information between intersections is essential for reducing congestion on the roads. To achieve this, we propose an adaptive routing algorithm that incorporates a fusion of vehicle-to-intersection (V2I) communication and intersection-to-intersection (I2I) communication in order to bring about significant reductions in congestion. To implement this algorithm, we constructed the Enhanced AIM simulation framework as an extension of AIM simulator (University of Texas, Austin). We demonstrate with simulation experiments that our proposed routing algorithm shows reduced congestion and wait-time, and improved user experience.autonomous vehiclesAutonomous Intersection Management (AIM)urban roadsneighborhood traffi

Konzeption, Umsetzung und Evaluation eines Manöverassistenzsystems mit haptischer Fahrerunterstützung

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2014von Florian Belse