Model-Based Threat Assessment in Semi-Autonomous Vehicles with Model Parameter Uncertainties

In this paper, we consider model-based threat assessment methods which rely on vehicle and driver mathematical models and are based on reachability analysis tools and set invariance theory. We focus on the parametric uncertainties of the driver mathematical model and show how these can be accounted for in the threat assessment. The novelty of the proposed methods lies in the inclusion of the driver model uncertainties in the threat assessment problem formulation and in their validation through experimental data. We show how different ways of accounting for the model uncertainties impact the capabilities and the effectiveness of the proposed algorithms in detecting hazardous driving situations

A Human Driver Model for Autonomous Lane Changing in Highways: Predictive Fuzzy Markov Game Driving Strategy

This study presents an integrated hybrid solution to mandatory lane changing problem to deal with accident avoidance by choosing a safe gap in highway driving. To manage this, a comprehensive treatment to a lane change active safety design is proposed from dynamics, control, and decision making aspects. My effort first goes on driver behaviors and relating human reasoning of threat in driving for modeling a decision making strategy. It consists of two main parts; threat assessment in traffic participants, (TV s) states, and decision making. The first part utilizes an complementary threat assessment of TV s, relative to the subject vehicle, SV , by evaluating the traffic quantities. Then I propose a decision strategy, which is based on Markov decision processes (MDPs) that abstract the traffic environment with a set of actions, transition probabilities, and corresponding utility rewards. Further, the interactions of the TV s are employed to set up a real traffic condition by using game theoretic approach. The question to be addressed here is that how an autonomous vehicle optimally interacts with the surrounding vehicles for a gap selection so that more effective performance of the overall traffic flow can be captured. Finding a safe gap is performed via maximizing an objective function among several candidates. A future prediction engine thus is embedded in the design, which simulates and seeks for a solution such that the objective function is maximized at each time step over a horizon. The combined system therefore forms a predictive fuzzy Markov game (FMG) since it is to perform a predictive interactive driving strategy to avoid accidents for a given traffic environment. I show the effect of interactions in decision making process by proposing both cooperative and non-cooperative Markov game strategies for enhanced traffic safety and mobility. This level is called the higher level controller. I further focus on generating a driver controller to complement the automated car’s safe driving. To compute this, model predictive controller (MPC) is utilized. The success of the combined decision process and trajectory generation is evaluated with a set of different traffic scenarios in dSPACE virtual driving environment. Next, I consider designing an active front steering (AFS) and direct yaw moment control (DYC) as the lower level controller that performs a lane change task with enhanced handling performance in the presence of varying front and rear cornering stiffnesses. I propose a new control scheme that integrates active front steering and the direct yaw moment control to enhance the vehicle handling and stability. I obtain the nonlinear tire forces with Pacejka model, and convert the nonlinear tire stiffnesses to parameter space to design a linear parameter varying controller (LPV) for combined AFS and DYC to perform a commanded lane change task. Further, the nonlinear vehicle lateral dynamics is modeled with Takagi-Sugeno (T-S) framework. A state-feedback fuzzy H∞ controller is designed for both stability and tracking reference. Simulation study confirms that the performance of the proposed methods is quite satisfactory

Decision-theoretic MPC: Motion Planning with Weighted Maneuver Preferences Under Uncertainty

Continuous optimization based motion planners require deciding on a maneuver homotopy before optimizing the trajectory. Under uncertainty, maneuver intentions of other participants can be unclear, and the vehicle might not be able to decide on the most suitable maneuver. This work introduces a method that incorporates multiple maneuver preferences in planning. It optimizes the trajectory by considering weighted maneuver preferences together with uncertainties ranging from perception to prediction while ensuring the feasibility of a chance-constrained fallback option. Evaluations in both driving experiments and simulation studies show enhanced interaction capabilities and comfort levels compared to conventional planners, which consider only a single maneuver

Context Exploitation in Data Fusion

Complex and dynamic environments constitute a challenge for existing tracking algorithms. For this reason, modern solutions are trying to utilize any available information which could help to constrain, improve or explain the measurements. So called Context Information (CI) is understood as information that surrounds an element of interest, whose knowledge may help understanding the (estimated) situation and also in reacting to that situation. However, context discovery and exploitation are still largely unexplored research topics. Until now, the context has been extensively exploited as a parameter in system and measurement models which led to the development of numerous approaches for the linear or non-linear constrained estimation and target tracking. More specifically, the spatial or static context is the most common source of the ambient information, i.e. features, utilized for recursive enhancement of the state variables either in the prediction or the measurement update of the filters. In the case of multiple model estimators, context can not only be related to the state but also to a certain mode of the filter. Common practice for multiple model scenarios is to represent states and context as a joint distribution of Gaussian mixtures. These approaches are commonly referred as the join tracking and classification. Alternatively, the usefulness of context was also demonstrated in aiding the measurement data association. Process of formulating a hypothesis, which assigns a particular measurement to the track, is traditionally governed by the empirical knowledge of the noise characteristics of sensors and operating environment, i.e. probability of detection, false alarm, clutter noise, which can be further enhanced by conditioning on context. We believe that interactions between the environment and the object could be classified into actions, activities and intents, and formed into structured graphs with contextual links translated into arcs. By learning the environment model we will be able to make prediction on the target\u2019s future actions based on its past observation. Probability of target future action could be utilized in the fusion process to adjust tracker confidence on measurements. By incorporating contextual knowledge of the environment, in the form of a likelihood function, in the filter measurement update step, we have been able to reduce uncertainties of the tracking solution and improve the consistency of the track. The promising results demonstrate that the fusion of CI brings a significant performance improvement in comparison to the regular tracking approaches

Predictive Maneuver Planning and Control of an Autonomous Vehicle in Multi-Vehicle Traffic with Observation Uncertainty

Autonomous vehicle technology is a promising development for improving the safety, efficiency and environmental impact of on-road transportation systems. However, the task of guiding an autonomous vehicle by rapidly and systematically accommodating the plethora of changing constraints, e.g. of avoiding multiple stationary and moving obstacles, obeying traffic rules, signals and so on as well as the uncertain state observation due to sensor imperfections, remains a major challenge. This dissertation attempts to address this challenge via designing a robust and efficient predictive motion planning framework that can generate the appropriate vehicle maneuvers (selecting and tracking specific lanes, and related speed references) as well as the constituent motion trajectories while considering the differential vehicle kinematics of the controlled vehicle and other constraints of operating in public traffic. The main framework combines a finite state machine (FSM)-based maneuver decision module with a model predictive control (MPC)-based trajectory planner. Based on the prediction of the traffic environment, reference speeds are assigned to each lane in accordance with the detection of objects during measurement update. The lane selection decisions themselves are then incorporated within the MPC optimization. The on-line maneuver/motion planning effort for autonomous vehicles in public traffic is a non-convex problem due to the multiple collision avoidance constraints with overlapping areas, lane boundaries, and nonlinear vehicle-road dynamics constraints. This dissertation proposes and derives some remedies for these challenges within the planning framework to improve the feasibility and optimality of the solution. Specifically, it introduces vehicle grouping notions and derives conservative and smooth algebraic models to describe the overlapped space of several individual infeasible spaces and help prevent the optimization from falling into undesired local minima. Furthermore, in certain situations, a forced objective selection strategy is needed and adopted to help the optimization jump out of local minima. Furthermore, the dissertation considers stochastic uncertainties prevalent in dynamic and complex traffic and incorporate them with in the predictive planning and control framework. To this end, Bayesian filters are implemented to estimate the uncertainties in object motions and then propagate them into the prediction horizon. Then, a pair-wise probabilistic collision condition is defined for objects with non-negligible geometrical shape/sizes and computationally efficient and conservative forms are derived to efficiently and analytically approximate the involved multi-variate integrals. The probabilistic collision evaluation is then applied within a vehicle grouping algorithms to cluster the object vehicles with closeness in positions and speeds and eventually within the stochastic predictive maneuver planner framework to tighten the chanced-constraints given a deterministic confidence margin. It is argued that these steps make the planning problem tractable for real-time implementation on autonomously controlled vehicles

Motion Planning for Autonomous Vehicles in Partially Observable Environments

Unsicherheiten, welche aus Sensorrauschen oder nicht beobachtbaren Manöverintentionen anderer Verkehrsteilnehmer resultieren, akkumulieren sich in der Datenverarbeitungskette eines autonomen Fahrzeugs und führen zu einer unvollständigen oder fehlinterpretierten Umfeldrepräsentation. Dadurch weisen Bewegungsplaner in vielen Fällen ein konservatives Verhalten auf. Diese Dissertation entwickelt zwei Bewegungsplaner, welche die Defizite der vorgelagerten Verarbeitungsmodule durch Ausnutzung der Reaktionsfähigkeit des Fahrzeugs kompensieren. Diese Arbeit präsentiert zuerst eine ausgiebige Analyse über die Ursachen und Klassifikation der Unsicherheiten und zeigt die Eigenschaften eines idealen Bewegungsplaners auf. Anschließend befasst sie sich mit der mathematischen Modellierung der Fahrziele sowie den Randbedingungen, welche die Sicherheit gewährleisten. Das resultierende Planungsproblem wird mit zwei unterschiedlichen Methoden in Echtzeit gelöst: Zuerst mit nichtlinearer Optimierung und danach, indem es als teilweise beobachtbarer Markov-Entscheidungsprozess (POMDP) formuliert und die Lösung mit Stichproben angenähert wird. Der auf nichtlinearer Optimierung basierende Planer betrachtet mehrere Manöveroptionen mit individuellen Auftrittswahrscheinlichkeiten und berechnet daraus ein Bewegungsprofil. Er garantiert Sicherheit, indem er die Realisierbarkeit einer zufallsbeschränkten Rückfalloption gewährleistet. Der Beitrag zum POMDP-Framework konzentriert sich auf die Verbesserung der Stichprobeneffizienz in der Monte-Carlo-Planung. Erstens werden Informationsbelohnungen definiert, welche die Stichproben zu Aktionen führen, die eine höhere Belohnung ergeben. Dabei wird die Auswahl der Stichproben für das reward-shaped Problem durch die Verwendung einer allgemeinen Heuristik verbessert. Zweitens wird die Kontinuität in der Reward-Struktur für die Aktionsauswahl ausgenutzt und dadurch signifikante Leistungsverbesserungen erzielt. Evaluierungen zeigen, dass mit diesen Planern große Erfolge in Fahrversuchen und Simulationsstudien mit komplexen Interaktionsmodellen erreicht werden

A Human Driver Model for Autonomous Lane Changing in Highways: Predictive Fuzzy Markov Game Driving Strategy

This study presents an integrated hybrid solution to mandatory lane changing problem to deal with accident avoidance by choosing a safe gap in highway driving. To manage this, a comprehensive treatment to a lane change active safety design is proposed from dynamics, control, and decision making aspects. My effort first goes on driver behaviors and relating human reasoning of threat in driving for modeling a decision making strategy. It consists of two main parts; threat assessment in traffic participants, (TV s) states, and decision making. The first part utilizes an complementary threat assessment of TV s, relative to the subject vehicle, SV , by evaluating the traffic quantities. Then I propose a decision strategy, which is based on Markov decision processes (MDPs) that abstract the traffic environment with a set of actions, transition probabilities, and corresponding utility rewards. Further, the interactions of the TV s are employed to set up a real traffic condition by using game theoretic approach. The question to be addressed here is that how an autonomous vehicle optimally interacts with the surrounding vehicles for a gap selection so that more effective performance of the overall traffic flow can be captured. Finding a safe gap is performed via maximizing an objective function among several candidates. A future prediction engine thus is embedded in the design, which simulates and seeks for a solution such that the objective function is maximized at each time step over a horizon. The combined system therefore forms a predictive fuzzy Markov game (FMG) since it is to perform a predictive interactive driving strategy to avoid accidents for a given traffic environment. I show the effect of interactions in decision making process by proposing both cooperative and non-cooperative Markov game strategies for enhanced traffic safety and mobility. This level is called the higher level controller. I further focus on generating a driver controller to complement the automated car’s safe driving. To compute this, model predictive controller (MPC) is utilized. The success of the combined decision process and trajectory generation is evaluated with a set of different traffic scenarios in dSPACE virtual driving environment. Next, I consider designing an active front steering (AFS) and direct yaw moment control (DYC) as the lower level controller that performs a lane change task with enhanced handling performance in the presence of varying front and rear cornering stiffnesses. I propose a new control scheme that integrates active front steering and the direct yaw moment control to enhance the vehicle handling and stability. I obtain the nonlinear tire forces with Pacejka model, and convert the nonlinear tire stiffnesses to parameter space to design a linear parameter varying controller (LPV) for combined AFS and DYC to perform a commanded lane change task. Further, the nonlinear vehicle lateral dynamics is modeled with Takagi-Sugeno (T-S) framework. A state-feedback fuzzy H∞ controller is designed for both stability and tracking reference. Simulation study confirms that the performance of the proposed methods is quite satisfactory

A new integrated collision risk assessment methodology for autonomous vehicles

Real-time risk assessment of autonomous driving at tactical and operational levels is extremely challenging since both contextual and circumferential factors should concurrently be considered. Recent methods have started to simultaneously treat the context of the traffic environment along with vehicle dynamics. In particular, interaction-aware motion models that take inter-vehicle dependencies into account by utilizing the Bayesian interference are employed to mutually control multiple factors. However, communications between vehicles are often assumed and the developed models are required many parameters to be tuned. Consequently, they are computationally very demanding. Even in the cases where these desiderata are fulfilled, current approaches cannot cope with a large volume of sequential data from organically changing traffic scenarios, especially in highly complex operational environments such as dense urban areas with heterogeneous road users. To overcome these limitations, this paper develops a new risk assessment methodology that integrates a network-level collision estimate with a vehicle-based risk estimate in real-time under the joint framework of interaction-aware motion models and Dynamic Bayesian Networks (DBN). Following the formulation and explanation of the required functions, machine learning classifiers were utilized for the real-time network-level collision prediction and the results were then incorporated into the integrated DBN model for predicting collision probabilities in real-time. Results indicated an enhancement of the interaction-aware model by up to 10%, when traffic conditions are deemed as collision-prone. Hence, it was concluded that a well-calibrated collision prediction classifier provides a crucial hint for better risk perception by autonomous vehicles