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

Control predictivo basado en modelo para control lateral de vehículos

200 p.La mejora tecnológica ha supuesto el desarrollo de sistemas de seguridad avanzada en vehículos, entrelos que destacan los sistemas de guiado avanzados para vehículos autónomos y semiautónomos. Deforma general, este tipo de sistemas se basan en la resolución de diferentes subproblemas independientes:la definición de la trayectoria a seguir, el control para que el vehículo siga dicha trayectoria y la toma dedecisión para la asistencia al conductor. De estos tres subproblemas la presente tesis abarca el relacionadocon el control.Para dicho fin, se plantea el desarrollo de un control predictivo para control deseguimiento lateral de trayectoria de un vehículo, de forma que éste sea capaz de seguiruna trayectoria de referencia conocida de forma segura y confortable para todoslos pasajeros, cumpliendo las restricciones impuestas y garantizando así la permanenciadentro del carril y evitando giros bruscos de volante. Además, esta metodologíapropuesta, a diferencia de otros trabajos, puede ser aplicada para diferentes trazadosy en un amplio rango de velocidades.En primer lugar, se desarrolla un modelo del sistema para ser empleado adecuadamente como modelo depredicción en las diferentes estrategias de control. A continuación, se plantean tres propuestas de controlpredictivo: un control predictivo basado en modelo y con modelo de predicción variable, que sirve comobase para las siguientes estrategias de control; un control predictivo basado en modelo con estabilidadgarantizada y modelo de predicción variable; y un control predictivo basado en modelo robusto conestabilidad garantizada basado en tubos de trayectorias. Los tres controladores propuestos han sidovalidados experimentalmente