Safe Navigation in Dense Traffic Scenarios using Reinforcement Learning as Global Guidance for a Model Predictive Controller

The successful integration of autonomous vehicles (AVs) in human environments is highly dependent on their ability to navigate safely and timely through dense traffic conditions. Such conditions involve a diverse range of human behaviors, ranging from cooperative (willing to yield) to non-cooperative human drivers (unwilling to yield) that need...

Motion Planning for Non-holonomic Autonomous Vehicles in Parking Spaces: An optimal Control Problem Approach

This MSc. thesis explores the design and implementation of a motion planner for non-holonomic autonomous vehicles in parking spaces. The planner must avoid collisions with static obstacles, satisfy performance, comfort and safety constraints for the motion, satisfy the non-holonomic constraints of the vehicle model, consider the dynamics of the...