Planning and Vision Based Methods for Autonomous Vehicles

摘要: Autonomous vehicles(AVs) have the potential to revolutionize how we ultimately perceive modern transportation. Many current car models already feature advanced driver-assist systems (ADAS), such as adaptive cruise control (ACC), Lane Departure Warning (LDW), Lane Keep As-sist (LKA), and parking assist systems that allow cars to steer themselves. Companies like Waymo are working towards achieving Level 5 autonomy, allowing vehicles to drive on existing roads and navigate various environmental conditions with little human input. However, the adoption of AVs has been slow. Studies have cited missing research as a barrier that has prevented a more widespread deployment of AVs. Through this work, I hope to address some of these research gaps in motion planning and perception of AV and contribute to the advancement of AV technology. Chapter 2 explores mission planning approaches for a team of motion-constrained vehicles that need to visit a set of targets. The problem is formally defined as the Minmax Dubins Generalized multiple Travelling Salesman Problem (MD-GmTSP), and three main approaches for solving it are presented: 1) an Optimization approach using a Mixed Integer Programming formulation solved on a CPLEX Solver, 2) a heuristics-based approach using Variable Neighborhood search, and 3) policy modeling using Reinforcement learning. The resulting output is a global mission plan that consists of a target visitation order and feasible orientations at each target for a team of curvature-constrained vehicles. Chapter 3 investigates the problem of finding a collision-free Curvature-constrained Shortest Path …