Grid-based Environment Estimation for Local Autonomous Vehicle Navigation

Produktbeschreibung

This dissertation is focused on the environment model for automated vehicles. A reliable model of the local environment available in real-time is a prerequisite to enable almost any useful ­activity performed by a robot, such as planning motions to fulfill tasks. It is particularly important in safety critical applications, such as for autonomous vehicles in regular traffic. In this thesis, novel concepts for local mapping, tracking, the detection of principal moving directions, cost evaluations in motion planning, and road course estimation have been developed. An object- and sensor-independent grid representation forms the basis of all presented methods enabling a generic and robust estimation of the environment. All approaches have been evaluated with sensor data from real road scenarios, and their performance has been experimentally demonstrated with a test vehicle.

Keywords: Autonomous Vehicles, Environment Model, Grids, Mapping, Tracking, Navigation, Road Boundary, Collision Checking and Cost Evaluations, Laser Scanners, Radar Sensors, Autonomous Vehicles, Environment Model, Grids, Mapping, Tracking, Navigation, Road Boundary, Collision Checking and Cost Evaluations, Laser Scanners, Radar Sensors