Autonomous Car

Abstract

This dissertation describes a simulated autonomous car capable of driving on urban- style roads. The system is built around TORCS, an open source racing car simulator. Two real-time solutions are implemented; a reactive prototype using a neural network and a more complex deliberative approach using a sense, plan, act architecture. The deliberative system uses vision data fused with simulated laser range data to reliably detect road markings. The detected road markings are then used to plan a parabolic path and compute a safe speed for the vehicle. The vehicle uses a simulated global positioning/inertial measurement sensor to guide it along the desired path with the throttle, brakes, and steering being controlled using proportional controllers. The vehicle is able to reliably navigate the test track maintaining a safe road position at speeds of up to 40km/h.