<p>17721 TeamXAR: Developing an Autonomous Vehicle as Part of the DARPA Grand Challenge – Sensor Research Authors: Philip Schlesinger, Anton Popov, Paul Bobba, Lorraine Kan, Hussien Sleiman, Adrian Sugandhi, Titus Sanchez, Peng Sun, Hrayr Artunyan, Jordan Levy, Bayan Towfiq, Theresia Olson, Della Halim, Jonathan Chow, Harry Mulyanto, Navid Azimi Mentors: Professors Crista Lopes and Tony Givargis, ICS</p><p>To foster the advancement of autonomously driven vehicles, DARPA offered a Grand Challenge to the technical community: to design and implement a vehicle that can drive itself without human help through 175 miles of desert while avoiding various natural and man-made obstacles. In ICS, we have formed a team for studying the problems of this challenge, and for trying to solve some of them. The team is divided into three groups. Part of the "challenge" is to build a system where the vehicle can choose a path to its next interim waypoint based on a list of detected obstacles and the vehicle’s known position, heading, and attitude. With that in mind, as part of our feasibility study using a model car, we have researched options to scale down the algorithm irrespective of hardware. We have developed a simulation system to test the various algorithms and communication systems. The simulation provides a 3D terrain with user definable obstacles and a vehicle. The vehicle gathers obstacle information from the terrain and transmits it to an AI (artificial intelligence) algorithm that resides on a separate machine. The AI is a neural network implementation that takes in the obstacle data and issues driving commands back to the simulation; it was trained by familiarizing the algorithm with the appropriate response to given obstacles. With this research, we will be able to build the central decision-making component of the autonomous vehicle and we will be able to test new algorithms without hardware constraints. </p>