Development of unmanned vehicles will increase as the need to save lives rises. In both military and civilian applications, humans can be taken out of the loop through the implementation of safe and intelligent autonomous vehicles. Although hardware and software development continue to play a large role in the autonomous vehicle industry, validation of these systems will always be necessary. The ability to test these vehicles thoroughly and efficiently will ensure their proper and flawless operation. On November 3, 2007 the Defense Advanced Research Projects Agency held the Urban Challenge to drive the development of autonomous ground vehicles for military use. This event required vehicles built by teams across the world to autonomously navigate a 60 mile course in an urban environment in less than 6 hours. This thesis addresses the testing aspect of autonomous ground vehicles that exhibit the advanced behaviors necessary for operating in such an event. Specifically, the experiences of Team Victor Tango and other Urban Challenge teams are covered in detail. Testing facilities, safety measures, procedures, and validation methods utilized by these teams provide valuable information on the development of their vehicles. Combining all these aspects results in a proposed testing strategy for autonomous ground vehicles. / Master of Science

Autonomous ground vehicle operations, such as those found in the 2007 DARPA Urban Challenge, require a reliable and capable vehicle platform. To meet this requirement, an autonomous ground vehicle platform based on a 2005 Ford Escape Hybrid was developed for operations in urban environments. The vehicle conversion, dubbed Odin, contains a drive-by-wire system that is highly integrated with the OEM systems, providing throttle, steering, shifting, and braking actuation. The vehicle also includes a controller that provides low-level longitudinal using a map-linearized PI controller and lateral curvature control using a bicycle model. The control algorithms proved capable of controlling the vehicle at a level acceptable for autonomous operations. Communications are implemented using the Joint Architecture for Unmanned Systems (JAUS) using custom messages to enhance interoperability potential. The net result is a highly capable autonomous vehicle platform that was validated when Odin successfully completed the 60 mile Urban Challenge. / Master of Science

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Research in mobile robotics, unmanned systems, and autonomous man-portable vehicles has grown rapidly over the last decade. This push has taken the problems of robot cognition and behavioral control out of the lab and into the field. Two good examples of this are the DARPA Urban Challenge autonomous vehicle race and the RoboCup robot soccer competition. In these challenges, a mobile robot must be capable of completing complex, sophisticated tasks in a dynamic, partially observable and unpredictable environment. Such conditions necessitate a behavioral programming approach capable of performing high-level action selection in the presence of multiple goals of dynamically changing importance, and noisy, incomplete perception data. In this thesis, an approach to behavioral programming is presented that provides the designer with an intuitive method for building contextual intelligence while preserving the qualities of emergent behavior present in traditional behavior-based programming. This is done by using a modified hierarchical state machine for behavior arbitration in sequence with a command fusion mechanism for cooperative and competitive control. The presented approach is analyzed with respect to portability across platforms, missions, and functional requirements. Specifically, two landmark case-studies, the DARPA Urban Challenge and the International RoboCup Competition are examined. / Master of Science

The field of autonomous vehicle research is growing rapidly. The Congressional mandate for the military to use unmanned vehicles has, in large part, sparked this growth. In conjunction with this mandate, DARPA sponsored the Urban Challenge, a competition to create fully autonomous vehicles that can operate in urban settings. An extremely important feature of autonomous vehicles, especially in urban locations, is their ability to perceive their environment. The research presented in this thesis is directed toward providing an autonomous vehicle with real-time data that efficiently and compactly represents its forward environment as it navigates an urban area. The information extracted from the environment for this application consists of stop line locations, lane information, and obstacle locations, using a single camera and LIDAR scanner. A road/non-road binary mask is first segmented. From the road information in the mask, the current traveling lane of the vehicle is detected using a minimum distance transform and tracked between frames. The stop lines and obstacles are detected from the non-road information in the mask. Stop lines are detected using a variation of vertical profiling, and obstacles are detected using shape descriptors. A laser rangefinder is used in conjunction with the camera in a primitive form of sensor fusion to create a list of obstacles in the forward environment. Obstacle boundaries, lane points, and stop line centers are then translated from image coordinates to UTM coordinates using a homography transform created during the camera calibration procedure. A novel system for rapid camera calibration was also implemented. Algorithms investigated during the development phase of the project are included in the text for the purposes of explaining design decisions and providing direction to researchers who will continue the work in this field. The results were promising, performing the tasks fairly accurately at a rate of about 20 frames per second, using an Intel Core2 Duo processor with 2 GB RAM.

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There are many forms of speed control for an autonomous ground vehicle currently in development. Most use a simple PID controller to achieve a speed specified by a higher-level motion planning algorithm. Simple controllers may not provide a desired acceleration profile for a ground vehicle. Also, without extensive tuning the PID controller may cause excessive speed overshoot and oscillation. This paper examines an approach that was designed to allow a greater degree of control while reducing the computing load on the motion planning software. The SpAM+PI (Speed-based Acceleration Map + Proportional Integral controller) algorithm outlined in this paper uses three inputs: current velocity, desired velocity and desired maximum acceleration, to determine throttle and brake commands that will allow the vehicle to achieve its correct speed. Because this algorithm resides on an external controller it does not add to the computational load of the motion planning computer. Also, with only two inputs that are needed only when there is a change in desired speed or maximum desired acceleration, network traffic between the computers can be greatly reduced. The algorithm uses splines to smoothly plan a speed profile from the vehicle's current speed to its desired speed. It then uses a lookup table to determine the correct pedal position (throttle or brake) using the current vehicle speed and a desired instantaneous acceleration that was determined in the splining step of the algorithm. Once the pedal position is determined a PI controller is used to minimize error in the system. The SpAM+PI approach is a novel approach to the speed control of an autonomous vehicle. This academic experiment is tested using Odin, Team Victor Tango's entry into the 2007 DARPA Urban Challenge which won 3rd place and a $500,000 prize. The evaluation of the algorithm exposed both strengths and weaknesses that guide the next step in the development of a speed control algorithm. / Master of Science