The area of intelligent autonomous vehicles or robots has proved to be very active and extensive both in challenging applications as well as in the source of theoretical development. Automation technology is rapidly developing in many areas including: agriculture, mining, traditional manufacturing, automotive industry and space exploration. The 2nd IFAC Conference on Intelligent Autonomous Vehicles 1995 provides the forum to exchange ideas and results among the leading researchers and practitioners in the field. This publication brings together the papers presented at the latest in the series and provides a key evaluation of developments in automation technologies.

When human drivers let intelligent software take the wheel: the beginning of a new era in personal mobility.

The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.

This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of “autonomous driving".

This thesis is concerned with the theoretical and practical development of reliable and robust localisation algorithms for autonomous land vehicles operating at high speeds in unstructured, expansive and harsh environments. Localisation is the ability of a vehicle to determine its position and orientation within an operating environment. The need for such a localisation system is motivated by the requirement of developing autonomous vehicles in applications such as mining, agriculture and cargo handling. The main drivers in these applications are for safety, efficiency and productivity. The approach taken to the localisation problem in this thesis guarantees that the safety and reliability requirements imposed by such applications are achieved. The approach also aims to minimise the engineering or modification of the environment, such as adding artificial landmarks or other infrastructure. This is a key driver in the practical implementation of a localisation algorithm. In pursuit of these objectives, this thesis makes the following principal contributions: 1. The development of an Iterative Closest Point - Extended Kalman Filter (ICP-EKF) algorithm - a map-based iconic algorithm that utilises measurements from a scanning laser rangefinder to achieve localisation. The ICP-EKF algorithm entails the development of a map-building algorithm. The main attraction of the map-based localisation algorithm is that it works directly on sensed data and thus does not require extraction and matching of features. It also explicitly takes into account the uncertainty associated with measurements and has the ability to include measurements from a variety of different sensors. 2. The development and implementation of an entropy-based metric to evaluate the information content of measurements. This metric facilitates the augmentation of landmarks to the ICP-EKF algorithm thus guaranteeing reliable and robust localisation. 3. The development and adaptation of a view-invariant Curvature Scale Space (CSS) landmark extraction algorithm. The algorithm is sufficiently robust to sensor noise and is capable of reliably detecting and extracting landmarks that are naturally present in the environment from laser rangefinder scans. 4. The integration of the information metric and the CSS and ICP-EKF algorithms to arrive at a unified localisation framework that uses measurements from both artificial and natural landmarks, combined with dead-reckoning sensors, to deliver reliable vehicle position estimates. The localisation framework developed is sufficiently generic to be used on a variety of other autonomous land vehicle systems. This is demonstrated by its implementation using field data collected from three different trials on three different vehicles. The first trial was carried out on a four-wheel drive vehicle in an underground mine tunnel. The second trial was conducted on a Load-Haul-Dump (LHD) truck in a test tunnel constructed to emulate an underground mine. The estimates of the proposed localisation algorithms are compared to the ground truth provided by an artificial landmark-based localisation algorithm that uses bearing measurements from a laser. To demonstrate the feasibility and reliability of both the natural landmark extraction and localisation algorithms, these are also implemented on a utility vehicle in an outdoor area within the University's campus. The results demonstrate the robustness of the proposed localisation algorithms in producing reliable and accurate position estimates for autonomous vehicles operating in a variety of unstructured domains.

Many robotics researchers consider high-level vision algorithms (computational) too expensive for use in robot guidance. This book introduces the reader to an alternative approach to perception for autonomous, mobile robots. It explores how to apply methods of high-level computer vision and fuzzy logic to the guidance and control of the mobile robot. The book introduces a knowledge-based approach to vision modeling for robot guidance, where advantage is taken of constraints of the robot's physical structure, the tasks it performs, and the environments it works in. This facilitates high-level computer vision algorithms such as object recognition at a speed that is sufficient for real-time navigation. The texts presents algorithms that exploit these constraints at all levels of vision, from image processing to model construction and matching, as well as shape recovery. These algorithms are demonstrated in the navigation of a wheeled mobile robot.

This book surveys the history of automatic vehicle guidance based on the processing of visual information, starting from the very first projects worldwide up to the latest developments. It also presents the ARGO prototype vehicle, developed at the University of Parma (Italy), and describes its equipment, setup, and performance. ARGO has been equipped with cameras and processing systems to drive autonomously in real traffic conditions. The complete system has been tested on public roads, during a tour in which ARGO drove itself along the Italian highway network for more than 2000 km. A detailed analysis of this trip is also included.