We report on the development of the LISA Technology Package (LTP) experiment that will fly on board the LISA Pathfinder mission of the European Space Agency in 2008. We first summarize the science rationale of the experiment aimed at showing the operational feasibility of the so called Transverse-Traceless coordinate frame within the accuracy needed for LISA. We then show briefly the basic features of the instrument and we finally discuss its projected sensitivity and the extrapolation of its results to LISA.

This paper presents a novel approach to the assessment of the manoeuvrability of vehicles which is not based on the simulation of open-loop manoeuvres, nor does it rely on the modelling of the driver as a control system. Instead, the essence of the method is the solution of a two-point optimal control boundary value problem, in which a vehicle, subject to physical constraints like tyre adherence and road borders, among others, is required to go between given initial and final positions as fast as possible. The control inputs - i.e., the driver's actions - that make the vehicle move between the two states in the most efficient way are found as a part of the solution procedure and represent the actions of a sort of ideal, perfect driver. The resulting motion is called the optimal manoeuvre and, besides being the most efficient way that the given vehicle has for travelling between the two points according to the chosen optimal criterion, may be taken as a reference for meaningful comparisons with other vehic...

This paper describes a novel driver-support system that helps to maintain the correct speed and headway (distance) with respect to lane curvature and other vehicles ahead. The system has been developed as part of the Integrating Project PReVENT under the European Framework Programme 6, which is named SAfe SPEed and safe distaNCE (SASPENCE). The application uses a detailed description of the situation ahead of the vehicle. Many sensors [radar, video camera, Global Positioning System (GPS) and accelerometers, digital maps, and vehicle-to-vehicle wireless local area network (WLAN) connections] are used, and state-of-the-art data fusion provides a model of the environment. The system then computes a feasible maneuver and compares it with the driver's behavior to detect possible mistakes. The warning strategies are based on this comparison. The system “talks” to the driver mainly via a haptic pedal or seat belt and “listens” to the driver mainly via the vehicle acceleration. This kind of operation, i.e., the comparison between what the system thinks is possible and what the driver appears to be doing, and the consequent dialog can be regarded as simple implementations of the rider-horse metaphor (H-metaphor). The system has been tested in several situations (driving simulator, hardware in the loop, and real road tests). Objective and subjective data have been collected, revealing good acceptance and effectiveness, particularly in awakening distracted drivers. The system intervenes only when a problem is actually detected in the headway and/or speed (approaching curves or objects) and has been shown to cause prompt reactions and significant speed correction before getting into really dangerous situations.

This paper presents an experimental investigation of human control of vehicles carried out on the basis of general theories on human movement. The longitudinal and lateral accelerations are studied, and their relations with theories of motor optimality principles, such as minimum jerk, minimum variance, and the two-thirds power law are highlighted. Data have been collected during the final experimental phase of the EU interactIVe project, in which a vehicle developed by Centro Ricerche Fiat has been used to demonstrate driver continuous support produced by an artificial co-driver, within a shared initiative framework. 24 subjects drove the vehicle on a test route twice: once with the system active, the other with the system silent. The test route is composed of urban arterials, extra urban and motorway roads, and takes approximately 40–45 min to be driven. The total database thus amounts to ~35 h of driving data recordings, for a total of ~1.2 M samples per signal. Statistical summary data are presented, which describe human preferred accelerations, correlation between acceleration, curvature, and speed, and between longitudinal and lateral acceleration. Different driving modalities, corresponding to different motor strategies and primitives, are revealed. Comparisons with literature data are also made and discussed. The summary statistics may be useful for the design of future ADAS systems, and indeed they have been collected for the final tuning of the interactIVe co-driver.