With an increasing degree of automation, automated vehicle systems become more complex in terms of functional components as well as interconnected hardware and software components. Thus, holistic systems engineering becomes a severe challenge. Emergent properties like system safety are not solely arguable in singular viewpoints such as structural representations of software or electrical wiring (e.g. fault tolerant). This states the need to get several viewpoints on a system and describe correspondences between these views in order to enable traceability of emergent system properties. Today, the most abstract view found in architecture frameworks is a logical description of system functions which structures the system in terms of information flow and functional components. In this article we extend established system viewpoints towards a capability-based assessment of an automated vehicle and conduct an exemplary safety analysis to derive behavioral safety requirements. These requirements can afterwards be attributed to different viewpoints in an architecture frameworks and thus be integrated into a development process for automated vehicles.
Bergmiller @cite_24 uses the concept of capabilities to describe and monitor the performance of the over-actuated by-wire demonstration vehicle . The work details movement capabilities with concepts of sports psychology and introduces a fuzzy based framework to determine the actual performance of actuation system and outlines the possibilities of functional redundancies in case of actuator errors or failures.
@cite_24
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In this paper, we focus on the problem of motion tracking in unknown environments using visual and inertial sensors. We term this estimation task visual-inertial odometry (VIO), in analogy to the well-known visual-odometry problem. We present a detailed study of extended Kalman filter (EKF)-based VIO algorithms, by comparing both their theoretical properties and empirical performance. We show that an EKF formulation where the state vector comprises a sliding window of poses (the multi-state-constraint Kalman filter (MSCKF)) attains better accuracy, consistency, and computational efficiency than the simultaneous localization and mapping (SLAM) formulation of the EKF, in which the state vector contains the current pose and the features seen by the camera. Moreover, we prove that both types of EKF approaches are inconsistent, due to the way in which Jacobians are computed. Specifically, we show that the observability properties of the EKF's linearized system models do not match those of the underlying system, which causes the filters to underestimate the uncertainty in the state estimates. Based on our analysis, we propose a novel, real-time EKF-based VIO algorithm, which achieves consistent estimation by (i) ensuring the correct observability properties of its linearized system model, and (ii) performing online estimation of the camera-to-inertial measurement unit (IMU) calibration parameters. This algorithm, which we term MSCKF 2.0, is shown to achieve accuracy and consistency higher than even an iterative, sliding-window fixed-lag smoother, in both Monte Carlo simulations and real-world testing. This paper reports on the system design for integrating the various processes needed for end-to-end implementation of a smart assistive robotic manipulator. Specifically, progress is reported in the empowerment of the UCF-MANUS system with a suite of sensory, computational, and multimodal interface capabilities so that its autonomy can be made accessible to users with a wide range of disabilities. Laboratory experiments are reported to demonstrate the ability of the system prototype to successfully and efficiently complete object retrieval tasks. Benchmarking of the impact of the various interface modalities on user performance is performed via empirical studies with healthy subjects operating the robot in a simulated instrumental activities of daily living tasks setup. It is seen through a analysis of the collected quantitative data that the prototype is interface neutral and shows robustness to variations in the tasks and the environment. It is also seen that the prototype autonomous system is quantitatively superior to Cartesian control for all tested tasks under a “number of commands” metric, however, under a “time to task completion” metric, the system is seen to be superior for “hard” tasks but not for “easy” tasks. This book presents approaches to address key challenges based on a vehicle level view and with a special emphasis on Drive-by-Wire systems. The design and testing of modern vehicle electronics are becoming more and more demanding due to increasing interdependencies among components and the safety criticality of tasks. The development towards Drive-by-Wire functionalities in vehicles with multiple actuators for vehicle control further increases the challenge. The book explicitly takes into account the interactions between components and aims at bridging the gap between the need to generate additional customer benefits and the effort to achieve functional safety. The book follows a twofold approach: on the one side, it presents a toolchain to support efficient further development of novel functionalities for Drive-by-Wire vehicles. The toolchain comprises appropriate software tools and scaled and full-scale experimental vehicles. On the other side, development towards functionally safe and flexible Drive-by-Wire vehicles is addressed by proposing a top-down designed architecture for vehicle electronics that is enabled by suitable mechanisms. The resulting goal achievement with regard to functional safety is evaluated based on a novel hierarchical approach We present a task-parameterized probabilistic model encoding movements in the form of virtual spring-damper systems acting in multiple frames of reference. Each candidate coordinate system observes a set of demonstrations from its own perspective, by extracting an attractor path whose variations depend on the relevance of the frame at each step of the task. This information is exploited to generate new attractor paths in new situations (new position and orientation of the frames), with the predicted covariances used to estimate the varying stiffness and damping of the spring-damper systems, resulting in a minimal intervention control strategy. The approach is tested with a 7-DOFs Barrett WAM manipulator whose movement and impedance behavior need to be modulated in regard to the position and orientation of two external objects varying during demonstration and reproduction.