The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems

Scenario-based testing for the safety validation of highly automated vehicles is a promising approach that is being examined in research and industry. This approach heavily relies on data from real-world scenarios to derive the necessary scenario information for testing. Measurement data should be collected at a reasonable effort, contain naturalistic behavior of road users and include all data relevant for a description of the identified scenarios in sufficient quality. However, the current measurement methods fail to meet at least one of the requirements. Thus, we propose a novel method to measure data from an aerial perspective for scenario-based validation fulfilling the mentioned requirements. Furthermore, we provide a large-scale naturalistic vehicle trajectory dataset from German highways called highD. We evaluate the data in terms of quantity, variety and contained scenarios. Our dataset consists of 16.5 hours of measurements from six locations with 110 000 vehicles, a total driven distance of 45 000 km and 5600 recorded complete lane changes. The highD dataset is available online at: http://www.highD-dataset.comComment: IEEE International Conference on Intelligent Transportation Systems (ITSC) 201

A Hardware Model Validation Tool for Use in Complex Space Systems

One of the many technological hurdles that must be overcome in future missions is the challenge of validating as-built systems against the models used for design. We propose a technique composed of intelligent parameter exploration in concert with automated failure analysis as a scalable method for the validation of complex space systems. The technique is impervious to discontinuities and linear dependencies in the data, and can handle dimensionalities consisting of hundreds of variables over tens of thousands of experiments

Pose self-calibration of stereo vision systems for autonomous vehicle applications

Nowadays, intelligent systems applied to vehicles have grown very rapidly; their goal is not only the improvement of safety, but also making autonomous driving possible. Many of these intelligent systems are based on making use of computer vision in order to know the environment and act accordingly. It is of great importance to be able to estimate the pose of the vision system because the measurement matching between the perception system (pixels) and the vehicle environment (meters) depends on the relative position between the perception system and the environment. A new method of camera pose estimation for stereo systems is presented in this paper, whose main contribution regarding the state of the art on the subject is the estimation of the pitch angle without being affected by the roll angle. The validation of the self-calibration method is accomplished by comparing it with relevant methods of camera pose estimation, where a synthetic sequence is used in order to measure the continuous error with a ground truth. This validation is enriched by the experimental results of the method in real traffic environments.This work was supported by the Spanish Government through the CICYTprojects (TRA2013-48314-C3-1-R and TRA2015-63708-R) and Comunidad de Madrid through SEGVAUTO-TRIES (S2013/MIT-2713).Publicad

Translating expert system rules into Ada code with validation and verification

The purpose of this ongoing research and development program is to develop software tools which enable the rapid development, upgrading, and maintenance of embedded real-time artificial intelligence systems. The goals of this phase of the research were to investigate the feasibility of developing software tools which automatically translate expert system rules into Ada code and develop methods for performing validation and verification testing of the resultant expert system. A prototype system was demonstrated which automatically translated rules from an Air Force expert system was demonstrated which detected errors in the execution of the resultant system. The method and prototype tools for converting AI representations into Ada code by converting the rules into Ada code modules and then linking them with an Activation Framework based run-time environment to form an executable load module are discussed. This method is based upon the use of Evidence Flow Graphs which are a data flow representation for intelligent systems. The development of prototype test generation and evaluation software which was used to test the resultant code is discussed. This testing was performed automatically using Monte-Carlo techniques based upon a constraint based description of the required performance for the system

Modeling of macro fiber composite actuated laminate plates and aerofoils

© 2019 Sage Publications . The final, definitive version of this paper has been published in the Journal of Intelligent Material Systems and Structures by Sage Publications Ltd. All rights reserved. It is available at: https://doi.org/10.1177/1045389X19888728This article investigates the modeling of macro fiber composite-actuated laminate plates with distributed actuator patches. The investigation details an analytical and finite element modeling, with experimental validation of the bending strain and deflection of an epoxy E-glass fiber composite laminate. An analytical approach is also developed to estimate the plate deflection from the experimental strain measurements. The analytical method uses direct integration of single dimensional plate bending moments obtained by strain-induced shear moments from the macro fiber composite actuators. Finite element analysis software was used with the composite laminate modeled in ANSYS ACP. The results from both analytical and numerical models show good agreement with the experimental results, with strain values agreeing within 20 ppm and the maximum difference in deflection not exceeding 0.1 mm between models. Finally, an application of the analytical model for developing morphing aerofoil designs is demonstrated.Peer reviewe

PROCESS CONTROL SYSTEM IDENTIFICATION

System identification is a method for generating workable dynamic response models based on an observed dataset from an actual system. It is used to give the input-output relationship of the dynamic response. The objective of this project is to design and implement System Identification for Liquid System Pilot Plant. The project will also make comparisons between the conventional and intelligent modeling technique. The project concentrates on the conventional technique known as empirical modeling and intelligent modeling by means of System Identification Toolbox. In empirical model building, models are determines by making small changes in the input variable about a nominal operating condition. The model developed by using this method provides the dynamic relationship between selected input and output variables. Matlab providesthe SystemIdentification Toolboxthat helps to ensure the observedtest data represents the dynamics of the system under investigation. It provides tools for creating mathematical models ofdynamic systems based on the observed input-output data. For the intelligent technique, two model predictors, ARX and ARMAX, are used to obtain the best model. From the analysis, it shows that the ARX models exhibit quite the same characteristics as the models obtained from the empirical technique. By using the System Identification Toolbox, the ARMAX structures are the best models in representing the actual system. After model validation tests, all models from both the conventional and intelligent technique are capable of reproducing observed data with minimum predictive error

Cyber-physical energy systems modeling, test specification, and co-simulation based testing

The gradual deployment of intelligent and coordinated devices in the electrical power system needs careful investigation of the interactions between the various domains involved. Especially due to the coupling between ICT and power systems a holistic approach for testing and validating is required. Taking existing (quasi-) standardised smart grid system and test specification methods as a starting point, we are developing a holistic testing and validation approach that allows a very flexible way of assessing the system level aspects by various types of experiments (including virtual, real, and mixed lab settings). This paper describes the formal holistic test case specification method and applies it to a particular co-simulation experimental setup. The various building blocks of such a simulation (i.e., FMI, mosaik, domain-specific simulation federates) are covered in more detail. The presented method addresses most modeling and specification challenges in cyber-physical energy systems and is extensible for future additions such as uncertainty quantification

Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor

Identification of human movements is crucial for the design of intelligent devices capable to provide assistance. In this work, a Bayesian formulation, together with a sequential analysis method, is presented for identification of sit-to-stand (SiSt) and stand-to-sit (StSi) activities. This method performs autonomous iterative accumulation of sensor measurements and decision-making processes, while dealing with noise and uncertainty present in sensors. First, the Bayesian formulation is able to identify sit, transition and stand activity states. Second, the transition state, divided into transition phases, is used to identify the state of the human body during SiSt and StSi. These processes employ acceleration signals from an inertial measurement unit attached to the thigh of participants. Validation of our method with experiments in offline, real-time and a simulated environment, shows its capability to identify the human body during SiSt and StSi with an accuracy of 100% and mean response time of 50 ms (5 sensor measurements). In the simulated environment, our approach shows its potential to interact with low-level methods required for robot control. Overall, this work offers a robust framework for intelligent and autonomous systems, capable to recognise the human intent to rise from and sit on a chair, which is essential to provide accurate and fast assistance