To obtain real-time interactions in the virtual cockpit system (VCS), a real-time trajectory generation method based on dynamical nonlinear optimization and regression prediction for the haptic feedback manipulator (HFM) is presented in this paper. First, a haptic feedback system based on servoserial manipulator is constructed. Then, the trajectory planning problem for the HFM is formulated as a nonlinear optimization problem to balance the motion time and power consumption and ensure the safety of physical human–robot interactions (pHRI). Multiple optimization problems are solved to generate the optimal database off-line. Finally, the classified multivariate (CM) regression method is presented to learn the database and generate optimal trajectories with arbitrary initial and objective positions on-line. Results show that trajectories with rapidity, safety, and lower power consumption can be generated in real-time by this method, which lay a basis of haptic interactions in the VCS.

Advanced driver assistance systems (ADASs) allow information provision through visual, auditory, and haptic signals to achieve multidimensional goals of mobility. However, processing information from ADAS requires operating expenses of mental workload that drivers incur from their limited attentional resources. The change in driving condition can modulate drivers' workload and potentially impair drivers' interaction with ADAS. This paper shows how the measure of cardiac activity (heart rate and the indexes of autonomic nervous system (ANS)) could discriminate the influence of different driving conditions on drivers' workload associated with attentional resources engaged while driving with ADAS. Fourteen drivers performed a car-following task with visual ADAS in a simulated driving. Drivers' workload was manipulated in two driving conditions: one in monotonous condition (constant speed) and another in more active condition (variable speed). Results showed that drivers' workload was similarly affected, but the amount of attentional resources allocation was slightly distinct between both conditions. The analysis of main effect of time demonstrated that drivers' workload increased over time without the alterations in autonomic indexes regardless of driving condition. However, the main effect of driving condition produced a higher level of sympathetic activation on variable speed driving compared to driving with constant speed. Variable speed driving requires more adjustment of steering wheel movement (SWM) to maintain lane-keeping performance, which led to higher level of task involvement and increased task engagement. The proposed measures appear promising to help designing new adaptive working modalities for ADAS on the account of variation in driving condition.

The software is often responsible for controlling the behavior of mechanical and electrical components, as well as interactions among these components in cyber-physical systems (CPS). The risks in CPS systems could result in losing tools, features, performance and even life. Therefore, safety analysis for software in these systems is a highly critical and serious issue. In general, safety and reliability approaches play a major role in a risk management process in CPS. In this paper, after reviewing the major techniques of software reliability and safety in CPS, an software fault tree analysis (SFTA)-based approach is presented for analysis of operational use-cases (UC) in a CPS system. In our approach, the events related to use-cases are extracted, and the related SFTA is then obtained using the proposed algorithm. Moreover, a semi-automatic method is presented in this paper to produce software failure mode and effects analysis (SFMEA) from SFTA. The results of our approach are applicable for software safety analysis in a real CPS system, including the control system of Iranian National Observatory telescope. Assessment of the suggested method is performed through numerous safety/reliability criteria and the qualitative/quantitative analysis based on these criteria.

Simulation-based methods are emerging to address the challenges of complex systems risk assessment, and this paper identifies two problems related to the use of such methods. First, the methods cannot identify new hazards if the simulation model builders are expected to foresee the hazards and incorporate the abnormal behavior related to the hazard into the simulation model. Therefore, this paper uses the concept of deviation from design intent to systematically capture abnormal conditions that may lead to component failures, hazards, or both. Second, simulation-based risk assessment methods should explicitly consider what expertise is required from the experts that build and use the simulation models—the transfer of the methods to real engineering practice will be severely hindered if they must be performed by persons that are expert in domain safety as well as advanced computer simulation-based methods. This paper addresses both problems in the context of the functional failure identification and propagation (FFIP) method. One industrially established risk assessment method, hazard and operability study (HAZOP), is harnessed to systematically obtain the deviations from design intent in the application under study. An information system presents a user interface that is understandable to HAZOP professionals, so that their inputs are transparently entered to a data model that captures the deviations. From the data model, instructions for configuring FFIP simulation models are printed in a form that is understandable for FFIP experts. The method is demonstrated for discovering a hazard resulting from system-wide fault propagation in a boiling water reactor case.

Computational modeling continues to play an increasingly significant role in the design of more effective vehicle crash safety systems. Researchers now frequently exercise models formulated with sophisticated computer analyses to supplement empirically generated results and conduct extensive what-if exploratory studies for information formerly available solely from costly and time-consuming physical testing. Our research team is developing a modeling and analysis capability that features a scientific visualization toolkit called NYSCEDII CRS visualization module (NCVM). NCVM post-processes conventional crash simulation imagery, enabling modelers to visualize aspects of both the digital model and the sled-test simulation in an immersive visual environment. This paper describes NCVM’s design and development in detail and illustrates its application to a series of sled tests conducted with a recent-production child restraint system and dummy occupant.