Speech-based interfaces can be a promising alternative and/or addition to visual-manual interfaces since they reduce visual-manual distraction while driving. However, there are also findings indicating that speech-based assistants may be a source of cognitive distraction. The aim of this experiment was to quantify drivers' cogni-tive distraction while interacting with speech-based assistants. Therefore, 31 participants performed a simulated driving task and a detection response task (DRT). Concurrently they either sent text-messages via speech-based assistants (Siri, Google Assistant, or Alexa) or completed an arithmetic task (OSPAN). In a multifactorial approach, following Strayer et al. (2017), cognitive distraction was then assessed through performance in the DRT, the driving speed, the task completion time and self-report measures. The cognitive distraction associated with speech-based assistants was compared to the OSPAN task and a baseline condition without a secondary task. Participants reacted faster and more accurately to the DRT in the baseline condition compared to the speech conditions. The performance in the speech conditions was significantly better than in the OSPAN task. However, driving speed did not significantly differ between the experimental conditions. Results from the NASA-TLX indicate that speech-based tasks were more demanding than the baseline but less demanding than the OSPAN task. The task completion times revealed significant differences between speech-based assistants. Sending messages took longest with the Google Assistant. Referring to the findings by Strayer et al. (2017), we conclude that nowadays speech-based assistants are associated with a rather moderate than high level of cognitive distraction. Nonetheless, we point towards the need to assess the effects of human-machine interaction via speech-based interfaces due to their potential for cognitive distraction.
The thermal performance of pulsating heat pipes is based on a self-sustained fluid motion in presence of phase change phenomena. To study this complex coupling of pressure and temperature, the volume-of-fluid method is a widely used approach to capture the phase distribution and interface dynamics, while the Lee model is employed to account for phase change. However, no consensus can be found in literature regarding the film resolution requirements, turbulence or an optimal choice of the mass transfer intensity parameter. In this study, the influence of these factors on pulsating heat pipe simulations is systematically investigated. A closed interface-tracking CFD-VOF method is presented, whereby phase change is only assumed to occur in computational cells within the phase boundaries. First, the model is successfully validated on the one-dimensional analytic sucking interface problem to proove the preservation of supersaturated states and the correct calculation of momentum and energy sources due to phase changes. Subsequently, as a basic reference for the flow in a pulsating heat pipe, the impact of model parameters on a Taylor bubble expanding due to evaporation within a micro-channel is investigated. It has been observed that both, the near wall film discretization and choice of the mass transfer intensity factor are significantly influencing the transferred latent heat and thus, the expansion velocity of the bubble. Finally, the model is extended to account for flow phenomena in an experimental two-turn pulsating heat pipe. The simulation results revealed that it is crucial to adjust the mass transfer intensity factor of condensation in order to achieve viable pressure levels within the closed system. The proposed model was demonstrated to be easily implemented, numerically efficient, and capable of performing in depth two-phase studies.
Numerous challenges in automotive production lead to an increased need for transparency and optimization. Real-Time-Location-Systems (RTLS) is a key tool for achieving this goal. They enable intelligent and automated production processes. Existing solutions such as Ultra-Wideband, Bluetooth-Low-Energy, or Radio-Frequency-Identification have drawbacks in costs, range, or accuracy. 5G is a newly developing standard, recently including high-accuracy positioning. It is unclear, however, if 5G positioning has reached industrial maturity. This contribution aims to determine the state-of-the-art of 5G positioning by performing a systematic literature review and comparing its findings to industrial positioning requirements. 143 articles were analyzed and categorized as: the fundamentals of radio-frequency positioning, an overview of existing solutions, the state-of-the-art, and the maturity of 5G as an industrial positioning system. Results show that, theoretically, centimeter-level accuracies are pursued. However, practical tests are rarely conducted. Concluding, 5G positioning shows great potential, but industrial pilots are required to validate the theoretical characteristics.
We solve robot-trajectory planning problems at industry-relevant scales. Our end-to-end solution integrates highly versatile random-key algorithms with model stacking and ensemble techniques, as well as path relinking for solution refinement. The core optimization module consists of a biased random-key genetic algorithm. Through a distinct separation of problem-independent and problem-dependent modules, we achieve an efficient problem representation, with a native encoding of constraints. We show that generalizations to alternative algorithmic paradigms such as simulated annealing are straightforward. We provide numerical benchmark results for industry-scale data sets. Our approach is found to consistently outperform greedy baseline results. To assess the capabilities of today’s quantum hardware, we complement the classical approach with results obtained on quantum annealing hardware, using qbsolv on Amazon Braket. Finally, we show how the latter can be integrated into our larger pipeline, providing a quantum-ready hybrid solution to the problem.
An improvement in energy efficiency of Battery Thermal Management Systems (BTMS) can increase range and reduce well-to-wheel emissions of Battery Electric Vehicles (BEV). In this work, the potential of a predictive BTMS using Quantile Convolutional Neural Networks (QCNN) was examined. The QCNN provided quantile predictions of battery temperature based on input data from both previous and following drive segments. The predictive control was designed to choose battery cooling thresholds based on a weighted sum of battery cooling, ageing and derating costs derived by the quantile predictions. The predictive BTMS was analyzed concerning its adaptability to different routes ahead, tunability of cost weights as well as robustness to uncertainty of inputs. A setup with unchanged ageing costs reduced average cooling costs by 9% compared to a fixed threshold strategy in a set of 18 scenarios. Simplifications and limitations were discussed to provide a base for further improvements, for example concerning the limited freedom of cooling threshold choice. In conclusion, the developed framework was able to use QCNN predictions to increase the BTMS energy efficiency while taking ageing and derating effects into account.
The market for on-demand mobility services is growing worldwide. These services include, for example, ride-hailing, ride-sharing, and car-sharing. Large-scale fleets of such services collect GPS trajectory (probe vehicle) data constantly everywhere in the network. At a certain penetration rate, this data becomes representative of the entire road network. It can give valuable insights into traffic dynamics and the evolution of congestion. In this paper, we use such GPS trajectory data from Chengdu, China, to investigate the stability and recurrence of macroscopic traffic patterns. Using the two-fluid theory, we find that the two-fluid coefficients are robust on between-day variation, not only supporting the theory itself but also emphasizing that the general evolution of traffic is a robust pattern. We investigate the deviations from the model using time series analysis of the residuals of the two-fluid model. Here, we find evidence for daily and weekly seasonality in the residuals, indicating that congestion patterns are convincingly recurring. These patterns can be used for network-wide traffic state prediction. We conclude that GPS trajectory data from large on-demand mobility fleets is a promising data source for observing traffic patterns in urban road networks once the data becomes representative.
Over decades, many researchers developed complex in-lab systems with the overall goal to track multiple body parts of the user for a richer and more powerful 2D/3D interaction with a distant display. In this work, we introduce a novel smartphone-based tracking approach that eliminates the need for complex tracking systems. Relying on simultaneous usage of the front and rear smartphone cameras, our solution enables rich spatial interactions with distant displays by combining touch input with hand-gesture input, body and head motion, as well as eye-gaze input. In this paper, we firstly present a taxonomy for classifying distant display interactions, providing an overview of enabling technologies, input modalities, and interaction techniques, spanning from 2D to 3D interactions. Further, we provide more details about our implementation—using off-the-shelf smartphones. Finally, we validate our system in a user study by a variety of 2D and 3D multimodal interaction techniques, including input refinement.
Predicting future events accurately is a task of great importance for autonomous vehicles. In this work we focus on lane change events. For this, we propose a novel attention mechanism on top of recurrent neural networks for the prediction task, which improves performance and yields more interpretable models. As critical corner cases are often not considered and reflected in traditional prediction metrics, we additionally introduce a new scenario-based evaluation scheme, which we posit be considered for further maneuver prediction works. Prediction and planning tasks often are correlated, usually sharing input representations and differing in expected outputs and their subsequent consideration. Here, we detail a supporting layer for planning tasks, which analyzes situations w.r.t. their suitability for lane changes and can serve as decision-making support for any planning algorithm. Exploitation of similarities between this task and the aforementioned prediction problem further improves performance of the prediction task, as well as labelling quality of the assessment task. Additionally, we extend our evaluation to urban scenarios, showcasing the generalizability of our proposed prediction models.
The development of Co‐free Li[NixMn1−x]O2 cathodes for lithium‐ion batteries (LIBs) that can supersede Co‐containing Li[NixCoyMn1−x−y]O2 and Li[NixCoyAl1−x−y]O2 cathodes is considered a priority as Co is associated with price volatility, environmental concerns, and human rights violations. However, the complete removal of Co from cathodes for LIBs is difficult because Co‐free cathodes suffer from structural instability and inferior capacity. In this study, a morphology‐engineering approach is used to develop a Co‐free Li[Ni0.9Mn0.1]O2 cathode with a Ni‐rich core–Mn‐rich shell structure to overcome the limitations of Co‐free Li[NixMn1−x]O2 cathodes. The engineered morphology of the Co‐free Li[Ni0.9Mn0.1]O2 cathode particles effectively dissipates internal strain caused by state‐of‐charge heterogeneity and fracture toughening the cathode. Owing to the effective dissipation of internal strain and chemical protection provided by the Mn‐rich shell, the Co‐free cathode demonstrates excellent long‐term cycling stability; it retains 78.5% of its initial capacity after 2000 cycles at 1 C charge and 0.8 C discharge rates, and retains an unprecedented 79.5% after 1000 cycles under fast‐charging conditions (3 C charge and 1 C discharge). The proposed Co‐free layered oxide cathode represents a next‐generation cathode that affords fast‐charging and durable LIBs, which are more cost effective than LIBs featuring commercial Co‐containing cathodes. Nanostructured (Ns) Co‐free Li[Ni0.9Mn0.1]O2 (NM90) cathode deters Li inhomogeneity to prevent mechanical strain accumulation, enabling 79.5% capacity retention after 1000 cycles under fast‐charging condition (i.e., cycling at 3 C charge). Thus, the Ns‐NM90 cathode represents a new class of Co‐free layered oxide cathodes that affords durable, fast‐charging, and thermally stable lithium‐ion batteries, suitable for electric vehicle application, at a low cost compared to conventional Co‐containing Li[NixCoyAl1‐x‐y]O2 or Li[NixCoyMn1‐x‐y]O2 cathodes.
Speech is considered a promising modality for human-machine interaction while driving, especially in reducing visual and manual distraction. However, speech-based user interfaces themselves have shown to increase cognitive distraction. There remains a lack of standardized and unambiguous methods for measuring the impact of speech-based assistants on cognitive distraction while driving. This work aims to investigate whether the combination of the box task and the detection response task (DRT) is a suitable method for assessing the cognitive distraction caused by speech-based assistants. For this purpose, participants (N = 39) engaged in artificial (n-back tasks) and natural speech-based secondary tasks (interaction with Android's Google Assistant and Apple's Siri) differing in predefined levels of cognitive workload while performing the box task and the DRT. The results showed that DRT performance differed between the 0-back and 1-back task but not between the different cognitive workload levels of the speech-based assistants. No clear effects emerged for the box task parameters. Thus, the combination of the box task and DRT is well-suited for measuring cognitive distraction caused by artificial secondary tasks but not by natural interactions with speech-based assistants.
Private permissioned blockchains are deployed in ever greater numbers to facilitate cross-organizational processes in various industries, particularly in supply chain management. One popular example of this trend is Hyperledger Fabric. Compared to public permissionless blockchains, it promises improved performance and provides certain features that address key requirements of enterprises. However, also permissioned blockchains are still not as scalable as centralized systems, and due to the scarcity of theoretical results and empirical data, their real-world performance cannot be predicted with the necessary precision. We intend to address this issue by conducting an in-depth performance analysis of Hyperledger Fabric. The paper presents a detailed compilation of various performance characteristics using an enhanced version of the Distributed Ledger Performance Scan (DLPS). Researchers and practitioners alike can use the various performance properties identified and discussed as guidelines to better configure and implement their Hyperledger Fabric network. Likewise, they are encouraged to use the DLPS framework to conduct their measurements.
Lower body posture influences driving comfort and safety. The posture recommendations from the literature, however, are often based on preferred postures and can be inconsistent due to differences in the experimental setups. Furthermore, the ranges of preferred postures focusing on sitting comfort are often wide and, therefore, difficult to use for occupant packaging in the automotive industry. To cope with these issues, we developed a task-oriented approach to evaluate the lower body driving posture. We predefined 12 seating positions (4 knee angles × 3 seating heights) and measured the physical strain, discomfort perception, and task performance of gas pedal control and emergency braking. Results of the 11 participants showed that seating closer to the front with a 110° knee angle caused more foot dorsiflexion for gas pedal control and increased the right leg's shin muscle activity and discomfort; seating further back with a 145° knee angle decreased emergency braking performance. In conclusion, our new approach is feasible for objectively and effectively evaluating lower body driving posture. Relevance to industry The approach of this study can provide a new perspective on the driving posture assessment. The findings can be utilized to optimize the occupant packaging process in the automotive industry.
The flank load carrying capacity of bevel and hypoid gears is mainly limited by the failure modes pitting, scuffing, tooth flank fracture and the phenomenon micropitting. By application of a standardized calculation method, e.g. according to the international standard ISO 10300:2014, a first estimation of the flank load carrying capacity can be made based on the macro geometry of the bevel or hypoid gear set. According to method B of ISO 10300:2014 the complexity of the real geometry of bevel and hypoid gears is reduced to a virtual cylindrical gear geometry. The load carrying capacity regarding scuffing, micropitting and tooth flank fracture can be determined by using the virtual cylindrical gear geometry along the path of contact. However, the determination of the pitting load carrying capacity is carried out on a single representative point on the path of contact of the virtual cylindrical gear. This paper shows an extended calculation method for the determination of the pitting load carrying capacity of bevel and hypoid gears along the path of contact of the virtual cylindrical gear geometry. Due to the calculation along the path of contact the extended method allows a more precise estimation of the pitting load carrying capacity than the current standard calculation method ISO 10300-2:2014 using the same input data. Within this paper all relevant factors of the extended calculation method are explained in detail. Furthermore, the verification of the extended calculation method with calculation results of an intense validated loaded tooth contact analysis, corresponding to method A of ISO 10300-2:2014, is presented.
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