A load-independent constant current (CC)-constant voltage (CV) output is an important requirement of inductive power transfer (IPT) systems for electric vehicle charging applications. Zero phase angle (ZPA) is also a desirable feature, to ensure a lower power rating requirement for the switching converter. CC and CV output along with ZPA can be achieved by using a suitable compensation topology. Equation manipulation techniques can be used for designing the compensation topology. But, it can be mathematically intensive especially for higher order topologies. To overcome this problem, resonant-tank based approaches are adopted in several works to achieve CC and CV conditions. However, equation-based approaches are depended upon either wholly or partly for realizing ZPA. This approach can be tedious and lacks physical insight. The proposed method extends resonant tank approach to achieve ZPA also, besides CC and CV. The need for a separate method to achieve ZPA is eliminated. Further, it simplifies the process in arriving at the constraints that ensure ZPA. As a sample validation, the proposed method is applied to a S-SP compensation topology. The CC-ZPA and CV-ZPA constraints for the S-SP topology are shown to be in line with the ones arrived at using an existing equation-based impedance approach. The simplicity of the proposed method can be observed from the sample validation.
This paper is a continuation on my revolutionary theory of solving the pointwise fluid flow approximation model for time-varying queues. Thus, the long-standing simulative approach has now been replaced by an exact solution by using a constant ratio 𝛽 (Ismail's ratio) , offering an exact analytical solution. The stability dynamics of the time-varying 𝑀/𝐸 𝑘 /1 queueing system are then examined numerically in relation to time, 𝛽, and the queueing parameters.
This paper details an experiment utilizing ESP8266 modules as servers to wirelessly control diverse electrical appliances in home automation. The experiment showcased the modules' capability to respond to commands via a web interface on both mobile and desktop platforms or even tablets. While most of the experiment ran smoothly, occasional freezing and connectivity disruptions were observed. The abstract encapsulates the experiment's successes, discusses encountered challenges, and outlines a forward-looking perspective, including the integration of a custom PCB for enhanced system stability.
In this work, the problem of predicting a pedestrian’s intention to cross the road is addressed using visual data captured from a camera. The proposed ROS-based modular architecture consists of four modules starting with the Visual-Perception, Intention Prediction, and the Planning and Control Modules. The visual perception is further divided into three sub-modules. First, pedestrian detection is responsible for detecting the pedestrian and analyzing his state using motion and looking classifiers. Secondly, the detection of the lane that is responsible for analyzing the structured environment which helps in the road state classifiers. The third sub-module aims to extract some curvilinear localization states that are essential for the vehicle’s motion planning and control. The intention prediction module is integrated to capture the pedestrian’s intention to cross the road. In this module, a comparative study is conducted between three different data-driven sequential models. Each model is trained on the JAAD dataset and different extracted features form the visual perception module. It is observed that the proposed GRU model obtained 86% average f1-score, and can predict a pedestrian’s intention three seconds before crossing. In order to control the maneuver of the vehicle, the Proportional-Integral (PI) controller is implemented for longitudinal velocity control to brake the vehicle to avoid collision with the pedestrian, and a Linderoth controller is used to control the lateral motion of the vehicle. Finally, this work is verified on a 1:4 scaled real vehicle to ensure the applicability of implementing this work in real hardware.
Emotion monitoring in driving is important. Emotions can affect attention, memory, and decision-making and have a significant impact on our driving behaviors and safety. However, measuring and interpreting emotions is challenging: The same emotion can have different manifestations and different emotions can have similar manifestations. Contextualizing emotions can help with the interpretation and translation of emotional states. However, research on context and drivers' emotional states is limited. We investigate the effect of time, area, weather, surrounding conditions, and traffic conditions on drivers' emotions. Sixty-four images of various driving scenarios were generated using DALL•E 2, a generative AI model, and 238 participants were recruited through Prolific to respond how they would feel driving in such contexts. The results showed that rainy weather, tumultuous surrounding, and high traffic conditions were associated with an increase in negative emotions. On the other hand, driving in rural areas, in the morning time or with no traffic increased the intensity of positive emotions, while rainy weather conditions increase the intensity of negative emotions. The findings can guide the development of driver monitoring systems with respect to the effect of driver's emotional states.
This work develops a methodology for studying the effect of an offload zone on the ambulance ramping problem using a multi-server, multi-class non-preemptive priority queueing model that can be treated analytically. A prototype model for the ambulance/emergency-department interface is constructed, which is then implemented as a formal discrete event simulation, and is run as a regenerative steady-state simulation for empirical estimation of the ambulance queue-length and waiting-time distributions. The model is also solved by analytical means for explicit and exact representations of these distributions, which are subsequently tested against simulation results. A number of measures of performance is extracted, including the mean and 90th percentiles of the ambulance queue length and waiting time, as well as the average number of ambulance days lost per month due to offload delay (offload delay rate). Various easily computable approximations are proposed and tested. In particular, a closed-form, purely algebraic expression that approximates the dependence of the offload delay rate on the capacity of the offload zone is proposed. It can be evaluated directly from model input parameters and is found to be, for all practical purposes, indistinguishable from the exact result.
The proliferation of computing applications in Edge devices emphasizes the need for efficient and accurate Deep Learning (DL) models, especially in safety applications like Driver Distraction Detection (DDD). However, DL's substantial computational requirements hinder deployment in resourceconstrained environments like vehicles. This paper introduces a differentiable architecture search method for optimal and resource-conscious neural network architecture design. We integrate edge-related constraints in a multi-objective function. We investigate Pareto optimality to explore a diverse set of solutions that cover a spectrum of trade-offs and objectives, rather than a single, narrowly optimized solution. We specifically tailor the model design to target a predetermined computational budget in terms of inference time and model size. The proposed method has been evaluated for DDD using 2 benchmark datasets, namely, SFD and AUCD2, and deployed on a spectrum of devices (workstation, embedded system, and mobile devices). We obtained detection accuracies of 98.17% and 95.80% on SFD and AUCD2, respectively, while significantly reducing model sizes to 0.25 MB and 0.36 MB and inference latency to 3 ms and 4 ms on Nvidia Jetson Xavier NX. Additionally, we achieve almost an order of magnitude fewer parameters (0.06M and 0.09M) compared to state-of-the-art.
The development of automated and connected driving functions is currently a central objective for vehicle manufacturers. Such functions generally are introduced at different levels of automation with limited operational design domains (ODDs), which is gradually extended. However, a concise and practical description of ODDs has not yet been established. This work aims at providing a suitable and mathematically concise description of the operational design domain and relates the new description to the definitions of related terms that are widely used in the research community. This work follows a top-down approach. Engineering applications for ODD descriptions are introduced that go beyond scenario-based test design, like ADAS specification, function delimitation and cooperative, connected mobility. Furthermore the ODD can be seen as an instrument and language for the description of system capabilities, building a fundamental tool for cooperative and collaborative development and operations. A set of requirements on the parameterization of operational design domains is derived and methods for selecting suitable parameters are presented. The application of these methods is demonstrated with real world examples. Finally, a discussion of open issues provides starting points for continuous further research.
Assisted and automated driving functions will rely on machine learning algorithms, given their ability to cope with real world variations, e.g. vehicles of different shapes, positions, colours, etc. Supervised learning needs annotated datasets, and several datasets are available for developing automotive functions. However, these datasets are tremendous in volume, and labelling accuracy and quality can vary across different datasets and within dataset frames. Accurate and appropriate ground truth is especially important for automotive, as "incomplete" or "incorrect" learning can impact negatively vehicle safety when these neural networks are deployed. This work investigates ground truth quality of widely adopted automotive datasets, including a detailed analysis of KITTI MoSeg. According to the identified and classified errors in the annotations of different automotive datasets, this paper provides three different criteria collections for producing improved annotations. These criteria are enforceable and applicable to a wide variety of datasets. The three annotations sets are created to: (i) remove dubious cases; (ii) annotate to the best of human visual system; (iii) remove clear erroneous bounding boxes. KITTI MoSeg has been reannotated three times according to the specified criteria, and three state-of-the-art deep neural network object detectors are used to evaluate them. The results clearly show that network performance is affected by ground truth variations, and removing clear errors is beneficial for predicting real world objects only for some networks. The relabelled datasets still present some cases with "arbitrary"/"controversial" annotations, and therefore this work concludes with some guidelines related to dataset annotation, metadata/sub-labels, and specific automotive use-cases.
With a global energy system in rapid transformation from fossil fuels, Green Hydrogen is one of few solutions to hard-to-abate emissions within industry. While most hydrogen projects are in a planning phase, the Ovako hydrogen facility in Hofors, with a scrap-based Electric Arc Furnace process, was inaugurated in September 2023. This project studies wider system benefits of the electrolyser such as power grid support, oxygen byproduct, providing hydrogen to external actors, and district heating. This is analysed both with current capacity and in regards to possible future development. Replacing fossil fuel with hydrogen produced by an atmospheric alkaline electrolyser is an indirect electrification with the potential to decrease Green House Gas emissions. Industry-wide electrification increases the demand for electricity, affecting all existing users. Therefore, system benefits and sector couplings such as enabling ancillary services to the grid, producing low-marginal cost hydrogen for hydrogen-powered trucks, and using waste heat for district heating, are important to ascertain system-wide improvements.
Grid-tie voltage source converters (VSCs) can operate in three distinct modes: AC-dominant, DC-dominant, and balanced, depending on the placement of the stiff voltage sources. The varying operation modes of the VSCs traditionally demand different synchronization control techniques, posing challenges for the power systems to accommodate and coordinate the VSCs. A promising universal control technique for VSCs is the DC-link voltage synchronization control (DVSC) based on a lead compensator (LC). The LC DVSC stabilizes both the DC and AC voltages of a VSC while achieving synchronization with the AC grid. This results in a dual-port grid-forming (DGFM) characteristic for the VSC. However, there has been very limited study on the stability and synchronization controller design of the VSCs with the LC DVSC operating in various modes. To bridge this gap, the paper presents a quantitative analysis on the stability and steady-state performance of the LC DVSC in all three operation modes of the DGFM VSC. Based on the analysis, the paper provides step-by-step design guidelines for the LC DVSC. Furthermore, the paper uncovers an instability issue related to the LC DVSC when the DGFM VSC operates in the balanced mode. To tackle the instability issue, a virtual resistance control is proposed and integrated with the LC DVSC. Simulation results validate the analysis and demonstrate the effectiveness of the DGFM VSC with LC DVSC designed using the proposed guidelines in all three operation modes. Overall, the paper demonstrates the feasibility of employing the DGFM VSC with the LC DVSC for all three possible operation modes.
Effective obstacle detection and avoidance play pivotal roles in the implementation of autonomous navigation systems. While numerous authors have addressed obstacle avoidance for single unicycles and car-like vehicles, this work extends the scope to encompass generalised N-trailer vehicles, consisting of a single active segment pulling an arbitrary number of trailers. In contrast to treating obstacles as hard constraints or barrier functions, we introduce a unique approach by modelling them as soft constraints. Gaussian functions are seamlessly integrated into the objective function of the model predictive controller, preserving the convexity of the search space and significantly alleviating computational demands. Although this strategy allows regions occupied by obstacles to remain viable for navigation, we counteract this by thoughtfully designing the amplitude of the Gaussian function. This design is influenced by various components within the formulation, discouraging navigation through obstacle-occupied spaces. The effectiveness of this approach is substantiated through a series of simulated and field experiments involving a tractor pulling two trailers. These experiments showcase the method’s proficiency in navigating around obstacles while maintaining computational efficiency, thereby affirming its practical viability in real-world scenarios.
In the last 5 years, the availability of large audio datasets in African countries has opened unlimited opportunities to build machine intelligence (MI) technologies that are closer to the people and speak, learn, understand, and do businesses in local languages, including for those who cannot read and write. Unfortunately, these audio datasets are not fully exploited by current MI tools, leaving several Africans out of MI business opportunities. Additionally, many state-of-the-art MI models are not culture-aware, and the ethics of their adoption indexes are questionable. The lack thereof is a major drawback in many applications in Africa. This paper summarizes recent developments in machine intelligence in Africa from a multi-layer multiscale and culture-aware ethics perspective, showcasing MI use cases in 54 African countries through 400 articles on MI research, industry, government actions, as well as uses in art, music, the informal economy, and small businesses in Africa. The survey also opens discussions on the reliability of MI rankings and indexes in the African continent as well as algorithmic definitions of unclear terms used in MI.
In the evolving domain of autonomous vehicles, the importance of decision-making cannot be overstated. Deep Reinforcement Learning emerges as a pivotal tool in this landscape. However, traditional DRL algorithms grapple with inaccuracies in Q-value estimation, predominantly due to system noise and function approximation errors. Such inaccuracies, coupled with real-world unpredictabilities, often misdirect autonomous vehicles, jeopardizing safety. This work introduces a novel DRL algorithm tailored for uncertainty and noise-aware decisionmaking in autonomous vehicles. This novel approach harnesses Bayesian Neural Networks (BNN) and skew-geometric Jensen-Shannon divergence, rectifying the aforementioned limitations and also improving exploration. Evaluated in the OpenAI gymnasium environment, the algorithm has clear advantages over contemporary methods in terms of cumulative rewards and convergence speed.
Task-oriented semantic communication is an emerging technology that transmits only the relevant semantics of a message instead of the whole message to achieve a specific task. It reduces latency, compresses the data, and is more robust in low SNR scenarios. This work presents a multitask-oriented semantic communication framework for connected and autonomous vehicles (CAVs). We propose a convolutional autoencoder (CAE) that performs the semantic encoding of the road traffic signs. These encoded images are then transmitted from one CAV to another CAV through satellite in challenging weather conditions where visibility is impaired. In addition, we propose task-oriented semantic decoders for image reconstruction and classification tasks. Simulation results show that the proposed framework outperforms the conventional schemes, such as QAM-16, regarding the reconstructed image's similarity and the classification's accuracy. In addition, it can save up to 89% of the bandwidth by sending fewer bits.
The use of radar sensors in the detection and ranging of targets is an important technology that plays a leading role in the operation of many modern technologies such as the automotive driving assistant systems (ADAS) and the automated driving (AD) technologies. ADAS/AD are technologies that enable unmanned vehicle control along a trajectory. Some of the challenges of using these technologies in vehicles include the risk of misdirection and collision of the vehicle with an obstacle along its trajectory. To avoid these, many technologies such as radar are being used to detect and track targets and trajectories of ADAS/AD vehicles. In this study, we focus on radar tracking technologies and propose a collaborative predictive model in time series, called CoPreMo, for this purpose. We carried out experiments with the model on a simulated radar system to track the range of a target in an ADAS/AD scenario and achieved a range tracking performance that surpasses those of the presented baseline models.
Driving while drowsy may lead to car accidents and other dangerous situations. Since yawning is an obvious sign of drowsiness, it is important to construct a high-accuracy real-time approach to yawning detection. However, existing research on facial keypoint-based segment-level models is still relatively scarce, and not yet comprehensive. Therefore, this paper proposes an approach where the facial keypoints in video clips are first recognized by OpenPose and standardized, and then yawning and other mouth behaviors are detected by our graphtemporal convolutional network (GTCN) model. Extensive experimental results on the public yawning detection dataset YawDD not only reflect the superiority of OpenPose as the facial keypoint extractor and graph convolutional network (GCN) as the spatial feature extractor, but also indicate that the GTCN model achieved a state-of-the-art performance, with 91.73% accuracy on three classifications of normal, talking, and yawning, and 99.25% accuracy on the binary classification problem of yawning detection on the testing set. Experiments also reveal that the GTCN model has good real-time performance in practice.