bluuebunny/crossref_metadata_2025 huggingface dataset:
Mechanoluminescent/Electric Dual‐Mode Sensors Enabled by Trace Carbon Nanotubes
Authors: Zhang Kaiying , Chang Shulong , Shang Yuanyuan , Liu Wenjin , Peng Danni , Deng Yuan , Dai Shuge , Shan Chong‐Xin , Dong Lin
Published: October 2024

Mechanoluminescence (ML)‐based sensors are emerging as promising wearable devices, attracting attention for their self‐powered visualization of mechanical stimuli. However, challenges such as weak brightness, high activation threshold, and intermittent signal output have hindered their development. Here, a mechanoluminescent/electric dual‐mode strain sensor is presented that offers enhanced ML sensing and reliable electrical sensing simultaneously. The strain sensor is fabricated via an optimized dip‐coating method, featuring a sandwich structure with a single‐walled carbon nanotube (SWNT) interlayer and two polydimethylsiloxane (PDMS)/ZnS:Cu luminescence layers. The integral mechanical reinforcement framework provided by the SWNT interlayer improves the ML intensity of the SWNT/PDMS/ZnS:Cu composite film. Compared to conventional nanoparticle fillers, the ML intensity is enhanced nearly tenfold with a trace amount of SWNT (only 0.01 wt.%). In addition, the excellent electrical conductivity of SWNT forms a conductive network, ensuring continuous and stable electrical sensing. These strain sensors enable comprehensive and precise monitoring of human behavior through both electrical (relative resistance change) and optical (ML intensity) methods, paving the way for the development of advanced visual sensing and smart wearable electronics in the future.
A study on bioconvective peristaltic flow of a Casson nanofluid in a porous uniform/ non‐uniform flexible conduit
Authors: Ajithkumar M. , Lakshminarayana P.
Published: October 2024

This investigation presents a unique mathematical model that integrates a comparative study on the Magnetohydrodynamics (MHD) bioconvective peristaltic transport of Casson nanofluid in a porous uniform/non‐uniform elastic channel with wall properties. The effects of thermal radiation, heat source, chemical reaction, partial slip conditions, Soret, and Dufour are analyzed in the flow scenario. The present model inspired numerous implementations in biological engineering, bio‐fertilizers, biofuels, enzymes, solar transceiver systems, heat transfer engineering, power storage units, and so forth. The homotopy perturbation technique (HPM) is employed to provide an analytical solution to the system of simplified non‐dimensional governing equations with the help of a lubrication approach. The influence of different parameters on the axial velocity, temperature, concentration, density of the motile microorganism, heat transfer coefficient, and streamlines are examined through various graphs and tables. According to the observations, an increase in the chemical reaction parameter decreases the density of motile microorganisms. In addition, a comparison is made between a uniform and non‐uniform channel through tabular data and graphical representations. It is detected that the fluid velocity and temperature are increased in the non‐uniform channel when compared to the uniform channel.
Effect of Control Laws for Torque-Vectoring Systems on Steady-State Cornering in Race Cars
Authors: Kobayashi Ikkei , Yoshida Fumiya , Fu Liting , Ebashi Yusuke , Yamada Hayato , Kuroda Jumpei , Uchino Daigo , Ogawa Kazuki , Ikeda Keigo , Kato Taro , Endo Ayato , Peeie Mohamad Heerwan Bin , Kato Hideaki , Narita Takayoshi
Published: October 2024

This paper presents an analysis of the control law for a torque-vectoring system that actively distributes left and right drive torques to maximize the steady-state cornering performance of a rear-wheel drive race car. The control law allocates torque to the left and right vertical load distribution. Steady-state analysis results show that the maximum lateral acceleration could be improved by 7.8% by decreasing the maximum slip ratio and altering the point at which the yaw moment is trimmed compared to the existing passive system. Furthermore, the stability of the system is reduced, and measures to ensure stability are presented.
Vehicle State Estimation Through Dynamics Modeled Factor Graph
Authors: Dahal Pragyan , Arrigoni Stefano , Bijelic Mario , Braghin Francesco
Published: October 2024

Ego Vehicle state estimation is integral to every autonomous driving software stack. Thereby, the estimation of the state and its components as for example the side slip angle, is a crucial component to track the vehicle maneuvers. In the absence of a direct sensor measuring side slip angle, most of the existing literature either use observers like Kalman Filters or non-modular factor graphs by modeling lateral dynamics. However, the modularity of such graphs, to integrate multiple asynchronous sensors that provide disentangled measurements, like LiDAR, GNSS, and IMU is still overlooked in the literature. In this work, we propose a novel factor graph-based architecture that builds upon the vehicle dynamics at its core to enable the fusion of multiple sensors asynchronously and enables to perform robust and accurate state estimation.

We validate the proposed algorithm against two baselines, a model-based Extended Kalman Filter and a factor graph-based state estimator that uses the IMU pre-integration factor as a reference factor. The algorithms are validated in a custom dataset collected using an in-house vehicle.
Profile Generation of Cooperative Driving in Urban Intersections for Energetic Evaluation
Authors: Flormann Maximilian , Sturm Axel , Henze Roman , Raksincharoensak Pongsathorn
Published: October 2024

In the context of urban smart mobility, automated vehicles communicating with each other, surrounding infrastructure, and other traffic participants yield the use for cooperative driving in urban environments. This, alongside an increase in safety and comfort, can help to reduce fuel and energy consumption individually and with regard to the cooperative vehicle cluster. In this paper we are addressing the impact urban cooperative driving has on the energy consumption of electric vehicles and further investigate the impact on drive systems and their layout, as well as the impact different control strategies of the cooperative network and scenarios with varying vehicles densities have. In order to evaluate the impact, defined driving profiles from a graph based optimization for cooperation networks are evaluated with regard to their energy consumption. Based upon the results the overall energetic impact of different intersection approaching strategies as well as the drive system impact are discussed.
Validation of Control Method to Improve Posture Stability of Narrow Tilting Vehicles Using Real-Vehicle Dynamic Tests
Authors: Araki Keizo , Gwak Jongseong , Suda Yoshihiro
Published: October 2024

Narrow Tilting Vehicles (NTVs) have been proposed as a solution to traffic problems such as congestion and limited parking spaces. However, their small footprint can lead to a reduction in postural stability. To address this issue, NTVs have been designed to reduce the lateral acceleration experienced by occupants by tilting the vehicle body inwards during turns, depending on the driving situation. Three types of tilt control methods have been proposed for NTVs: direct tilt control (DTC), steering tilt control (STC), and combined steering and direct tilt control (SDTC). In this study, we proposed a simplified method to control SDTC using a single roll directional motion equation, focusing on the yaw angular acceleration and roll inertia of the vehicle body. The proposed method was found to result in a 84% reduction in the lateral acceleration experienced by occupants during turning maneuvers in the evaluation of real-vehicle dynamic tests.
Interaction Between L4 AVs and Human Drivers in Italian Take-over Scenarios
Authors: Boscaro Linda , De Guglielmo Veronica , Fossati Andrea , Galbiati Andrea , Gobbi Massimiliano , Mastinu Gianpiero , Previati Giorgio , Sabbioni Edoardo , Signorini Maria Gabriella , Somma Antonella , Subitoni Luca , Uccello Lorenzo
Published: October 2024

Automated Driving (AD) technologies are rapidly transforming road transportation, emphasizing the critical role of Human-Machine Interaction (HMI). In this regard, the paper examines the interaction between Level 4 Autonomous Vehicles (L4 AVs) and human drivers in take-over scenarios within Italian traffic environments. Employing the Dynamic Driving Simulator at Politecnico di Milano, the study presents two simulation environments: an urban roundabout and a Ligurian highway. The research aims to measure the driver response during take-over requests. Questionnaires are used to psychologically analyse the participants. Physiological signals, including ECG, EEG, and EDA, are acquired throughout the entire simulation.
A Moving Laboratory for Automotive Components Safety Testing (MoLAS)
Authors: Biffi A. , Ballo F. , Gobbi M. , Mastinu G.
Published: October 2024

The MoLAS is a moving laboratory able to fully characterize the tire behavior in the real working environment. The basic structure of the moving laboratory is represented by a semi-trailer. A rotating frame that supports the measuring system is used to set the camber level by using an electric actuator. An electric steering actuation system guarantees high output torque and high output power. A pneumatic actuator is used to apply the vertical load, allowing to generate a large range of vertical force up to SUVs values. A complex driveline including an internal combustion engine (ICE) coupled with the gearbox and an electromagnetic (EM) retarder is used to apply both driving and braking torques. This aspect constitutes a new feature of the system.
Design of Explicit and Lateral-Longitudinal Integrated Motion Controller with Safety Guarantee for Autonomous Vehicles
Authors: Gao Haoyu , Liu Chang , Piao Yingxi , Yang Sen , Jiang Beiyan , Li Shengbo Eben
Published: October 2024

Model predictive control (MPC) is an effective method in lateral-longitudinal integrated control with safety guarantee for autonomous vehicles. But its computational burden is significant, making it challenging to meet real-time requirements. The contribution of this paper is to propose an explicitly solvable autonomous vehicle motion controller with lateral-longitudinal integrated characteristics and safety guarantee, achieved by integrating input-output controllers from exponential control Lyapunov function (ECLF) and exponential control barrier function (ECBF). We performed simulations using a high-fidelity dynamics model in CarSim for simple urban traffic scenarios. The simulation results demonstrate that our designed controller has good trajectory tracking performance, safety guarantee, low computational burden and a certain level of robustness.
AI-Based Power Split Strategy for Hybrid Commercial Vehicle Applications
Authors: Nair Pratheesh Sivaraman , Bukic Tomislav , Burnner Dominik , Koutroulis Georgios , Zivadinovic Milan
Published: October 2024

In the rapidly evolving landscape of hybrid commercial vehicle technology, integrating artificial intelligence (AI) with fuel cell applications offers a promising frontier for efficient, sustainable and eco-friendly road freight system. There has been many different approaches on optimization of power split between the electric motor and the fuel cell system (FCS). Conventional approaches use quadratic optimization to determine the optimal power from the electric motor at each discrete grid point along the route, with initial and final battery state of charge (SoC) as constraints. This paper proposes a deep reinforcement learning-based approach to optimize the power split between the electric motor and the FCS in a hybrid vehicle at every time point during the vehicle’s trip. The agent demonstrated the ability to autonomously learn and improve power split decisions, resulting in enhanced fuel efficiency.
Nonlinear Dynamics of a Controlled Two-Wheeled Trailer
Authors: Horvath Hanna Zsofia , Feher Adam Balint , Takacs Denes
Published: October 2024

The nonlinear dynamics of towed two-wheeled trailers is investigated using a spatial, 4-DoF model. Namely, the yaw, pitch, and roll motions are all taken into account. Geometrical nonlinearities and the non-smooth characteristics of the tire forces are considered. A linear state feedback controller with feedback delay is designed to enhance the stability performance of the trailer. Numerical bifurcation analysis is performed to investigate the large amplitude vibrations and unsafe (bistable) zones, where the stable rectilinear motion and the stable limit cycle coexist with each other. The effects of the control gain and the feedback delay of the controller are presented on bifurcation diagrams. It is shown, that with appropriately chosen control gains, the size of the bistable region can be limited.
Trajectory Tracking for High-Performance Autonomous Vehicles with Real-Time Model Predictive Control
Authors: Pierini Matteo , Fusco Paolo , Senofieni Rodrigo , Corno Matteo , Panzani Giulio , Savaresi Sergio Matteo
Published: October 2024

This work is the development of a Model Predictive Controller (MPC) for the integrated control of lateral and longitudinal dynamics of a high-performance autonomous car, which follows a given trajectory on a racetrack. The MPC model is based on an Affine-Force-Input single-track nonlinear bicycle model that accounts for actuation dynamics and delays. The MPC problem is formulated as a quadratic problem, enabling efficient real-time solution with a specific quadratic programming (QP) solver. The controller is implemented in
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and tested in a real-time hardware-in-the-loop (HIL) simulator, showing excellent tracking performance up to 280 km/h.
STS-GAN: Spatial-Temporal Attention Guided Social GAN for Vehicle Trajectory Prediction
Authors: Chen Yanbo , Yu Huilong , Xi Junqiang
Published: October 2024

Accurately predicting the trajectories of other vehicles is crucial for autonomous driving to ensure driving safety and efficiency. Recently, deep learning techniques have been extensively employed for trajectory prediction, resulting in significant advancements in predictive accuracy. However, existing studies often fail to explicitly distinguish the impact of historical inputs at different time steps and the influence of surrounding vehicles at distinct locations. Moreover, deep learning-based approaches generally lack model interpretation. To overcome the issues, we propose the Spatial-Temporal Attention Guided Social GAN (STS-GAN). In the generator, we proposed a spatial-temporal attention mechanism to guide the utilization of trajectory features and interaction of the target vehicle with its surrounding vehicles. The spatial attention mechanism evaluates the importance of surrounding vehicles for predictions of the target vehicle, while the temporal attention mechanism learns the significance of historical trajectory information at different historical time steps, thereby enhancing the model interpretation. A convolutional social pooling module is employed to capture interaction features from surrounding vehicles, which are subsequently fused with the attributes of the target vehicle. Experimental results demonstrate that our model achieves competitive performance compared with state-of-the-art methods on publicly available datasets.
Research on Jerk Reduction Route Planning Method for Autonomous Driving Using Vehicle Forward Information
Authors: Komiya Takumi , Abe Masato , Kano Yoshio , Yamakado Makoto , Sato Yu , Ueno Ken-taro , Tanaka Yusuke
Published: October 2024

We propose a forward-gazing model for path planning (Preview Path-Planning model) as the most basic model that provides insight to people who share the paradigm of human-like path-planning modeling. The basic concept is to apply a low-pass filter to changes in forward curvature. In addition, the phase delay due to the low-pass filter is compensated by forward gaze. We also report the results of determining the longitudinal motion by applying the GVC technology to the situation in which the vehicle could run perfectly along the path.
A Data-Driven Framework for Tire Force Estimation of Distributed Electric-Drive Vehicles
Authors: Yan Rujun , Jiang Kun , Zhang Bowei , Miao Jinyu , Yang Diange
Published: October 2024

In recent years, with the development of wheel-side motors and hub motors, distributed electric drive vehicles, gradually enter the electric vehicle market.Tire force are often derived from rule-based model in the past. However, distributed electric drive vehicles have a higher degree of freedom put forward new control requirements. This puts forward higher requirements for the accuracy of the tire force model. Rule-based model cannot meet the requirements quite well. Because of this, our study established a tire force residual correction framework for distributed electric drive vehicles. The framework consists of a neural network model (MLP, MLP-seq, and MLP-mixer) and a physical rule-based model. The framework was proved in the study to output a more accurate force estimation which will help dynamic modeling and control tasks.
Inverse Approach to Vehicle Generalized Parameters for Individual Drives
Authors: Vantsevich Vladimir , Paldan Jesse , Gorsich David , Moradi Lee
Published: October 2024

In this paper, a method is presented to determine the individual tire slippages and wheel circumferential forces/torques for vehicles with individually-driven wheels. The method is based on a system of parameters of individual wheels mathematically linked to the vehicle generalized parameters. The paper demonstrates an inverse approach on how the generalized parameters can be used to determine individual wheel parameters when the vehicle generalized parameters are given for a single, generalized wheel, whose kinematics and dynamics are equivalent to those of the vehicle. The method can be applied to improve terrain mobility and energy efficiency of autonomous ground vehicles, including planet rovers whose guidance systems do not take into consideration that the tire slippages and circumferential forces are different or should be different by determining individual parameters of the e-motors to provide maximum mobility or energy efficiency in a straight line motion.
Vibration Suppression of Automotive Drivetrains Based on Tire-Speed Observer and Backlash Compensation
Authors: Maeda Kenta , Shinohara Naoki , Kaneko Satoshi , Sonoda Hiroki
Published: October 2024

This paper proposes a novel vibration suppression control method, which suppresses the torsional resonance of the drive shaft caused by motor torque change in the general drivetrain structure of automobiles. For realizing the method in the simplest and most prospective way, the proposed method calculates the feedback torque from the difference between the motor speed and the tire speed multiplied by a proportional gain, with estimating the tire rotation speed by an observer. In addition, a backlash compensation method is developed to reduce noise and vibration in gear retightening in the backlash region. The method observes the fluctuation of the motor speed after the torque zero-crossing and determine the motor torque so that the motor rotation speed returns to the speed at the entrance of the backlash. The effectiveness of the proposed methods is verified by simulation and driving test on the experimental vehicle.
Robust Lane Keeping Control with Estimation of Cornering Stiffness and Model Uncertainty
Authors: Seong Junyeong , Park Sungjun , Huh Kunsoo
Published: October 2024

This paper introduces an adaptive lane-keeping control strategy that adapts to varying cornering stiffness while ensuring robustness against uncertainties. The system consists of three blocks: an Interacting Multiple Model (IMM) cornering stiffness estimator, a cornering stiffness uncertainty estimator, and a Robust Model Predictive Controller (RMPC). Improvements in estimation accuracy are achieved through a novel IMM probability derivation method, and the uncertainty estimator utilizes the IMM probability matrix to obtain reliable uncertainty boundaries. Real-time cornering stiffness estimations are integrated into the RMPC for adaptive model predictions. Uncertainty boundaries provide robustness against estimation error in the RMPC by constraint tightening and smoothing techniques. The performance of the estimator and controller is validated in simulations, where the overall control performance is compared to that of the Model Predictive Control (MPC) based on static cornering stiffness.
A Parametric Interpolation-Based Approach to Sideslip Angle Estimation
Authors: Tristano Mariagrazia , Lenzo Basilio
Published: October 2024

Vehicle sideslip angle has always been of interest for stability controls enhancing vehicle safety. As well-known, measuring sideslip is impractical and expensive, motivating techniques to estimate it using already-available vehicle sensors. This paper proposes a new methodology to estimate sideslip angle, separating kinematic and dynamic sideslip angle contributions with the idea that the former is straightforward and the latter may be obtained with a lateral-acceleration-based interpolation. The proposed approach is validated through experimental data on a passenger vehicle.
Primary and Secondary Vehicle Lightweighting Achieved by Acting on the Battery Thermal Management System
Authors: Sandrini Giulia , Chindamo Daniel , Gadola Marco , Candela Andrea , Magri Paolo
Published: October 2024

Global warming and air pollution are the main factors influencing international, national, and local strategies for the transition towards clean technologies to reduce polluting and climate-altering emissions. A further reduction of the latter can be achieved, with the same powertrain technology, by reducing vehicle consumption. One technique is to lighten the vehicle. The goal of this feasibility study is to act on the battery thermal management system to achieve vehicle lightweighting. Specifically, a sedan car with active-cooled batteries was considered as a reference case, and primary lightweighting was achieved through the use of passive cooling methods, i.e., air and Phase Change Material (PCM) cooling systems, followed by secondary lightweighting to re-establish the target range of the reference vehicle by downsizing the batteries. The air-cooled system leads to greater lightweighting, but its field of application is limited to vehicles operating in fleets; this obstacle can be overcome by using a PCM.
A Propulsion Energy Estimator for Road Vehicles
Authors: Emvin Carl , Bruzelius Fredrik , Romano Luigi , Jacobson Bengt , Johannesson Pär , Andersson Rickard
Published: October 2024

Residual range estimation plays a crucial role in route selection and the trust of electric vehicles (EVs). With inspiration from longitudinal vehicle dynamics, a simple and computationally efficient model for traction power is presented. Such a model has the advantage of being exclusively based on vehicle exogenous parameters. The model allows for insight into variations in power usage along a transport operation and separation of power losses originating from air drag, rolling resistance, hill climbing, and inertial forces. A model of this structure can handle regenerative braking and estimate service brake usage as an additional feature. Also, it treats the inherent truncation bias resulting from truncating a stochastic process. Evaluation of the performance is presented using Monte Carlo simulations, comparing the estimation error against a simple benchmark model and vehicle log data.
A General 3D Road Model for Motorcycle Racing
Authors: Fork Thomas , Borrelli Francesco
Published: October 2024

We present a novel control-oriented motorcycle model for nonplanar racetracks and use it for computing racing lines. The proposed model combines recent advances in nonplanar road models with the dynamics of motorcycles. Our approach considers the additional camber degree of freedom of the motorcycle body with a simplified model of the rider and front steering fork bodies. We demonstrate the effectiveness of our model by computing minimum-time racing trajectories on a nonplanar racetrack.
Energy-Efficient Straight-Line Driving Torque Vectoring for Electric Vehicles with Disconnect Clutches and Unequal Front/Rear Motors
Authors: Grđan Ivo , Škugor Branimir , Deur Joško
Published: October 2024

This paper investigates potential of energy efficiency improvement for electric vehicle (EV) equipped with unequal front/rear-axle e-motors and disconnect clutches under straight-line driving conditions. First, a static optimization of front/rear torque distribution is performed for various driving cycles, which provides insights into energy efficiency gains and optimal powertrain operation including optimal torque switching curve for two- and four-wheel drive modes. Disconnect clutches enable inactive motors to be switched off when operating in the 2WD mode to avoid their drag losses. A dynamic programming (DP)-based optimization of torque vectoring control trajectories is carried out to find the globally optimal energy saving potential. For clutch durability reasons, the number of clutch state changes is minimized along with energy consumption. Finally, a rule-based (RB) control strategy is proposed and verified against the DP Pareto optimal frontier benchmark for different certification driving cycles.
YOLOgraphy: Image Processing Based Vehicle Position Recognition
Authors: Köpeczi-Bócz Ákos T. , Mi Tian , Orosz Gábor , Takács Dénes
Published: October 2024

A methodology is developed to extract vehicle kinematic information from roadside cameras at an intersection using deep learning. The ground truth data of top view bounding boxes are collected with the help of unmanned aerial vehicles (UAVs). These top view bounding boxes containing vehicle position, size, and orientation information, are converted to the roadside view bounding boxes using homography transformation. The ground truth data and the roadside view images are used to train a modified YOLOv5 neural network, and thus, to learn the homography transformation matrix. The output of the neural network is the vehicle kinematic information, and it can be visualized in both the top view and the roadside view. In our algorithm, the top view images are only used in training, and once the neural network is trained, only the roadside cameras are needed to extract the kinematic information.
Occluded Area Detection Based on Sensor Fusion and Panoptic Segmentation
Authors: Yoshitake Hiroshi , Gu Jinyu , Shino Motoki
Published: October 2024

Detecting occluded areas in a driving environment is crucial to preventing traffic accidents against hidden road agents coming out from such occluded areas. Our previous work proposed a novel detection method that can offer geometric information of the detected areas by utilizing camera and LiDAR sensor fusion. However, it had difficulty identifying individual areas formed by different objects without information about distinct objects. Thus, the objective of this study was to improve our previous methodology, and panoptic segmentation, which can distinguish between individual objects and offer semantic class labels of the object, was adopted to overcome the limitation. Evaluation results revealed that our proposed methodology could achieve satisfactory results in occlusion area detection and superior accuracy in estimating hidden road agent types in the detected areas.
Vehicle Teleoperation: SRPT Approach Resilience to State Estimation Errors Through Simulation Insights
Authors: Prakash Jai , Vignati Michele , Sabbioni Edoardo
Published: October 2024

Vehicle teleoperation holds great promise but faces challenges in complex scenarios, limited awareness, and network delays, impacting human operators’ cognitive workload. Our prior work introduced the Successive Reference Pose Tracking (SRPT) approach, transmitting poses instead of steering commands, potentially mitigating delays. Yet, SRPT’s robustness in the face of state estimation inaccuracies and the necessary sensors remain unclear. In this study, we assess SRPT under various challenging environmental conditions and measurement errors using a Simulink-based 14-DOF vehicle model. Results show SRPT’s consistent performance, using estimated states, in worst-case scenarios. Our minimalist sensor setup - IMU, wheel speed encoder, and steer encoder - underscores SRPT’s resilience without relying on GPS, vital for urban environments. This paper highlights SRPT’s robust teleoperation, setting the stage for future real-world vehicle tests prone to measurement errors.
Increasing Performance of Differential Braking as Steering Backup Using Combined Slip Effects
Authors: Salzwedel Leon , Frohn Christian , Heise Cedric , Iatropoulos Jannes , Henze Roman
Published: October 2024

Redundancies provide essential fail-operationality and are commonly utilized in design of Steer-by-Wire systems. Operationality of steering rack actuators can therefore be increased using differential braking as additional redundancy level. Research in this field has shown boundaries for lateral control to be mitigated compared to conventional steering systems. This paper focuses on performance increases by driveline torque and stability control, both yielding effects of combined longitudinal and lateral tire slip during cornering.

Coping with the tradeoff between under- and oversteer whilst increasing performance was the major aim during controller design. For vehicle prototype testing a model following control scheme for differential braking was derived and implemented. Measurements on a low friction proving ground in suitable driving maneuvers were carried out subsequently. The results show new boundaries of vehicle dynamics and address variations of steering kinematics parameters as well as driveline configurations. Investigated interactions of longitudinal and lateral tire slip in the field of differential braking brings up promising potential to increase cornering ability in the fallback level for steering rack actuators.
Laboratory Abrasion Tester to Estimate Tyre Grip and Cornering Stiffness
Authors: Colombo Francesco , Sonnino Samuel , Mantovani Federico , Ronchi Andrea , Michielan Luca , Vignati Michele , Sabbioni Edoardo
Published: October 2024

Identification of tyre grip is crucial for ensuring safety and performance. To develop new tyres matching the requirements for grip, extensive testing is required both indoor and outdoor, which is time-consuming and costly. Therefore, the possibility to assess their performance in laboratory before manufacturing a full tyre appears very attractive. On this purpose, the present paper compares the peak of the friction coefficient, evaluated using a Laboratory Abrasion Tester (LAT100) on two compounds and the grip assessed through MTS Flat-Trac machine tests on two full tyres having the same structure and made of the compounds tested on the LAT100. An ad hoc procedure for driving the test on the LAT100 and make them comparable with the full-tyre data was developed. A good correlation was found for the dependency of the friction coefficient on temperature and load, highlighting the possibility of using LAT100 tests to gain information about tyre performance, before the manufacturing of the full tyre.
Steering Noise Cancelling for Drift Assist Control
Authors: Sun Yiwen , Velenis Efstathios , Krishnakumar Ajinkya
Published: October 2024

In this paper we consider a driver assist system concept for the stabilization of the vehicle during high sideslip angle cornering (drifting). Unregulated steering inputs (steering noise) from inexperienced drivers, can disturb the drift equilibria and oppose to the controller’s stabilization task. Therefore, this paper presents a drift assist control concept to cancel the steering effects from the driver by torque vectoring. In detail, first by equilibria analysis for four-wheel drive vehicle, the steering effects on drift equilibria are analyzed, which indicates a contradiction between stabilizing the yaw rate and the sideslip angle. Then, by nonlinear programming, it is proven that the path angle rate together with the speed which determine the trajectory, could be stabilized with torque vectoring against the steering noise. Finally, a steering noise cancelling and trajectory tracking controller structure is designed and further evaluated with CarMaker.
A Decoupling Control Scheme for Path Tracking with Model Predictive Path Integral and Output Regulator
Authors: Wan Hang , Liu Hui , Nie Shida , Han Lijin
Published: October 2024

The coupling and nonlinearity of vehicle dynamics present considerable challenges to path tracking of autonomous vehicles. In this paper, a necessary condition is derived to decouple the translational motion from yawing motion based on the time-scale separation. Consequently, the translational motion is regulated over an extended control horizon to generate a human-like tracking trajectory. The yawing motion is regulated based on a high-fidelity control model. In addition, model predictive path integral (MPPI) is developed to mitigate the computational burden of nonlinear motion planning through sampling-based optimization. A predictive output regulator is developed to solve the underactuated problem in the 2-DOF lateral dynamics with only 1-DOF of control input. Simulation results show that the proposed method enhances computing efficiency and reduces the lateral jerk by an average of 50% with only one set of parameters.
Predictive Braking on a Nonplanar Road
Authors: Fork Thomas , Camozzi Francesco , Fu Xiao-Yu , Borrelli Francesco
Published: October 2024

We present an approach for predictive braking of a four-wheeled vehicle on a nonplanar road. Our main contribution is a methodology to consider friction and road contact safety on general smooth road geometry. We use this to develop an active safety system to preemptively reduce vehicle speed for upcoming road geometry, such as off-camber turns. Our system may be used for human-driven or autonomous vehicles and we demonstrate it with a simulated ADAS scenario. We show that loss of control due to driver error on nonplanar roads can be mitigated by our approach.
Point Cloud Interpolation by RGB Image to Estimate Road Surface Profile for Preview Suspension Control
Authors: Inoue Masato , Kawasaki Yosuke , Suzuki Takuma , Washimi Yuta , Tanimoto Tsutomu , Takahashi Masaki
Published: October 2024

The growing prevalence of autonomous driving is expected to shift passengers’ attention from driving, increasing the demand for enhanced ride comfort. Studies addressing ride comfort have prominently explored active suspension control with recent research on preview suspension control using on-board sensors. The proposed systems often include LiDAR deployment at the front for high-precision road surface profiles. However, these systems often involve costly sensors such as LiDAR, making it impractical for on-board installation. Nonetheless, in recent autonomous vehicles, LiDAR tend to be mounted on the roof. It would be beneficial to leverage this LiDAR for preview control, the point cloud obtained from the roof has insufficient density to accurately perceive the unevenness on the road surface. To overcome the low-density issue in point cloud data obtained from less channels LiDAR, this study applies a supervised machine learning model, developed for autonomous driving, to estimate road surface profiles and enhance the precision of these estimations.
Gain-Scheduled Bicycle Balance Controller Based on System Identification
Authors: Wang Yixiao , Bruzelius Fredrik , Sjöberg Jonas
Published: October 2024

Balancing a bicycle through steering is similar to balancing an inverted pendulum, with a travel-speed-dependent pivot point. This paper derives a speed-dependent balancing controller for a self-balancing bicycle. This controller is based on an identified gray box model. The identification procedure is formulated as a weighted least squares problem with the time-varying parameter of the model. Identification data was generated on a controlled bicycle robot. Excitation experiments were designed to account for the unstable nature of the problem. Based on this identified model, a gain-scheduled controller is derived for a speed-independent closed-loop performance for a speed range. The controller is further implemented on the bicycle and tested for a set of speeds. Tests performed on the bicycle illustrate the gain-scheduled controller’s performance gain.
Geometric actions for Lower Spin Gravity
Authors: Detournay Stéphane , Vandermiers Quentin
Published: October 2024

Abstract

We perform the Hamiltonian reduction of Lower Spin Gravity, the simplest bulk dual for a Warped Conformal Field Theory (WCFT), consisting in an SL(2) × U(1) Chern-Simons model. We identify the boundary action as the geometric action on coadjoint orbits of the Warped Virasoro group. We use this reduced action to compute one-loop contributions to the torus partition function and compare them to the Warped Virasoro characters.
Properties of dynamical black hole entropy
Authors: Visser Manus R. , Yan Zihan
Published: October 2024

Abstract

We study the first law for non-stationary perturbations of a stationary black hole whose event horizon is a Killing horizon, that relates the first-order change in the mass and angular momentum to the change in the entropy of an arbitrary horizon cross-section. Recently, Hollands, Wald and Zhang [1] have shown that the dynamical black hole entropy that satisfies this first law, for general relativity, is Sdyn = (1 − v∂v)SBH, where v is the affine parameter of the null horizon generators and SBH is the Bekenstein-Hawking entropy, and for general diffeomorphism covariant theories of gravity Sdyn = (1 − v∂v)SWall, where SWall is the Wall entropy. They obtained the first law by applying the Noether charge method to non-stationary perturbations and arbitrary cross-sections. In this formalism, the dynamical black hole entropy is defined as an “improved” Noether charge, which is unambiguous to first order in the perturbation. In the present article we provide a pedagogical derivation of the physical process version of the non-stationary first law for general relativity by integrating the linearised Raychaudhuri equation between two arbitrary horizon cross-sections. Moreover, we generalise the derivation of the first law in [1] to non-minimally coupled matter fields that are smooth on the horizon, using boost weight arguments rather than Killing field arguments, and we relax some of the gauge conditions on the perturbations by allowing for non-zero variations of the horizon Killing field and surface gravity. Finally, for f(Riemann) theories of gravity we show explicitly using Gaussian null coordinates that the improved Noether charge is Sdyn = (1 − v∂v)SWall, which is a non-trivial check of [1].
On the nonlinear dynamics of in-contact rigid bodies experiencing stick–slip and wear phenomena
Authors: D’Annibale Francesco , Casalotti Arnaldo
Published: October 2024

In this paper, the dynamic behavior of one degree-of-freedom oscillator subject to stick–slip and wear phenomena at the contact interface with a rigid substrate is investigated. The motion of the oscillator, induced by a harmonic excitation, depends on the tangential contact forces, exchanged with the rigid soil, which are modeled through piecewise nonlinear constitutive laws, accounting for stick–slip phenomena due to friction as well as wear due to abrasion, already developed by the authors in a previous work. The nonlinear ordinary differential equations governing the problem are derived, whose solution is numerically obtained via a typical Runge–Kutta-based algorithm. The main target of this study is to analyze and discuss the strong nonlinear behavior, descending from the presence of stick–slip and wear phenomena, thus investigating the effect of the different interface modeling. In this framework, the analysis is carried out considering the whole evolution of non-smooth contact laws, starting from the virgin interface.
A 14-year longitudinal study of neurofilament light chain dynamics in premanifest and transitional Huntington’s disease
Authors: Voysey Z. J. , Owen N. E. , Holbrook J. A. , Malpetti M. , Le Draoulec C. , Spindler L. R. B. , Goodman A. O. G. , Lazar A. S. , Barker R. A.
Published: October 2024

Background

Growing evidence supports the value of neurofilament light (NfL) as a prognostic biomarker in premanifest Huntington’s disease (HD). To date, however, there has been no longitudinal study exceeding 3 years examining either its serial dynamics or predictive power in HD. We aimed to conduct the first such study.

Methods

Serum NfL was sampled using ultrasensitive immunoassay at four timepoints across a 14-year period in a cohort of HD gene carriers (n = 21) and controls (n = 14). Gene carriers were premanifest at baseline. Clinical features of HD were evaluated by Unified Huntington’s Disease Rating Scale (UHDRS TMS), Montreal Cognitive Assessment (MoCA), Trail A/B task, Symbol Digit Modalities Task and semantic/phonemic fluency tasks.

Results

14/21 HD gene carriers converted to prodromal or manifest disease by the final timepoint (“converters”). At baseline and each subsequent timepoint, NfL levels were higher in converters than in non-converters and controls (p = < 0.001–0.03, ηp2 = 0.25–0.66). The estimated rate of change in NfL was higher in converters than in non-converters (p = 0.03) and controls (p = 0.001). Baseline NfL was able to discriminate converters from non-converters (area under curve = 1.000, p = 0.003). A higher rate of change in NfL was predictive of more severe motor (UHDRS-TMS p = 0.007, β = 0.711, R2 = 0.468) and cognitive deficits (MoCA p = 0.007, β = − 0.798, R2 = 0.604; Trail B, p = 0.007, β = 0.772, R2 = 0.567; phonemic fluency p = 0.035, β = − 0.632, R2 = 0.345).

Conclusions

Our data suggest that (1) NfL longitudinal dynamics in premanifest/transitional HD are non-constant; rising faster in those closer to disease onset, and (2) NfL can identify individuals at risk of conversion to manifest disease and predict clinical trajectory, > 10 years from disease onset.
Academic socialization: On a neglected effect of competition in science
Authors: Hamann Julian , Velarde Kathia Serrano
Published: October 2024

Competition is usually considered a mechanism for the allocation of scarce resources. This paper draws attention to a neglected effect of academic competition: competition provides participants with a normative orientation about field-specific values by informing them about which activities will be rewarded. We conceptualize this as a socializing effect. We study the socializing effects of competition for postdocs because this group is especially exposed to the dynamics of academic competition. Drawing on qualitative interviews with 60 postdocs in history and physics in Germany, the analysis reveals the orientations and values that competition conveys to postdocs. Since different forms of competition for publications, funding, and jobs are not neatly synchronized, they convey various expectations. Postdocs cope with this multiplicity, prioritizing a specific form of competition that is competition for funding. This comes at the expense of activities and tasks that are not organized competitively. The socializing effect of competition should be reflected more thoroughly not only by scholarship on academic competition but also by research policy that promotes competition.
Optomechanics Driven by Noisy and Narrowband Fields
Authors: Banniard Louise , Wang Cheng , Stirpe Davide , Børkje Kjetil , Massel Francesco , Mercier de Lépinay Laure , Sillanpää Mika A.
Published: October 2024

We report a study of a cavity optomechanical system driven by narrowband electromagnetic fields, which are applied either in the form of uncorrelated noise, or as a more structured spectrum. The bandwidth of the driving spectra is smaller than the mechanical resonant frequency, and thus we can describe the resulting physics using concepts familiar from regular cavity optomechanics in the resolved-sideband limit. With a blue-detuned noise driving, the noise-induced interaction leads to anti-damping of the mechanical oscillator, and a self-oscillation threshold at an average noise power that is comparable to that of a coherent driving tone. This process can be seen as noise-induced dynamical amplification of mechanical motion. However, when the noise bandwidth is reduced down to the order of the mechanical damping, we discover a large shift of the power threshold of self-oscillation. This is due to the oscillator adiabatically following the instantaneous noise profile. In addition to blue-detuned noise driving, we investigate narrowband driving consisting of two coherent drive tones nearby in frequency. Also in these cases, we observe deviations from a naive optomechanical description relying only on the tones’ frequencies and powers.
Size fraction characterisation of highly-calcareous and siliceous fly ashes
Authors: Delihowski Jurij , Izak Piotr , Wójcik Łukasz , Gajek Marcin , Kozień Dawid , Jarosz Marcin
Published: October 2024

The properties of coal fly ash vary significantly depending on factors such as coal type, combustion conditions, and flue gas emission reduction methods. This study investigates the influence of particle size fractionation on the chemical composition, mineralogical structure, and thermal behaviour of two types of fly ash: high calcium ash derived from lignite (S1) and silica-rich ash from bituminous coal (S2). Dry aerodynamic separation was used to obtain distinct size fractions, which were then subjected to a comprehensive characterisation including X-ray fluorescence, X-ray diffraction, scanning electron microscopy, and thermal analysis. The results reveal notable differences between the S1 and S2 ashes and between their size fractions. The finer fractions (< 20 μm) of S1 showed an increased calcium and sulphur content, while the coarser fractions (> 100 μm) contained more silica and alumina. The S2 ash exhibited a higher overall silica content, with alkali metals concentrated in finer fractions. Thermal analysis demonstrated distinct behaviours for each type and fraction of ash. Fine fractions of S1 ash showed SO2 emission at elevated temperatures, while S2 ash exhibited greater CO2 gas emission. After thermal treatment, the recrystallisation of the glassy phase was observed for S1, while the S2 ashes were more prone to melting and agglomeration. The study highlights the potential for the customised utilisation of specific ash fractions in various applications, such as geopolymer synthesis, adsorbent materials, and refractory products. This comprehensive characterisation contributes to a better understanding of fly ash properties and their dependence on particle size, providing valuable insights to optimise fly ash utilisation in various industries. The findings suggest strategies for a more efficient use of fly ash resources, particularly relevant in the context of decreasing fly ash availability due to the phase-out of coal power plants.
Even in presupposition denials
Authors: Francis Naomi
Published: October 2024

This paper explores a puzzling polarity-based asymmetry in the use of even in sentences that deny presuppositions. It argues that this asymmetry is produced by the interaction of even’s controversial additive presupposition with the alternatives that are salient in the relevant contexts and demonstrates that this proposal makes good crosslinguistic predictions. Along the way, this paper shows that presupposition denials are a fruitful testing ground for uncovering details about the behaviour of even and the role of presuppositions triggered within focus alternatives.