bluuebunny/crossref_metadata_2025_split huggingface dataset:
Profile Generation of Cooperative Driving in Urban Intersections for Energetic Evaluation
Authors: Flormann Maximilian , Sturm Axel , Henze Roman , Raksincharoensak Pongsathorn 
Published: October 2024

AbstractIn 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

AbstractNarrow 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

AbstractAutomated 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

AbstractThe 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

AbstractModel 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

AbstractIn 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

AbstractThe 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

AbstractThis 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
"Image missing"
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

AbstractAccurately 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

AbstractWe 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

AbstractIn 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

AbstractIn 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

AbstractThis 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

AbstractThis 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

AbstractVehicle 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

AbstractGlobal 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

AbstractResidual 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

AbstractWe 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

AbstractThis 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

AbstractA 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

AbstractDetecting 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

AbstractVehicle 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.