bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mapping and Localization Method for Autonomous Vehicles on Roads Using Environmental Magnetic Field
Authors: Ishii Kyoya , Shimono Keisuke , Suda Yoshihiro , Ando Takayuki , Mukumoto Hirotaka , Urakawa Kazuo 
Published: October 2024

AbstractVehicle localization is one of the key technical factors for autonomous vehicles. It requires high accuracy, precision, and robustness towards various road conditions. Popular localization methods include global navigation satellite system (GNSS) and visual methods, but their accuracy can degrade in some conditions. This work proposes to use the environmental magnetic field (EMF) for localization to complement the shortcomings of existing methods. EMF is a combination of the Earth’s geomagnetic field and magnetic field induced by man-made objects. It has local fluctuations that can be paired with coordinate positions and is time-invariant within a practical timescale. Past works considering the localization of road vehicles had few problems when applying them to the localization of autonomous vehicles. This work overcomes the problems in the existing method by creating a two-dimensional magnetic field map using Gaussian Process regression, using magnetic markers to enhance EMF fluctuations, and utilizing the Monte Carlo localization algorithm. The proposed method was validated through actual vehicle tests, and its robustness towards other vehicles was examined.
Robust Inverse Vehicle Map Regression Based on Laplace Distribution
Authors: Penet Maxime , Le Gall Gaetan 
Published: October 2024

AbstractThis paper deals with the identification of the relationship between vehicle acceleration and driver available actuators. The vehicle is modeled based on how a driving task is performed. The model is constructed using neural networks whose weights are identified using data collected through non tailored driving sessions. To take into account disturbances, the model follows a Laplace distribution. This leads to a more robust estimate of the vehicle knowledge and the confidence we have in it. The approach is illustrated on a prototype vehicle equipped with a petrol engine, plus a device to actuate the pedals.
MLIO: Multiple LiDARs and Inertial Odometry
Authors: Dahal Pragyan , Arrigoni Stefano , Bijelic Mario , Braghin Francesco 
Published: October 2024

AbstractWith the decreasing cost of LiDAR sensors, sensor setups with multiple LiDARs are becoming available. In such advanced setups with multiple LiDARs the sensor temporal asynchronicity and spatial miscalibration are critical factors for vehicle localization increasing measurement uncertainty. Hence, simple merging of synchronized point clouds as done in some literature can lead to sub-optimal results. To tackle this problem we propose MLIO, a factor graph-based odometry computation algorithm that fuses multiple LiDARs with an inertial measurement unit (IMU) and provides an accurate solution mitigating the effect of temporal asynchronisity and spatial miscalibration.The proposed algorithm is validated using a custom dataset. We compare the proposed algorithm with the state-of-the-art LiDAR-only odometry algorithms, such as KISS-ICP, and LiDAR-IMU fusion LIO-SAM and demonstrate its superiority. We were able to achieve up to 40% and 16% increment in positional and orientation accuracy compared to KISS-ICP and 25% increment in positional accuracy compared to LIO-SAM.
Powerslide Control with Deep Reinforcement Learning
Authors: Jaumann Florian , Schuster Tobias , Unterreiner Michael , Gräber Torben , Edelmann Johannes , Plöchl Manfred 
Published: October 2024

AbstractControlling a vehicle’s powerslide motion in the presence of a human driver is a challenging control task, but one that may have a significant impact on vehicle safety, for example, during rapid evasive manoeuvres. Reinforcement Learning, a data-driven optimal control strategy, has gained increasing attention in recent years, demonstrating its effectiveness in successfully controlling various nonlinear systems. In this work, a novel powerslide controller is designed for an all-wheel drive battery electric vehicle with individually driven front and rear axles and a human driver in closed-loop using Reinforcement Learning. The performance of the proposed controller is analysed, and its robustness to steering disturbances and changes in road friction is demonstrated.
Simulating Effects of Suspension Damper Degradation on Common Sensor Signals for Diagnosis Models in the Context of Condition-Based Maintenance
Authors: Ott Lorenz , Gräber Torben , Unterreiner Michael , Edelmann Johannes , Plöchl Manfred 
Published: October 2024

AbstractDegraded suspension dampers strongly influence vehicle safety and ride comfort, but often occur after several years of operation. Related workshop checks are usually not degradation-adaptive, so they can be significantly delayed to the need for maintenance. To make the maintenance adaptive to degradations, onboard diagnosis methods can be used, which rely on the degradation status extracted from sensor signals.To support the development of sensitive yet robust diagnosis models, a model that can simulate and explain the effects of damper degradation in common sensor signals is proposed. This paper focuses on low-frequency effects in signals of the wheel speed sensors, which are ultra low cost and always available in modern vehicles. As a result, the model shows a good qualitative match to real-world test drives, specifically in the frequency domain. Therefore, various real-world measurements were conducted, in particular, test bench measurements of degraded dampers and vehicle on-road tests.
Gradient Correction for Asynchronous Stochastic Gradient Descent in Reinforcement Learning
Authors: Gao Jiaxin , Lyu Yao , Wang Wenxuan , Yin Yuming , Ma Fei , Li Shengbo Eben 
Published: October 2024

AbstractDistributed stochastic gradient descent techniques have gained significant attention in recent years as a prevalent approach for reinforcement learning. Current distributed learning predominantly employs synchronous or asynchronous training strategies. While the asynchronous scheme avoids idle computing resources present in synchronous methods, it grapples with the stale gradient issue. This paper introduces a novel gradient correction algorithm aimed at alleviating the stale gradient problem. By leveraging second-order information within the worker node and incorporating current parameters from both the worker and server nodes, the gradient correction algorithm yields a refined gradient closer to the desired value. Initially, we outline the challenges associated with asynchronous update schemes and derive a gradient correction algorithm employing local second-order approximations. Subsequently, we propose an asynchronous training scheme incorporating gradient correction within the generalized policy iteration framework. Lastly, in the context of trajectory tracking tasks, we compare the impact of employing gradient correction versus its absence in an asynchronous update scheme. Simulation results underscore the superiority of our proposed training scheme, demonstrating notably faster convergence and higher policy performance compared to the existing asynchronous update methods.
Stability Issues in Adaptive Cruise Control Systems and Traffic Implication
Authors: Donà Riccardo , Mattas Konstantinos , Albano Giovanni , Váss Sandor , Ciuffo Biagio 
Published: October 2024

AbstractAdaptive Cruise Control (ACC) systems under short headway configurations have been found to have a potentially detrimental impact on the transport network due to the string instability effect. Such phenomenon results in traffic perturbations amplification downstream causing increasing fuel consumption and posing safety threats. However, recent findings summarized in this paper show how even the simpler platoon stability might not be attained with current ACC-equipped vehicles raising additional concerns regarding their unregulated operation. In fact, as part of a recent campaign involving state-of-the-art assisted vehicles, an ACC displayed a low-frequency oscillatory behavior around the equilibrium speed. This work, by leveraging a mixed simulation/empirical approach, uncovers the harmful influences of such behavior. Ultimately, we found that despite the poor stability phenomenon might not be impactful for one vehicle, the overall repercussions on the transportation network can be dramatically detrimental raising the need for a regulatory framework for lower-level automation.
The Influence of Transient Tire Force Transmission on Sideslip Angle Estimation
Authors: Puščul Dženana , Schabauer Martin , Lex Cornelia 
Published: October 2024

AbstractKnowledge of the sideslip angle is significant in development, testing and validation of advanced driver assistance systems and vehicle control systems, to enhance vehicle performance and stability. This article investigates the influence of having additional information about transient tire behavior during estimation of the sideslip angle. Since transient behavior depends on the dynamic excitation, the effect during different driving maneuvers (excitations) such as steady-state cornering, slalom and general handling as well as on different road surfaces is investigated. The results show that considering transient force transmission even by a pragmatic approach leads to a significant improvement.
Risk-Predictive Path Planning in Urban Autonomous Driving: A Geometric Approach to VRU Crossing
Authors: Fujinami Yohei , Raksincharoensak Pongsathorn 
Published: October 2024

AbstractThis research introduces a method for autonomous driving systems to safely overtake bicycles in urban environments. It identifies the risk of sudden crossing when overtaking a bicycle and proposes a solution to minimize this risk. The method classifies the situation into three conditions related to the drivable space of the road and speed of the bicycle, and it determines the target speed and positions. The proposed algorithm uses onboard sensor information and assumptions of the bicycle’s virtual sudden-crossing motion, enabling real-time calculations in a practical environment. Simulations demonstrate the method’s effectiveness, showing it can generate a natural overtaking speed and lateral gap based on road width and bicycle speed. The proposed method is compatible with waypoint-based path generation methods we have proposed in previous research, making it a promising solution for future autonomous driving systems. Future research will discuss the method’s implementation in automated vehicles, contributing to safer and more efficient autonomous driving systems.
A Geometric Electric Motor Model for Optimal Vehicle Family Design
Authors: Clemente Maurizio , Borsboom Olaf , Salazar Mauro , Hofman Theo 
Published: October 2024

AbstractThis paper presents a design optimization framework that jointly optimizes battery size with the geometric dimensions of the electric motor for a family of battery electric vehicles, with global optimality guarantees. As opposed to conventional models, we devise a quasi-static model of the motor internal losses as a function of both its geometry and operating points, using a convex surrogate modeling approach. Specifically, we implement a low-level motor scaling, capturing the impact on performance and losses of changing the motor geometry in axial and radial directions. Hence, we leverage the framework to solve a concurrent optimization problem and identify the optimal module sizing for a family of electric vehicles. Finally, we test our framework on a benchmark problem where we jointly design motor and battery for three different types of vehicles (a city car, a compact car, and a cross over), whereby the prediction efficiency is in line with the high-fidelity modeling software.
Model-Free Automated Reversing of Articulated Heavy Goods Vehicles
Authors: Rahman Shammi , Gordon Timothy , Henderson Leon , Gao Yangyan , Coleman Sonya , Kerr Dermot 
Published: October 2024

AbstractThis paper presents a technique for automated reversing control of articulated vehicles. Reversing articulated Heavy Goods Vehicles (HGVs) can be a challenging and time consuming task for a human driver, sometimes requiring multiple forward and backward motions to reduce errors. Here, the aim is to automate the task to provide high levels of precision using Artificial Flow Guidance (AFG). AFG uses simple geometry to define a spatially distributed motion reference, requiring only short-range error corrections and possessing global convergence properties. AFG has previously been applied to rigid and articulated vehicles in forward motion, with demonstrable benefits in terms of tracking precision and robustness. Here results focus on the tractor-semitrailer, but the AFG approach is equally applicable to the reversing of longer combination vehicles.
Yaw Stability Control of Vehicles Using a Slip Polytope Validated with Real Tests
Authors: Erdinc Umur , Jonasson Mats , Kati Maliheh Sadeghi , Laine Leo , Jacobson Bengt , Fredriksson Jonas 
Published: October 2024

AbstractArticulated heavy vehicles (AHVs) face yaw instabilities, especially under extensive propulsion or regenerative braking force on the driven axles, risking their directional stability and potentially leading to jackknifing. Hence, safe operating envelopes (SOEs) are essential for allocating propulsion and braking forces among different units. This study proposes a novel approach to ensure yaw stability by reducing longitudinal slip limits of the electric motors (EMs) based on side-slip, enhancing stability and acceleration performance. Validation through simulations and real vehicle tests shows promising results.
Motion Control of a 6 $$\times $$ 4 Heavy Vehicle: Autonomous Collision Avoidance Using Integrated Chassis Control
Authors: Asiabar Aria Noori , Gordon Timothy , Gao Yangyan , Henderson Leon , Laine Leo 
Published: October 2024

AbstractThis paper considers the coordinated chassis control of a 6 $$\times $$
×
4 HGV tractor unit using a multivariable nonlinear controller for a transient handling manoeuvre under friction-limited conditions. The controller’s performance is evaluated through simulation. It receives Centre of Gravity (CG) longitudinal and lateral acceleration targets, corresponding to curvature and longitudinal acceleration requests, and aims for the CG to track the target accelerations. It employs the Modified Hamiltonian Algorithm (MHA) to generate steering and braking commands for the tractor. A combined-slip Magic Formula tyre model used within the algorithm allows for simultaneous stability control and path tracking, even in scenarios where the vehicle is operating at the limits of tyre adhesion. The manoeuvre is an autonomous obstacle avoidance on a packed snow. Results show the advantages and possible limitations of tracking acceleration targets for integrated chassis control.
Investigating Characteristics and Opportunities for Rear-Wheel Steering
Authors: Lind Jonsson Oskar , Balachandran Arvind , Zhou Jian , Olofsson Björn , Nielsen Lars 
Published: October 2024

AbstractThe potential of additional steering possibilities (like rear-wheel or all-wheel steering) is analyzed for critical situations to investigate possible safety improvements. For this purpose, a dynamic optimization problem is formulated to find the best possible maneuver. The optimization criterion is to maximize the entry speed into a constant radius $$90^\circ $$

90
∘

-curve. The optimization problem is solved for different steering topologies, and the results quantify the increase in maximum entry speed, highlighting the potential for safety improvements. Further, the optimal steering strategies are determined, and they show interesting characteristics like initial diagonal driving or, in other cases, a transition from initial out-of-phase steering to in-phase steering.
Development and Experimental Assessment of a Control Logic for Hydroplaning Prevention
Authors: Montini Edoardo , Salierno Marco , Frigerio Stefano , Melzi Stefano 
Published: October 2024

AbstractHydroplaning plays a crucial role in road safety. The water layer wedged between the tires and the road reduces the capability of the vehicle to respond to the driver’s inputs. Factors like vehicle speed, water layer thickness, tread pattern, and tire wear affect hydroplaning onset. Based on the technical literature and experimental tests, the research developed a tire model to simulate hydroplaning effects. Scaling factors were added to the MFTyre model to reproduce the changes in cornering stiffness, relaxation length, friction coefficient, and motion resistance. Then, a control logic to counteract hydroplaning was designed and implemented on a 14-dof vehicle model. As last, the effectiveness of the control logic was assessed through a series of indoor tests on a dynamic driving simulator.
Effects of External Tire Heating on Rolling Resistance Energy Consumption
Authors: Askerdal Mikael , Bruzelius Fredrik , Fredriksson Jonas 
Published: October 2024

AbstractIt is known that the rolling resistance decreases with increasing tire temperature. If the tires could be heated to a high temperature, the rolling resistance’s energy loss could be reduced. The question arises whether the reduced rolling resistance energy consumption overcomes the energy required to heat the tire.This paper investigates the effects of external heating and improved tire insulation theoretically. The results indicate that external tire heating can be beneficial only if the heat used is waste heat, generated from a heat pump or similar with a coefficient of performance greater than one or taken from the grid.
Safety Filter for Lane-Keeping Control
Authors: Jiang Chenhuan , Gan Hanyu , Vörös Illés , Takács Dénes , Orosz Gábor 
Published: October 2024

AbstractA safe lane-keeping controller is designed using a control barrier function (CBF) which ensures that if the vehicle starts between the lane boundaries then it does not leave the lane. The safety filter is applied on the top of a nominal path-following controller of the kinematic single-track model in order to modify the control input when the vehicle gets close to the boundary of the safe set in state space. Numerical simulations and phase portraits are used to demonstrate the performance of the proposed safety-critical controller.
Neural Network Tire Force Modeling for Automated Drifting
Authors: Broadbent Nicholas Drake , Weber Trey , Mori Daiki , Gerdes J. Christian 
Published: October 2024

AbstractAutomated drifting presents a challenge problem for vehicle control, requiring models and control algorithms that can precisely handle nonlinear, coupled tire forces at the friction limits. We present a neural network architecture for predicting front tire lateral force as a drop-in replacement for physics-based approaches. With a full-scale automated vehicle purpose-built for the drifting application, we deploy these models in a nonlinear model predictive controller tuned for tracking a reference drifting trajectory, for direct comparisons of model performance. The neural network tire model exhibits significantly improved path tracking performance over the brush tire model in cases where front-axle braking force is applied, suggesting the neural network’s ability to express previously unmodeled, latent dynamics in the drifting condition.
Surrogate Modeling of Suspensions with High Stiffness Element for Real-Time Analysis Using Machine Learning
Authors: Hiraga Tojuro , Shiiba Taichi 
Published: October 2024

AbstractThe aim of this study is to generate a surrogate model of a suspension system with high stiffness elements for real-time analysis using machine learning. A Long Short-Term Memory networks was used as a machine learning method to generate surrogate models for three-degree-of-freedom quarter car model with a bush element. To evaluate the performance of the machine learning models, the simulation results and computation time were compared with the 3DOF model. As a result, it was confirmed that the response of the body acceleration was predicted with good accuracy by predicting the bush deformation and calculating the body acceleration based on the prediction results, instead of predicting the body acceleration directly. It was also confirmed that the machine learning model can accurately predict the body acceleration in less calculation time than the original model.
Bifurcation Analysis of a Nonlinear Vehicle Model on Banked Road
Authors: Zerbato Luca , Galvagno Enrico , Velardocchia Mauro 
Published: October 2024

AbstractTowards the transition to automated driving, lateral stability of the vehicle represents a key requirement to guarantee the safety of passengers and vulnerable road users, especially during emergency operating conditions where nonlinearities arise. The present paper aims at investigating the effect of road banking angle on vehicle plane motion stability. To perform this analysis, a pure lateral nonlinear double track model is numerically derived for an oversteering vehicle. Lateral load transfer and its distribution among the axles are included for exploiting the tyre saturation region. The stability analysis is conducted by searching for the vehicle steady-state conditions and deriving the linearised equations around the equilibrium points. Moreover, the phase-plane plot is adopted to draw the states trajectories and to identify potential unstable regions. Finally, the bifurcation analysis as function of the road banking angle is investigated to highlight possible change of the phase portrait topology. The results show that a saddle-node bifurcation may occur when the vehicle is negotiating a certain level of bank road angle, affecting the vehicle yaw stability region.
Speed Profile Generation for a Dual Motor Equipped Electrified Series Hybrid Tracked Vehicle Through Dynamic Programming Based Energy Optimization Procedure
Authors: Gocer Ismail , Baslamisli S. Caglar 
Published: October 2024

AbstractElectrification is a widely explored area in many fields and is becoming a widely researched topic in tracked vehicles. Achieving efficient operation for such vehicles is crucial similar to traditional vehicles. This paper introduces a speed profile generation procedure for a dual track – series hybrid tracked vehicle with an emphasis on fuel economy and trip time optimization. A Dynamic Programming method is proposed in spatial domain, considering the properties of predefined road geometries. The study includes examination of speed and torque estimation of the sprockets, particularly during steering maneuvers. Unlike wheeled vehicles, tracked vehicles have to overcome the turning resistance moment of skid steered tracks. Taking these dynamics into account, the study investigates efficient operation, presenting over a predefined road track including straight road, inclined road and curve sections, discussing the tradeoff between fuel economy and trip time optimality. As the result of problem solution, speed profiles are generated, and operation trends of the powertrain components are discussed.