bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fuel Economy Assessment of MPC-ACC on Powertrain Testbed
Authors: Favelli Stefano , Castellanos Molina Luis M. , Mancarella Alessandro , Marello Omar , Tramacere Eugenio , Manca Raffaele , Silvagni Mario , Tonoli Andrea , Amati Nicola 
Published: October 2024

AbstractThe development and testing of Advanced Driver Assistance Systems (ADAS) is one of the most active fields in the automotive industry towards Automated Driving (AD). This work presents the deployment and testing of an Adaptive Cruise Control (ACC) based on Model Predictive Control (MPC). The goal is to design and validate through the experimental campaign a computationally efficient longitudinal dynamics controller and assess its fuel economy potential. The development of the control structure as well as the definition of the testing method for energy efficiency assessment are central aspects of this work. The performance of the approach is tested on a light-duty commercial vehicle on a state-of-the-art 4-axis powertrain testbed. The findings demonstrate that the speed profile can be optimized to achieve a fuel reduction of up to $$13\%$$

13
%

while maintaining mission timing and comfort.
Driver Behaviour Characterization Using an Instrumented Steering Wheel Conscious/Unconscious Muscle Activation
Authors: Akutain Xabier Carrera , Comolli Francesco , Gobbi Massimiliano , Mastinu Giampiero , Radaelli Giovanni 
Published: October 2024

AbstractThe paper presents an Instrumented Steering Wheel (ISW) that measures the forces and moments that the driver applies at each hand. The ISW is equipped with two six-axis load cells and has the same inertia of a reference steering wheel fitted on normal production cars. The ISW is used to assess the conscious or unconscious application of forces at the steering wheel during a set of manoeuvres. The conscious steering actions refer both to sinusoidal steering input and handling circuit driving. A statistical analysis is performed to characterize the conscious driver behaviour. Referring to unconscious activation of muscles, a kick-plate pass-by manoeuvre is studied. Several drivers are employed for such tests. Typical behavioural patterns are found, describing how drivers apply forces and moments in conscious steering actions. The unconscious moment applied during a kick-plate manoeuvre may be even 20% of the maximum torque applied during counter-steering. The results can be used to develop new driver models, additionally, the ISW is proposed as a tool for properly defining the ADAS intervention logic to reduce the intrusiveness feeling felt by the driver and increase safety in case of unconscious steering actions.
Energetic Impact of Urban Cooperative Driving on the Example of Intersections
Authors: Sturm Axel , Flormann Maximilian , Henze Roman , Raksincharoensak Pongsathorn 
Published: October 2024

AbstractThe use of automated driving and connectivity can be an additional lever to reduce fuel and energy consumption in real driving and has an impact on the drive system and its dimensioning as well. In this paper we are addressing the impact urban cooperative driving on the energy consumption of electric vehicles and investigate further the impact of the drive system and their layout. To this end, a concept study is being conducted for a D-segment vehicle and two battery electric powertrains. In order to evaluate the impact, defined driving profiles from a connected intersection are used for simulation. Based upon the results the overall energetic impact of different intersection approaching strategies as well as the drive system impact are discussed. It showed that a cooperative intersection scenario leads to an energy reduction of 14% and first come first serve scenarios to an energy reduction of 7%.
Force Sensors for the Active Safety of Road Vehicles
Authors: Milivinti M. , Amadini M. , Ballo F. , Gobbi M. , Mastinu G. 
Published: October 2024

AbstractForce and moment measurement within road vehicles plays a break-through role in automotive engineering. Both wheel force transducers and instrumented hub carriers are considered in the paper. Both technologies have advantages and disadvantages. Active safety systems (ABS, ESP, up to full automated driving) are expected to be impacted by the measurement of forces and moments at the wheels. Friction potential evaluation and driver model development and monitoring are major field of research. Force and moment measurement technology may also be exploited for lightweight construction purposes. Promising technologies are the ones that don’t need RF data transfer, providing low latency for data transfer and are resiliency against cyber-attacks.
Towards Automated Driving: Findings and Comparison with ADAS
Authors: Donà Riccardo , Mattas Konstantinos , Albano Giovanni , Váss Sandor , Ciuffo Biagio 
Published: October 2024

AbstractThe present contribution provides preliminary empirical findings concerning the first-in-literature independent experimental characterization of a commercially viable automated driving system in Europe. In particular, the paper reports on the testing campaign involving a type-approved Automated Lane Keeping System (ALKS) equipped vehicle and its comparison with the comparable driving assistance feature technology from the same vehicle: the Adaptive Cruise Control (ACC) system. The results suggest that the ALKS shows substantially enhanced performances with respect to ACC. In particular, both the string stability metrics and the reaction time show remarkable improvements. Additionally, the increased stability is not obtained via resorting to a significantly higher time gap which further motivates that string stable car-following is indeed feasible.
Speed Control in the Presence of Road Obstacles: A Comparison of Model Predictive Control and Reinforcement Learning
Authors: Mandl P. , Jaumann F. , Unterreiner M. , Gräber T. , Klinger F. , Edelmann J. , Plöchl M. 
Published: October 2024

AbstractThe paper compares two optimal control methods — Reinforcement Learning and Model Predictive Control — for adaptive speed control in the presence of road obstacles to enhance ride comfort. Both methods use a model for training or prediction and a reward or cost function to achieve a desired control objective. Using the same quarter-car model and objective function for both methods, differences in planned speed profiles, optimality of the control objective, and differences in computational time are analysed through simulations over a series of cosine-shaped road bumps.
Road Friction Adaptive Lateral Control of Automated Vehicles with Differential Braking
Authors: Iatropoulos Jannes , Ahrenhold Tim , Salzwedel Leon , Henze Roman 
Published: October 2024

AbstractDifferential braking offers a promising approach to ensure lateral control of automated vehicles in the event of steering actuator failure. Braking interventions induce longitudinal forces on the wheel, which influence the vehicle’s yaw motion and thus enable lateral control along a given trajectory. Within the scope of this paper, a road friction adaptive system for lateral vehicle control by differential braking on a dry, wet, snow- and ice-covered road is derived simulatively. The investigated friction coefficient adaption is implemented by a variable path planning and calculated on the basis of an estimated road friction coefficient. The driving situation considered is based on the severe lane-change maneuver according to ISO 3888-2 for the investigation of vehicle dynamics and road-holding ability. The limitation of the trajectory results from the maximum yaw rate and expands the trajectory’s length for the different road friction coefficients.
Study on Accident-Avoidance Mechanism in Driver-Vehicle System When Activating Level-2 ADAS
Authors: Arai Norika , Kamimura Jinnosuke , Fujinami Yohei , Zhang Xingguo , Raksincharoensak Pongsathorn , Uechi Masaaki , Inoue Shintaro , Sugaya Fumio , Nogi Kazunori , Okita Toshinori , Hayashi Hideaki , Niki Keitaro 
Published: October 2024

AbstractAdvanced Driver Assistance Systems (ADAS) in passenger cars, such as Adaptive Cruise Control (ACC) and Lane Keeping Assist (LKA), have recently been widely deployed. However, these systems are not designed for high-risk situations, yet some drivers over trust the system and engage in secondary tasks. These behaviours may lead to serious accidents. On the other hand, it has been reported that the use of ACC reduced approximately half of the collision rate on highways based on public road data. This study aims to clarify the mechanism how ADAS affects driver behaviour and enhances accident prevention performance. Specifically, the focus is on the forward collision risk and the driver’s brake operating behaviour. Through the analysis using a driving simulator, this study compared driver behaviour in high collision risk situations in the case with and without ACC. The analysis of subject drivers’ internal parameters confirmed the driver behaviour that avoided a collision by using ADAS to take over control and brake earlier.
Accuracy Requirements of Camera-Based Depth Estimation for Urban Automated Driving
Authors: Westendorf M. , Thal S. , Ahrenhold T. , Henze R. 
Published: October 2024

AbstractFor autonomous driving in urban areas higher accuracy requirements for localization of surrounding traffic participants become apparent. The use of cost-efficient camera sensors shows potential for a performant depth estimation and can supplement perception systems to achieve redundancy. Current research focuses on improving the algorithms towards better performance whereas the application-oriented analysis of present estimation errors in relation to urban traffic scenarios is often neglected. Based on stereo and mono camera images, a benchmark analysis of rule- and deep learning-based depth estimation approaches is conducted in this work. The error-prone estimation results are then analyzed against braking distances of urban traffic scenarios simulated by a two-track model to analyze the criticality of different depth estimation approaches. The application-oriented evaluation shows that current approaches could already be used in real automated driving systems and enable the definition of requirements.
Determination of the Loss Behavior of Wheel Bearings Under Real Driving Conditions
Authors: Hartmann Lukas , Ohms Leon , Rebesberger Ron , Brandes Gerrit , Henze Roman 
Published: October 2024

AbstractThe characterization of component losses is typically conducted on dedicated test benches with the objective of enhancing component efficiency. Nevertheless, obtaining precise measurements of the actual loss contributions of components during real-world vehicle operations is often challenging. This challenge particularly pertains to wheel bearings, whose loss characteristics are efficiently delineated through the generation of efficiency maps on test benches at the Institute of Automotive Engineering. This submission presents a methodology for establishing these efficiency maps and introduces a developed methodology that enables the transfer of loss behavior from the component test into real-world driving conditions. Subsequently, the power losses are quantified across distinct driving domains, including urban, rural, and highway conditions. The outcomes of this methodology are contrasted with the losses observed on the test benches in order to identify potential avenues for reducing the losses of wheel bearings. Furthermore, the methodology is applied to homologation-relevant vehicle test benches with the objective of comparing the power losses under these conditions with real-world vehicle operations.
Time-Optimal Learning-Based LTV-MPC for Autonomous Racing
Authors: Guo Zijun , Yu Huilong , Xi Junqiang 
Published: October 2024

AbstractAutonomous racing is a time- and accuracy-critical application of vehicle motion planning and control techniques. Despite being promising for its ability to handle constraints, model predictive control (MPC) for autonomous racing is limited by the relatively low computational speed and the problem of model mismatch. In this work, we present a time-optimal linear-time-variant-MPC (LTV-MPC) that incorporates a min-time objective function, the friction ellipse constraint, and the successive linearization over the prediction horizon to improve computational speed and prediction accuracy. To tackle model mismatch, the proposed LTV-MPC is further combined with Gaussian process regression to learn the lateral tire force error. Compensation for the error is implemented over the prediction horizon and on the friction ellipse constraint. This work presents simulation validation on the racing track of Formula Student Autonomous China (FSAC) and experimental validation on a self-designed track. We show that compared with nonlinear MPC, the proposed LTV-MPC reduces the average computation time from 66 ms to 2.5 ms with a 0.6% increase in lap time. With learned tire force error, a 2% reduction in lap time can be achieved.
Speed Profile Definition for GLOSA Implementation on Buses Based on Statistical Analysis of Experimental Data
Authors: Vignarca Daniele , Arrigoni Stefano , Sabbioni Edoardo , Cheli Federico 
Published: October 2024

AbstractThe latest advancements in vehicle automation have revealed significant potential for enhancing traffic management via Advanced Driver Assist Systems (ADAS), benefiting both safety and environmental considerations. Green Light Optimal Speed Advisory (GLOSA) systems represent a significant application in the Cooperative-Intelligent Transportation System (C-ITS) field adopting Vehicle-to-Everything (V2X) communication technology. The literature nowadays addresses quite extensively the GLOSA, and C-ITS in general, for conventional vehicles like cars. At the same time, there is emerging research starting to involve also the public transportation vehicles within this framework. The focus for buses is typically posed either on the comfort and regularity of the service for passengers or on the energy consumption which is reduced by adopting suitable speed profiles for the vehicle, thus avoiding unnecessary stops. This work presents a statistical analysis based on experimental data collected in real-world urban scenarios over one entire year. The outcome of this analysis allows the design of speed profiles typical for a public transportation vehicle, accounting for features such as the bus stop station for getting passengers off and on.
Effect of Driving Force Control that Imitates the Function of Tire Lateral Force on Vehicle Dynamics
Authors: Katsuyama Etsuo 
Published: October 2024

AbstractVarious control methods have been proposed that aim to improve steering response, disturbance stability, and stabilize steering characteristics by distributing drive force to each wheel. Conventionally, controls have been constructed by combining those control methods and tuning gains for each control. However, such control design methods require a huge amount of man-hours, and besides, it is not clear which states are optimal. Therefore, in this paper, we first focused on the mechanism of tire lateral force. In general, vehicles can run stably against disturbances without implementing any feedback control. It was clarified that this is because the tire lateral force plays the role of a skyhook damper installed horizontally on the side of the vehicle. Therefore, we proposed a control method that brings out the maximum potential of tires based on the idea that tire longitudinal force should have the same function as tire lateral force.
Drifting Maneuver Investigation via Phase Plane Analysis of Experimental Data
Authors: Righetti Giovanni , dell’Annunziata Guido Napolitano , Farroni Flavio , Massaro Matteo , Lenzo Basilio 
Published: October 2024

AbstractThis paper presents a comprehensive analysis of experimental data on drifting maneuvers, using vehicle data collected by Stanford University with a professional driver. Vehicle dynamics during drifting, characterised by high sideslip angles and countersteering, are examined. By using a nonlinear single track model with nonlinear tires, this study compares real-car data to simulated models within a phase-plane framework. It also explores the application of saddle-node bifurcation theory to understand the abrupt changes in vehicle behaviour during drifting.
Black Ice Detection Based on Tire Friction Coefficient Estimation of Vehicle Longitudinal Model
Authors: Lee Seung-Yong , Lee Ho-Jong , Kim Gi-Woo 
Published: October 2024

AbstractBlack ice is a deadly hazard on the road because it is visually transparent and difficult to identify by driver’s naked eye while driving. Because tire friction on a black icy road surface is obviously smaller than normal road, the braking distance significantly increases and leads to severe traffic accidents. Road hazard detection such as black ice has been actively attempted so far, usually focusing on methodology using intelligent vision systems (e. g., cameras). However, current image-based methods are prone to reduced low accuracy due to their susceptibility to vibrations transmitted from road surfaces to vehicles. In addition, incorporating cameras and light detection and ranging sensors increases the complexity and computational burden, especially when extending their functionality to include road surface classification. Therefore, we investigate the potential of new road surface classification based on vehicle longitudinal velocity and tire effective radius estimation from vehicle longitudinal model. This study explores a sensor-fusion type indirect road surface classification algorithm based on Kalman filtering.
Experimental Evaluation on Effects of Torque Vectoring Using Four In-Wheel Motor Independent Torque Control
Authors: Homma Yota , Yamakaji Yoshihiro , Kajitani Koji , Ford Richard , Furukawa Yoshimi 
Published: October 2024

AbstractThis paper explores the effects of Direct Yaw Control (DYC) on driver’s maneuverability feeling and vehicle dynamics. DYC is implemented in the experimental vehicle equipped with four-wheel independent controlled In-Wheel Motors (IWMs). The effects on vehicle dynamics are validated through open-loop testing. Subsequently, the closed-loop test is conducted to confirm the correlation between subjective evaluation and Vehicle dynamics data.
A Novel Torque Vectoring Approach to Enhance Driving Experience
Authors: Paparone Marco , Pino Alessandro , Giacomel Filippo , Bussalai Giovanni , Macaluso Andrea , Lamps Antoine , Mavroudakis Basileios 
Published: October 2024

AbstractThe current state of Direct Yaw Control literature has reached an advanced level, yielding compelling results in stabilizing vehicle behavior, and enhancing overall performance. However, conventional approaches exhibit limitations in addressing highly transient lateral dynamics, particularly during conditions of rapidly increasing vehicle sideslip angles, leading to poor controllability and reduced intuition for the driver. This paper introduces a novel extension to the established yaw rate tracking technique, integrating the trivial yaw rate feedback control with a less conventional model-based feedforward term, complemented by an innovative sideslip rate tracking loop. The primary objective of this term is to smoothen vehicle cornering response, effectively dampening oscillations in the vehicle’s behavior without compromising time delays to driver inputs. The intended outcome is not only enhancing safety but also delivering a more intuitive and enjoyable driving experience. The effectiveness of the approach is demonstrated by the results obtained through detailed Driver-in-Motion (DiM) sessions.
Basic Study on the Effect of Driver Condition on Steering Burden
Authors: Uchino Daigo , Kintou Ri , Kobayashi Ikkei , Kuroda Jumpei , Ogawa Kazuki , Ikeda Keigo , Kato Taro , Endo Ayato , Kato Hideaki , Narita Takayoshi 
Published: October 2024

AbstractWith the widespread use of steer-by-wire in automobiles, research is being conducted on high steering gear ratio systems that reduce the burden on the driver, which enables steering with high sensitivity. However, this decreases the resolution during steering and worsens operability, making steering operation more difficult. In addition, if the amount of steering operation and the magnitude of steering reaction torque are not appropriate for the driver, it may cause operation errors. In a highly sensitive steering system, a small change in steering reaction torque has a large potential to affect the driver. The driver’s steering burden varies depending on conditions, so it is not always possible to reduce the steering burden. Therefore, we evaluated the driver’s steering burden on different days and examined the effect of steering reaction force torque on the steering burden. The results confirmed that the deviation of the burden increases as the load increases.
Design and Analysis of Traction Control Strategies for Icy Road Conditions
Authors: Mihalkov M. , Caponio C. , Hankovszki Z. , Sorniotti A. , Montanaro U. , Gruber P. 
Published: October 2024

AbstractTraction control (TC) plays a key role in improving vehicle safety, especially for driving scenarios involving extremely low levels of tyre-road friction. In this paper a novel deep reinforcement learning (DRL) based TC strategy is formulated and its performance is compared against a nonlinear model predictive control (NMPC) solution for a simulated straight-line acceleration manoeuvre on icy road conditions. The paper explores the design and assessment of the proposed controllers using a vehicle model experimentally validated on ice. The simulation results show that the DRL solution outperforms the NMPC strategy by reducing the wheel slip ratio peaks and oscillations at the start of an acceleration manoeuvre. Additionally, it converges more quickly to the reference slip and is more computationally efficient.
Design and Verification of an Adaptive State-Tuned Power Management System for Series Hybrid Electric Tracked Vehicles
Authors: Çeliksöz Dersu , Göçer İsmail , Gülseren Kerim Arda 
Published: October 2024

AbstractThe accelerated shift towards electrification in the tracked vehicle industry, particularly concerning off-road and military vehicles, poses challenges due to their intensive power consumption and limited charging infrastructures. Addressing these challenges, this paper focuses on the development of an adaptive state-tuned power management system for a series hybrid electric tracked vehicle. The vehicle's architecture includes an electric traction unit and a hybrid powerpack. The core of this research involves designing a dynamic power allocation system that adjusts the power sharing between the battery and a generator set under varying operational conditions. Through a systematic approach, a power management algorithm featuring a hierarchical controller structure to manage the power flow is designed. Simulation tests, both Driver-in-the-Loop (DIL) and Model-in-the-Loop (MIL), were employed to verify the system’s performance. Results indicate that the algorithm coordinates power distribution, ensuring optimal performance while adhering to the system's constraints and adapting to immediate power demands, demonstrating its potential for enhancing hybrid vehicle operations in demanding maneuvers.