Green Light Optimal Speed Advisory (GLOSA) systems are a key innovation in Intelligent Transportation Systems (ITS), aiming to optimise vehicle speed profiles while harmonising with traffic light schedules. This paper presents a GLOSA system based on Non-linear Model Predictive Control (NMPC). The proposed system uses real traffic light data and accounts for bus stop dwell times to provide optimal speed alerts. The system has been assessed using a realistic simulation scenario built upon real-world data collected in Milan and implemented within IPG TruckMaker environment. Simulation results demonstrate the effectiveness of the proposed approach compared to a previously developed rule-based algorithm, as it enables the prediction of the vehicle’s future states while ensuring intersection crossings during green light phases. Moreover, real-time feasibility has been verified through deployment as a standalone ROS C++ node, achieving computational times of around 60 ms, thereby providing a new solution within the NMPC update interval of 1 s.

Hydroplaning poses a significant risk to road safety, as a water wedge between the tires and the road reduces the vehicle's responsiveness to driver inputs. This phenomenon is influenced by factors such as vehicle speed, water depth, and tire wear, directly impacting parameters like cornering stiffness, relaxation length, and friction coefficient. This study evaluates a control logic designed to assist drivers during hydroplaning. A novel tire model was developed and integrated into a 14-degree-of-freedom vehicle model to simulate hydroplaning effects, while an Advanced Driver-Assistance System (ADAS) was designed to enhance vehicle control. A dynamic driving simulator was used to test driver interactions with the proposed control system and assess its effectiveness. The ADAS was evaluated in double-lane-change and high-speed turn scenarios, where drivers of varying experience levels were asked to complete the maneuver at different speeds, both with and without the control system's assistance. The control strategy helped the drivers to complete the maneuvers, reducing their effort on the steering wheel. In conclusion, this study provided valuable insights into how everyday drivers manage hydroplaning, highlighting ADAS's potential to improve vehicle control and safety in such conditions.

With the increasing emphasis on environmental sustainability, the electrification of urban public bus fleets has gained significant attention. Understanding the factors influencing the energy consumption of battery-electric buses (BEBs) is crucial for enhancing their energy efficiency. Therefore, it is crucial to identify the subsystems that contribute most to energy consumption and understand how operational factors influence them. This paper presents a comprehensive analysis of BEB energy consumption based on experimental measurements performed with a 12 m fully electric battery bus. The main limitations of this study stem from the use of a single vehicle over a total period of 18 days, during which 187 routes were completed. Additionally, sandbags were used as ballast in place of actual passengers. Various parameters, including the number of passengers, drivers, route characteristics, environmental conditions, and traffic, were analyzed to assess their impact on BEB energy consumption. Data related to the energy consumed by various bus utilities were collected through the vehicle’s CAN network, with a sampling rate of 1 measurement per second. These data were analyzed both daily and per route, revealing the breakdown of energy consumption among different utilities and highlighting those responsible for the highest energy use. The results correlate the total distance traveled, service duration, average speed, driver’s driving style, route characteristics, internal and external temperatures, and air-conditioning system’s reference temperature with the energy consumption of the traction motors and climate control system. In addition, the correlation between the driver, vehicle acceleration, and throttle pedal use, and the energy consumed by the electric traction motor is presented.

The increased popularity of electric vehicles featuring distributed powertrains is enabling an easy and cost-effective implementation of torque vectoring. This is a renowned technique for controlling vehicle lateral dynamics having the objective of improving both vehicle handling and stability. Nevertheless, the application of torque vectoring at the front axle can increase the difficulty of usual driving tasks. This is because differential longitudinal forces at front tires generate a steering wheel torque, which can be badly perceived by the driver, up to the point of jeopardizing the benefits of having a torque vectoring control. The aim of this article is thus to study in detail the steering torque corruption caused by front axle torque vectoring for proposing some electric power steering control strategies compensating for this effect. Indeed, the electric power steering controllers developed in this study are designed based on the analytical derivation of the torque steer theory, which comprehensively highlights the contribution of each tire contact action to the steering torque. This innovative approach allows including the effect of front axle yaw moment in the generation of the steering feedback, which is currently neglected in the literature. Driver-in-the-loop simulations at a dynamic driving simulator are adopted for assessing the suitability of the proposed electric power steering control strategies in restoring proper steering feedback when the vehicle is featuring torque vectoring capabilities at the front axle. Moreover, different knowledge levels about the vehicle states are considered in the proposed electric power steering control strategies, proving that the compensation strategy can be effectively deployed even in production vehicles, which require the estimation of some key parameters for the torque steer theory, such as tire contact forces.

Rolling mill drive-trains, driven by AC or DC motors, have historically experienced premature component fatigue failures even when the perceived operating load is well below the design limit. This is often related to low damped torsional vibrations, especially to self-excited vibrations in rolling slippage (especially during threading and tailing out of a rolled piece), overloading the elements of the drive-train. Long transmission shafts make these vibrations become more critical. A regulator aimed at damping the torsional vibrations of the rolling mill drive-train and thus reducing the electric motor speed fluctuations is presented in this paper. The proposed regulator relies on a reduced order state observer able to estimate the shaft torque amplifications due to the working process. The capability of the regulator to damp out torsional vibrations has been verified through simulations on a lumped parameter torsional model of a single-stand rolling mill accounting for torsional deformability of the power-train shafts.

The development of Advanced Driver Assistance Systems (ADAS) requires a controlled testing environment for evaluating perception, decision-making, and control algorithms. This paper presents a connected infrastructure for ADAS testing, integrating roadside LiDAR and stereo cameras, a 5G-based communication network, and an instrumented prototype vehicle. The development included both a digital twin for virtual testing and a physically instrumented real-world area. The system supports key scenarios, including automated parking, safety-focused ADAS for Vulnerable Road Users (VRUs), and collaborative localization. A case study on LiDAR-based cooperative localization demonstrates improved positioning accuracy, highlighting the benefits of the connected infrastructure for autonomous driving and the potential of a custom-designed test area for this type of applications.

Platooning represents an advanced driving technology designed to assist drivers in traffic convoys of varying lengths, enhancing road safety, reducing driver fatigue, and improving fuel efficiency. Sophisticated automated driving assistance systems have facilitated this innovation. Recent advancements in platooning emphasize cooperative mechanisms within both centralized and decentralized architectures enabled by vehicular communication technologies. This study introduces a cooperative route planning optimization framework aimed at promoting the adoption of platooning through a centralized platoon formation strategy at the system level. This approach is envisioned as a transitional phase from individual (ego) driving to fully collaborative driving. Additionally, this research formulates and incorporates travel cost metrics related to fuel consumption, driver fatigue, and travel time, considering regulatory constraints on consecutive driving durations. The performance of these cost metrics has been evaluated using Dijkstra's and A* shortest path algorithms within a network graph framework. The results indicate that the proposed architecture achieves an average cost improvement of 14% compared to individual route planning for long road trips. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

In recent years training has started adopting innovative experimental techniques to assess athletes’ behaviour to define dedicated and focalized training strategies to further increase their performances. The mostly used technique is video analysis since it can be used for almost any sport and gives a direct and intuitive feedback. However, video analysis is not able to catch important aspects of the athlete’s movement, in particular forces exerted by the athletes on interacting elements (ground, grips, …). Thus, special and costly training tools have been designed and tested. The drawback of these tools is that they typically cannot be used in real competitions.

Agricultural vehicle operators are exposed to intense vibrations mainly induced by soil unevenness. Since most of tractors are not equipped with any chassis suspension, the seat is the only system able to reduce the vibrations experienced by the operator. Traditional passive seats amplify vibrations at frequencies close to their natural frequencies. The first natural frequency of typical passive seats with an air spring and a hydraulic shock-absorber is between 1.5 and 4Hz. Thus their efficiency is poor for low frequencies and high amplitudes. In order to improve comfort of operators, active or semi-active suspensions for the seat can be introduced. An active suspension system for the seat of an agricultural vehicle relying on an active air spring is presented in this paper. The capability of the system of improving comfort of operators has been evaluated through simulations carried out with a validated model of the entire vehicle. Results of the proposed active suspension system are compared with ones provided by the passive suspension and the ones provided by an active system where the traditional passive shock-absorber is substituted by a controllable hydraulic actuator.

Bobsleigh is a winter sport in which teams make timed runs down narrow, twisting, banked, iced tracks in gravity-powered sleds. Up to now, the optimization of bobsleighs has been carried out on the basis of athletes feedback. This has led to small modifications of the sled without univocal guidelines. This paper presents the results of an experimental campaign carried out at Cesana Pariol Olympic track with an instrumented two man bobsleigh. Aim of the experimental campaign was to assess the bobsleigh dynamics in order to objectively evaluate its performances and to set up a numerical model suitable for structural optimization purposes. During the tests, the bobsleigh was instrumented with an inertial gyroscopic platform to measure the vehicle dynamics, one optical sensor to measure the bobsleigh speed and sideslip angle, one potentiometer to measure the steer angle imposed by the driver. Moreover in order to estimate ice-skate contact forces, dynamometric axles have been designed and used during the tests. In particular forces have been reconstructed on the basis of the deformations measured by strain gauges placed on the axles connecting the sled with the skates.

The research activity aims at defining specific Operational Design Domains (ODDs) representative of Italian traffic environments. The paper focuses on the human-machine interaction in Automated Driving (AD), with a focus on take-over scenarios. The study, part of the European/Italian project “Interaction of Humans with Level 4 AVs in an Italian Environment - HL4IT”, describes suitable methods to investigate the effect of the Take-Over Request (TOR) on the human driver’s psychophysiological response. The DriSMI dynamic driving simulator at Politecnico di Milano has been used to analyse three different take-over situations. Participants are required to regain control of the vehicle, after a take-over request, and to navigate through a urban, suburban and highway scenario. The psychophysiological characterization of the drivers, through psychological questionnaires and physiological measures, allows for analyzing human factors in automated vehicles interactions and for contributing to advance AD technologies. Physiological signals, including electrocardiographic (ECG) and electroencephalographic (EEG) are acquired synchronously with eye-tracking and instrumented steering wheel signals throughout the entire test. The use of dynamic driving simulation enhances the study’s efficacy, facilitating early-stage development insights crucial for the advancement of AD technologies.

The safe deployment of Connected, Cooperative and Automated Mobility (CCAM) systems needs to take into account advanced human-machine interaction. In fact, CCAM is going to change both the cooperation between the vehicle and the human driver and the interaction with road users. This paper presents DriSMi, an advanced cable-driven dynamic driving simulator that enables safe, affordable and reliable CCAM testing. DriSMi can be used with the objectives of (1) modelling and testing the technology and (2) characterizing and modelling the driver, in particular analysing and modelling the driver’s behaviour, ergonomics and safety in different infrastructure/weather/traffic scenarios. After describing the features of DriSMi, a CCAM scenario and a procedure for assessing the acceptability of Adaptive Cruise Control are presented.

Multi-purpose agricultural tractors are vehicles commonly utilized in off-road environments with significant slope variations. The rollover stability of these vehicles during their operational tasks is a crucial aspect. This paper aims to investigate the impact of the chassis torsional flexibility on improving the rollover stability of an agricultural vehicle. For this purpose, numerical analyses are performed using a multi-body model of the vehicle, which was validated with experimental measurements. Static simulations are performed to examine the quasi-static rollover angle under different loading conditions, taking into account various levels of chassis torsional stiffness. Dynamic simulations are then run to replicate the vehicle behavior when encountering a pothole on a banked road under different loading conditions. The results underline that by decreasing the torsional flexibility of the chassis the road holding increases, although it only marginally decreases the rollover angle.

Torque vectoring is a widely known technique to improve vehicle handling and to increase stability in limit conditions. With the advent of electric vehicles, this is becoming a key topic since it is possible to have distributed powertrains, i.e., multiple motors are adopted, in which each motor is controlled separately from the others. Moreover, electric motors deliver the torque required by the controller faster and more precisely than internal combustion engines, active differentials and conventional hydraulic brakes. The state of the art of Direct Yaw Moment Control (DYC) techniques, ranging from classical to modern control theories, are analyzed and discussed in this paper. The aim is to give an overview of the currently available approaches while identifying their drawbacks regarding performances and robustness when dealing with common issues like model uncertainties, external disturbances, friction limit and common state estimation problems. This contribution analyzes all the steps from the lateral dynamics reference generation to the desired control action computation and allocation to the available actuators. In addition, some of the presented control logic is evaluated in a simulation environment for a passenger car. Results of both open-loop and closed-loop maneuvers allow the comparison and clarification of each control strategy’s key advantages.

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.

Heavy vehicles entering and exiting a tunnel at high speed under strong crosswinds is a particularly critical condition since the aerodynamic load change drastically, greatly affecting the lateral stability of the vehicle. Active control systems (active suspensions, active front steering, etc.) and infrastructure elements (e.g. wind fences) are proposed to reduce the induced risks. To help the design of these devices, the present paper investigates the response of the vehicle-driver system in the case of a high-sided lorry entering and exiting a tunnel under crosswind, by using driver-in-the-loop simulations. The study was performed using the dynamic driving simulator of Politecnico di Milano and 28 test drivers. Vehicle and aerodynamic models have been developed to reproduce the phenomenon in a highly immersive environment. During the tests, several combinations of vehicle and wind speed were considered. The effect of turbulence and vehicle loading condition (empty and full) was also investigated.

Wear is becoming a topic of major attention for tyres, affecting also other performances. Therefore, its estimation is of utter importance under several points of view, such as predictive maintenance and vehicle dynamics controllers. Indoor testing is emerging as an alternative way for predicting wear compared to on-road outdoor tests, which nowadays represent the standard methodology. Indoor tests, in fact, are performed in a more controllable environment, reducing testing time and costs. However, several challenges must be faced to reproduce indoor the same wear rate/shape obtained in real on-road working conditions. The present paper focuses one of the critical aspects for indoor testing: the definition of the load cycle to be applied to a tyre, i.e. the time history of forces, slip and angles to be provided as an input to the wear machine. Specifically, a clustering approach able to extract from outdoor data a limited set of manoeuvres representative of a given outdoor wear track is proposed.

Full electric vehicles with multiple and independently controlled powertrains allow for an improvement of vehicle handling capabilities both in steady state and in transient manoeuvres. This paper focuses on active lateral dynamics control of an electric vehicle equipped with 4 in-wheel motors and active rear steering. An integral terminal sliding mode controller (ITSMC) is derived starting from the linearized single track model with the addition of the rear wheel steering angle. The controller has a multi-input multi-output structure and is designed to track vehicle yaw rate and sideslip angle reference quantities through torque vectoring and active rear steering actuation. A novel approach for calculating reference sideslip angle and yaw rate using a logistic function is also presented in this paper. The ITSMC relies on real time knowledge of sideslip angle which cannot be measured in the real vehicle, thus it is estimated through the addition of an extended Kalman filter to the control loop. The performance of the controller is tested with VI-CarRealTime 14 degrees of freedom nonlinear model both for steady state and transient manoeuvres. Simulation results show a good tracking of the reference value with no chattering issues and with an improved behaviour if compared to a sliding mode controller from literature.

With the automotive industry's shift towards sustainability and energy efficiency, optimizing vehicle handling dynamics has become secondary. Additionally, there is a growing trend towards comfort-oriented design over handling performance. However, advancements such as integrating multiple independently controlled electric motors enable torque vectoring, offering a promising solution for reconciling these conflicting objectives. This paper proposes a novel approach to jointly improve vehicle handling and energy efficiency. Advanced simulation techniques are used to explore various suspension configurations to balance cornering performance and energy consumption. A torque vectoring controller is then designed in combination with meticulously tuned suspensions. This innovative approach, which considers active control design alongside suspension setup, achieves superior performance. Desired vehicle cornering capabilities are attained while ensuring significant efficiency in straight-line driving, which constitutes most road driving.