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As an era of autonomous driving approaches, it is necessary to translate handling comfort - currently a responsibility of human drivers - to a vehicle imbedded algorithm. Therefore, it is imperative to understand the relationship between perceived driving comfort and human driving behaviour. This paper develops a methodology able to generate the information necessary to study how this relationship is expressed in highway overtakes. To achieve this goal, the approach revolved around the implementation of sensor Data Fusion, by processing data from CAN, camera and LIDAR from experimental tests. A myriad of variables was available, requiring individuating the key-information and parameters for recognition, classification and understanding of the manoeuvres. The paper presents the methodology and the role each sensor plays, by expanding on three main steps: Data segregation and parameter selection; Manoeuvre detection and processing; Manoeuvre classification and database generation.

In mathematical and mechanical modeling terms, automotive seating is characterized by boundary conditions at the nonlinear contact interfaces. These contact interfaces are subjected to vibro-impacts (slaps) and frictional slips. The slaps occur in contact interfaces at high amplitude vibrations, being characterized by very short duration, rapid dissipation of energy and large accelerations and decelerations. By considering friction in contact interface modeling, the simulation of the interaction between the driver and the vehicle seat becomes more realistic. Vibro-impacts and frictional slips can be simultaneously developed in a contact surface. The boundary conditions identification for a seat and a wide range of drivers' body types is performed using the concept of interference distance or penetration. The interference distance is introduced as an optimization problem. It is shown that the optimization problem provides robust solutions to minimum distance and interference problems.

In automotive market, today more than in the past, it is very important to reduce time to market and, mostly, developing costs before the final production start. Ideally, bench and on-road tests can be replaced by multi-body studies because virtual approach guarantees test conditions very close to reality and it is able to exactly replicate the standard procedures. Therefore, today, it is essential to create very reliable models, able to forecast the vehicle behavior on every road condition (including uneven surfaces). The aim of this study is to build an accurate multi-body model of a heavy-duty truck, check its handling performance, and correlate experimental and numerical data related to comfort tests for model tuning and validation purposes. Experimental results are recorded during tests carried out at different speeds and loading conditions on a Belgian blocks track. Simulation data are obtained reproducing the on-road test conditions in multi-body environment.

Urban mobility represents one of the most critical global challenges nowadays. Several options regarding design and power sources technologies were recently proposed; among which electric and hybrid vehicles are quite successful to meet the increasingly restrictive environmental targets. This significant goal may affect the perceived vehicle comfort and drivability, especially in everyday urban scenarios. The purpose of this paper is to carry out a comparison in terms of comfort between vehicles belonging to different categories, but all designed for urban mobility: an electric 2-passenger quadricycle used during the demonstration phase of the European project STEVE, an internal combustion engine 2-passenger car (Smart Fortwo), an electric 4-passenger car (Bolloré Bluecar) and an internal combustion engine 4-passenger car (Fiat 500). Leading European car-sharing services use the last three car models.

This paper presents the methodology adopted by Politecnico di Torino Vehicle Dynamics Research Team to obtain objective indices for the evaluation of the comfort during the gear change process. Some test drivers and different passengers traveled on a test vehicle and assigned marks on the basis of their subjective feeling of comfort during the gearshifts. The comparison between the most significant subjective evaluations and the experimental values obtained by the instruments located on the vehicle is presented. As a consequence, some indices (based on physical parameters) to evaluate the efficiency and the comfort of the gearshift process are obtained. They are in good agreement with the subjective evaluations of the drivers and the passengers. The second part of the paper presents a driveline and vehicle model which was conceived to reproduce the phenomena experimented on the vehicle. The experimental validation of the model is presented.

H-ergo, a concept of light electric vehicle devoted to personal mobility, will here be presented. H-ergo is a low-noise, user-friendly, zero-emission vehicle, with a pleasant style. Its main features include high payload/vehicle mass ratio, electric energy supplied either by batteries or by fuel cell, ergonomic style in order to transport a driver or a person whit mobility problems, chassis design to minimize cost of production, variable wheelbase (through electric actuator). The paper presents the main ideas on which the vehicle design was based and summarizes the most important results obtained.

The first step toward a braking system ‘by wire’ is Electro-Hydraulic Braking System (EHB). The paper describes a method to evaluate through virtual experimentation the actual improvement in vehicle behaviour, from the point of view of both handling and comfort, including also pedal feeling, due to EHB. The first step consisted in modelling the hydraulic unit, comprehensive of sensors. Then it was conceived a control logic devoted to medium-low intensity braking manoeuvres, without ABS intervention, to determine an optimal braking force distribution and pedal feeling depending on the manoeuvre. A failsafe strategy, complete of on board diagnosis, to prevent dangerous system behaviour in the eventuality of a component failure was carried out and tested. Finally, EHB wheel pressure sensors were used to improve both ABS performance, increasing the adherence estimation, and Vehicle Dynamics Control (VDC) performance, through a more precise actuation.

This paper presents a novel strategy for the control of the motor torques of a rear wheel drive electric vehicle with the objective of improving the lap time of the vehicle around a racetrack. The control strategy is based upon increasing the size of the friction circle by implementing torque vectoring and tire slip control. A two-level nested control strategy is used for the motor torque control. While the outer level is responsible for computing the desired corrective torque vectoring yaw moment, the inner level controls the motor torques to realize the desired corrective torque vectoring yaw moment while simultaneously controlling the wheel longitudinal slip. The performance of the developed controller is analyzed by simulating laps around a racetrack with a non-linear multi-body vehicle model and a professional human racing driver controller setting.

The term driveability describes the driver's complex subjective perception of the interactions with the vehicle. One of them is associated to longitudinal acceleration aspects. A relevant contribution to the driveability optimization process is, nowadays, realized by means of track tests during which a considerable amount of driveline parameters are tuned in order to obtain a good compromise of longitudinal acceleration response. Unfortunately, this process is carried out at a development stage when a design iteration becomes too expensive. In addition, the actual trend of downsizing and supercharging the engines leads to higher vibrations that are transmitted to the vehicle. A large effort is therefore dedicated to develop, test and implement ignition strategies addressed to minimize the torque irregularities. Such strategies could penalize the engine maximum performance, efficiency and emissions. The introduction of the dual mass flywheel is beneficial to this end.

The paper presents a simulation methodology created to support an integrated safety system development process which was tested for the side impact collision load case. The methodology is based on the coupled and complementary use of two software packages: PreScan and Madymo. PreScan was utilized for designing two traffic scenarios and the sensing and control systems for the side collision recognition, while Madymo was utilized for assessing the effects of pre-crash deployment of thorax airbag. The collision conditions from the scenarios were used as input to define a Madymo side collision model of the host vehicle and to investigate and optimize several airbag deployment parameters: pre-crash deployment time, airbag permeability, vent hole size and vent hole opening time.

The paper deals with the virtual and experimental analysis of two commercial Mechanical Brake Assist systems. They are described in detail, then modeled and experimentally evaluated through a Hardware-In-the-Loop test bench and road tests. Three different kinds of drivers are compared, from the point of view of the performance increase promised by Brake Assist during an emergency brake maneuver. The three driver types are based on the measurement of the behavior of real drivers, as it is presented in specific research activities in literature.

A four wheel steer control logic is described. A first control logic release, obtained during previous research activity, is based only on feed forward (F.F.) but is here upgraded merging closed loop control (C.L.). Integration between F.F. and C.L. is described. Rear steering electromechanical actuator frequency response is analyzed, in order to consider its not ideal behaviour during control logic design. Several simulation are performed to qualitatively evaluate the error committed considering an ideal actuator during the control logic design. Specific manoeuvres are chosen to investigate about active system influence on vehicle handling; a 14 degrees of freedom vehicle model is validated in order to compare simulation results with experimental data.

Autonomous Driving is one of the main subjects of academic research and one important trend in the automotive industry. With the advent of self-driving vehicles, the interest around trajectory planning raises, in particular when a customer-oriented analysis is performed, since more and more the carmakers will have to pay attention to the handling comfort. With that in mind, an experimental approach is proposed to assess the main characteristics of human driving and gain knowledge to enhance quality of autonomous vehicles. Focusing on overtaking maneuvers in a highway environment, four comfort indicators are proposed aiming to capture the key aspects of the chosen paths of a heterogeneous cohort. The analysis of the distribution of these indicators (peak to peak lateral acceleration, RMS lateral acceleration, Smoothness and Jerk) allowed the definition of a human drive profile.

Ride comfort assessment is undoubtedly related to the interaction between the vehicle tires and the road surface. Indeed, the road profile represents the typical input for tire vertical load estimation in durability analysis and for active/semi-active suspension controller design. However, the road profile evaluation through direct experimental measurements involves long test time and excessive cost required by professional instrumentations to detect the road irregularities with sufficient accuracy. An alternative is shifting attention towards efficient and robust algorithms for indirect road profile evaluation. The object of this work aims at providing road profile estimation starting from vehicle dynamics measurements, through accessible and traditional sensors, with the application of a linear Kalman filter algorithm.

In the face of growing concerns about environmental sustainability and urban congestion, the integration of eco-driving strategies has emerged as a pivotal solution in the field of the urban transportation sector. This study explores the potential benefits of a CAV functioning as a virtual eco-driving controller in an urban traffic scenario with a group of following human-driven vehicles. A computationally inexpensive and realistic powertrain model and energy management system of the Chrysler Pacifica PHEV are developed with the field experiment data and integrated into a forward-looking vehicle simulator to implement and validate an eco-driving speed planning and energy management strategy assuming longitudinal automation. The eco-driving algorithm determines the optimal vehicle speed profile and energy management strategy.