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The goal of this interview analysis was to explore and document the perceptions of two unique lane centering systems (S90’s Pilot Assist and CT6’s Super Cruise). Both systems offer a similar type of functionality (adaptive cruise control and lane centering), but have significantly different design philosophies and HMI (Human-Machine Interface) implementations. Twenty-four drivers drove one of the two vehicle models for a month as part of a field operational test (FOT) study. Upon vehicle return, drivers took part in a 60-minute semi-structured interview covering their perceptions of the vehicle’s various advanced driver-assistance systems (ADAS). Transcripts of the interviews were coded by two researchers, who tagged each statement with relevant system and perception code labels. For analysis, the perception codes were grouped into larger thematic bins of safety, comfort, driver attention, and system performance.

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.

The fuel energy consumption of subsonic air transportation is examined. The focus is on identification and quantification of fundamental engineering design tradeoffs which drive the design of subsonic tube and wing transport aircraft. The sensitivities of energy efficiency to recent and forecast technology developments are also examined.

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.

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.

This paper presents the results of a study of how people interacted with a production voice-command based interface while driving on public roadways. Tasks included phone contact calling, full address destination entry, and point-of-interest (POI) selection. Baseline driving and driving while engaging in multiple-levels of an auditory-vocal cognitive reference task and manual radio tuning were used as comparison points. Measures included self-reported workload, task performance, physiological arousal, glance behavior, and vehicle control for an analysis sample of 48 participants (gender balanced across ages 21-68). Task analysis and glance measures confirm earlier findings that voice-command interfaces do not always allow the driver to keep their hands on the wheel and eyes on the road, as some assume.

The challenge of developing a robust, real-time driver gaze classification system is that it has to handle difficult edge cases that arise in real-world driving conditions: extreme lighting variations, eyeglass reflections, sunglasses and other occlusions. We propose a single-camera end-toend framework for classifying driver gaze into a discrete set of regions. This framework includes data collection, semi-automated annotation, offline classifier training, and an online real-time image processing pipeline that classifies the gaze region of the driver. We evaluate an implementation of each component on various subsets of a large onroad dataset. The key insight of our work is that robust driver gaze classification in real-world conditions is best approached by leveraging the power of supervised learning to generalize over the edge cases present in large annotated on-road datasets.

Advanced driver assistance systems (ADAS) are an increasingly common feature of modern vehicles. The influence of such systems on driver behavior, particularly in regards to the effects of intermittent warning systems, is sparsely studied to date. This paper examines dynamic changes in physiological and operational behavior during lane departure warnings (LDW) in two commercial automotive systems utilizing on-road data. Alerts from the systems, one using auditory and the other haptic LDWs, were monitored during highway driving conditions. LDW events were monitored during periods of single-task driving and dual-task driving. Dual-task periods consisted of the driver interacting with the vehicle’s factory infotainment system or a smartphone to perform secondary visual-manual (e.g., radio tuning, contact dialing, etc.) or auditory-vocal (e.g. destination address entry, contact dialing, etc.) tasks.

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.

In today's business climate rapid access to, and implementation of, new technology is essential to enhance competitive advantage. In the past, universities have been used for research contracts, but to fully utilize the intellectual resources of education institutions, it is essential to approach these relationships from a new basis: alliance. Alliances permit both parties to become active participants and achieve mutually beneficial goals. This paper will examine the drivers and challenges for industrial -- university alliances from both the industrial and academic perspectives.

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.

A method for detecting drivers' intentions is essential to facilitate operating mode transitions between driver and driver assistance systems. We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. HMM-based steering behavior models for emergency and normal lane changes as well as for lane keeping were developed using a moving base driving simulator. Analysis of these models after training and recognition tests showed that driver behavior modeling and recognition of different types of lane changes is possible using HMMs.

The nearly 77 million baby boomers, born between 1946 and 1964, can say that they are the automobile generation. Now turning 60 one every seven seconds, what are the new safety challenges and opportunities posed by the next generation of older adults? This paper presents a modified Haddon matrix to identify key product development, design and liability issues confronting the automobile industry and related stakeholders. The industry is now at a critical juncture to address the development of key technological innovations as well as the changing policy and liability environments being reshaped by an aging population.

Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.

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.