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As the automotive industry prepares to roll out an unprecedented range of fully electric propulsion vehicle models over the next few years - it really brings to a head for folks responsible for brakes what used to be the subject of hypothetical musings and are now pivotal questions for system design. How do we really go about designing brakes for electric vehicles, in particular, for the well-known limit condition of descending a steep grade? What is really an “optimal’ design for brakes considering the imperatives for the entire vehicle? What are the real “limit conditions” for usage that drive the fundamental design? Are there really electric charging stations planned for or even already existing in high elevations that can affect regenerative brake capacity on the way down? What should be communicated to drivers (if anything) about driving habits for electric vehicles in routes with significant elevation change?

There has been an increasing interest in exploring the potential to reduce energy consumption of future connected and automated vehicles. People have extensively studied various eco-driving implementations that leverage preview information provided by on-board sensors and connectivity, as well as the control authority enabled by automation. Quantitative real-world evaluation of eco-driving benefits is a challenging task. The standard regulatory driving cycles used for measuring exhaust emissions and fuel economy are not truly representative of real-world driving, nor for capturing how connectivity and automation might influence driving trajectories. To adequately consider real-world driving behavior and potential “off-cycle” impacts, this paper presents four collaborative evaluation methods: large-scale simulation, in-depth simulation, vehicle-in-the-loop testing, and vehicle road testing.

This paper discusses radio usage habits observed during analysis of 700 hours of video sampled from the 100-Car Naturalistic Driving Study database. Analysts used large-scale printouts of each vehicle's radio faceplate and recorded interactions based on video analysis of hand movement and location (without the assistance of audio recordings). The duration and specific manipulations or adjustments were recorded for each interaction. The results summarize the length and type of interactions, most often-used controls, and total percentage of time drivers interacted with the radio.

Sixteen participants aged 55–65yrs provided deBoer scale ratings of discomfort glare for a vehicle with horizontally swiveling HID headlamps and a vehicle with the same headlamps that did not swivel in eight scenarios staged in a darkened parking lot. Participants, who were seated in the driver’s position of a stationary vehicle and instructed when to look, viewed the oncoming test vehicles in scenarios of 180m left turn, 180m right turn, 80m left turn, 80m right turn, left turn beside participant vehicle, crossing left in front of participant vehicle, right turn beside participant vehicle, and straightaway, in counterbalanced presentation orders. The swiveling headlamp vehicle provided statistically lower glare ratings in both 180m curves and the 80m right curve and statistically lower or similar in the intersection scenarios than the fixed headlamp vehicle.

Twent-two participants of varying ages detected roadside targets in two consecutive dynamic evaluations of a horizontally swiveling headlamp vehicle and a vehicle with the same headlamps that did not swivel. Participants detected targets as they drove unlighted low-speed public roads. Scenarios encountered were intersection turns, and curves with approximate radii of 70-90m, 120-140m, 170-190m, and 215-220m. Results from the first study found improved detection distances from the swiveling headlamps in left curves, but unexpectedly decreased detection distances in larger radius right hand curves. The swiveling algorithm was altered for the second study, and the headlamps used did not have the same beam pattern as in the first study. Results from the second study again found improved detection distances from the swiveling headlamps while in the larger radius right hand curves fixed and swivel were not statistically different.

General Motors has pursued research to develop systems intended to assist drivers in recognizing people or objects behind them when they are backing, and this paper summarizes results from this research. We are currently working with ultrasonic rear parking assist systems, rear radar backing warning systems, and rear camera systems, which are briefly described and their utility for assisting drivers in recognizing people or objects behind them discussed. Our research on driver performance with a prototype long range backing warning system found that audible and visual warning combinations may not be effective in warning distracted drivers about unexpected objects. Driver expectancy is thought to play a significant role in this result. However, further research found drivers were more likely to notice an unexpected obstacle behind their vehicle with a prototype rear view video camera system compared to ultrasonic rear parking assist and trials that had no system.

Although advanced driver assistance systems (ADAS) have been widely introduced in automotive industry to enhance driving safety and comfort, and to reduce drivers’ driving burden, they do not in general reflect different drivers’ driving styles or customized with individual personalities. This can be important to comfort and enjoyable driving experience, and to improved market acceptance. However, it is challenging to understand and further identify drivers’ driving styles due to large number and great variations of driving population. Previous research has mainly adopted physical approaches in modeling drivers’ driving behavior, which however are often very much limited, if not impossible, in capturing human drivers’ driving characteristics. This paper proposes a reinforcement learning based approach, in which the driving styles are formulated through drivers’ learning processes from interaction with surrounding environment.

With the rapid development of artificial intelligence, autonomous driving technology will finally reshape an automotive industry. Although fully autonomous cars are not commercially available to common consumers at this stage, partially autonomous vehicles, which are defined as level 2 and level 3 autonomous vehicles by SAE J3016 standard, are widely tested by automakers and researchers. A typical Human-Machine-Interface (HMI) for a vehicle takes a form to support a human domination role. Although modern driving assistance systems allow vehicles to take over control at certain scenarios, the typical human-machine-interface has not changed dramatically for a long time. With deep learning neural network technologies penetrating into automotive applications, multi-modal communications between a driver and a vehicle can be enabled by a cost-effective solution.

This paper studies the use of active rear steering (4-wheel steering) to change the transient lateral dynamics and body motion of passenger cars in the stable or linear region of the tires. Rear steering systems have been used for several decades to improve low speed turning maneuverability and high speed stability, and various control strategies have been previously published. With a model-based, feed-forward rear steer control strategy, the lateral transient can be influenced separately from the steady-state steering gain. This lateral transient is influenced by many vehicle parameters, but we will look at the influence of active rear steer and various tire types such as all-season, snow, and summer. This study will explore the ability for a rear steering system to change the lateral transient to a step steer input, compared to the effect of changing tire types.

Since steering wheel torque feedback is one of the crucial factors for drivers to gain road feel and ensure driving safety, it is especially important to simulate the steering torque feedback for a driving simulator. At present, steering wheel feedback torque is mainly simulated by an electric motor with gear transmission. The torque response is typically slow, which can result in drivers’ discomfort and poor driving maneuverability. This paper presents a novel torque feedback device with magnetorheological (MR) fluid and coil spring. A phase separation control method is also proposed to control its feedback torque, including spring and damping torques respectively. The spring torque is generated by coil spring, the angle of coil spring can be adjusted by controlling a brushless DC motor. The damping torque is generated by MR fluid, the damping coefficient of MR fluid can be adjusted by controlling the current of excitation coil.

This paper presents an experimental comparative study between the OCTOGONAL-Gate Silicon-on-Insulator (SOI) nMOSFET (OSM) and the conventional SOI nMOSFET (CSM) considering the same bias conditions and the same gate area (AG), in order to verify the influence of this new MOSFET layout style to handle high current for automotive modules. Analog integrated circuits (ICs) design tends to be considered an art due to a large number of variables and objectives to achieve the product specifications. The designer has to find the right tradeoffs to achieve the desired automotive specification such as low power, low voltage, high speed and high current driver. SOI MOSFET's technology is required to provide the growth of embedded electronics. This growth is driving demand for power-handling devices that are smaller yet still provide high current driver capabilities.

Due to growing interest in automated driving, the need for better understanding of human driving behavior in uncertain environment, such as driving behavior at un-signalized crossroad and roundabout, has further increased. Driving behavior at roundabout is greatly influenced by different dynamic factors such as speed, distance and circulating flow of the potentially conflicting vehicles, and drivers should choose whether to leave or wait at the upcoming exit according to these factors. In this paper, the influential dynamic factors and driving behavior characteristics at the roundabout is analyzed in detail, random forest method is then deployed to predict the driving behavior. For training the driving behavior model, four typical roundabout layouts were created under a real-time driving simulator with PanoSim-RT and dSPACE. Traffic participants with different motion style were also set in the simulation platform to mimic real driving conditions.

Interior permanent magnet machines are being widely used in hybrid vehicles owing to their compact size and high power density. Vehicle level application requires the motor to operate at high speed beyond the base speed of the motor. This is accomplished through flux weakening control. Nonfunctioning of inverter switches and/or gate driver circuit during flux weakening could give rise to a potential fault scenario called Un-Controlled Generation (UCG). This paper gives a detailed background of UCG and its impact on the high voltage and propulsion systems. In further sections the details related to modelling and analysis of UCG will be discussed. Finally, the paper will conclude with simulation results and comparison of the results with motor dynamometer test data.

During the early phase of vehicle development, one of the key design attributes to consider is visibility for the driver. Visibility is the ability to see the surrounding environment as one is driving. This need should drive the vehicle design enabling a move favorable view for the driver. Certain vehicle characteristics such as the size of windshield and the design of the pillar influence the perception of visibility for the driver. One specific characteristic influencing satisfaction is forward up vision, which is the subject of this paper. The objective of this project was to analyze the influence of forward up vision on driver satisfaction under real world driving conditions. Other influences such as the positon of the occupant in the seat was also studied. This study was supported by research, statistical data analysis and dynamic clinics.

With rapid increase of vehicles on the road, safety concerns have become increasingly prominent. Since the leading cause of many traffic accidents is known to be by human drivers, developing autonomous vehicles is considered to be an effective approach to solve the problems above. Although trajectory tracking plays one of the most important roles on autonomous driving, handling the coupling between trajectory-tracking control and ESP under certain driving scenarios remains to be challenging. This paper focuses on trajectory-tracking control considering the role of ESP. A vehicle model is developed with two degrees of freedom, including vehicle lateral, and yaw motions. Based on the proposed model, the vehicle trajectory is separated into both longitudinal and lateral motion. The coupling effect of the vehicle and ESP is analyzed in the paper. The lateral trajectory-tracking algorithm is developed based on the preview follower theory.

This paper presents real-world driving energy and fuel consumption results for the second-generation Chevrolet Volt plug-in hybrid electric vehicle (PHEV). A drive cycle, local to Michigan Technological University, was designed to mimic urban and highway driving test cycles in terms of distance, transients and average velocity, but with significant elevation changes to establish an energy intensive real-world driving cycle for assessing potential energy savings for connected and automated vehicle (CAV) control. The investigation began by establishing baseline and repeatability of energy consumption at various battery states of charge. It was determined that drive cycle energy consumption under a randomized set of boundary conditions varied within 3.6% of mean energy consumption regardless of initial battery state of charge.

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver.

Hybrid and electric vehicles utilize high power electric motors to propel the vehicle requiring a significant level of electric current to travel between various modules such as energy storage devices, power inverter modules, energy charging modules, and the motors themselves. This electric current creates magnetic fields around the devices themselves and wiring that delivers this current between devices within the vehicle. These devices and wiring exist throughout the vehicle and can even exist near vehicle occupants, which has prompted investigations looking into the short term biological effect these non-ionizing fields can have on the human body. The findings from these investigations have been published by organizations such as the International Commission on Non-Ionizing Radiation Protection (ICNIRP), and some nations have passed laws regulating the magnetic and electric field exposure to vehicle occupants.

This study evaluated the effectiveness of alternative workload-based interventions intended to restore driver alertness following drowsy episodes. Unlike traditional drowsy driving studies, this experiment did not target sleep-deprived individuals, but rather studied normally rested drivers under the assumption that low-workload environments could trigger drowsy driving episodes. The study served as a proof of concept for varying the nature and onset of countermeasure interventions intended to disrupt the drowsiness cycle. Interventions to combat drowsiness attempted to target driver workload, either physical or cognitive, and included two primary treatment conditions: 1) physical workload to increase driver steering demands and 2) trivia-based interactive games to mentally challenge drivers. A benchmark comparison condition using music was also investigated to contrast the relative influence of workload-based interventions with passive listening to musical arrangements.

Multiple automotive applications exist for small electric motors that are activated by vehicle occupants for various functions such as window lifts and seat adjusters. For such a motor to be described as high quality, not only should the sound it produces be low in amplitude, but it also needs to be free from pulsations and variations that might occur during its (otherwise) steady-state operation. If a motor’s sound contains pulsations or variations between 2 and 8 cycles per second, the variation is described as warble. To establish performance targets for warble noise at both the vehicle and component level a way to measure and quantify the warble noise must be established. Building on existing sound quality metrics such as loudness and pitch variation, a method is established by which processed sound data is put through a secondary operation of Fourier analysis.