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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.

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.

Because of rising demands to improve aerodynamic performance owing to its impact on vehicle dynamics, efforts were previously made to reduce aerodynamic lift and yawing moment based on steady-state measurements of aerodynamic forces. In recent years, increased research on dynamic aerodynamics has partially explained the impact of aerodynamic forces on vehicle dynamics. However, it is difficult to measure aerodynamic forces while a vehicle is in motion, and also analyzing the effect on vehicle dynamics requires measurement of vehicle behavior, amount of steering and other quantities noiselessly, as well as an explanation of the mutual influence with aerodynamic forces. Consequently, the related phenomena occurring in the real world are still not fully understood.

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.

Road Noise is generated by the change of random displacement input inside the tire contact patch. Since the existing 3 or 6 directional electromagnetic shakers have a flat surface at the tire contact patch, these shakers cannot excite the vehicle in a manner representative of actual on-road road noise input. Therefore, this paper proposes a new experimental method to measure the road noise vehicle transfer function. This method is based on the reciprocity between the tire contact patch and the driver's ear location. The reaction force sensor of the tire contact patch is newly developed for the reciprocal loud speaker excitation at the passenger ear location. In addition, with this equipment, it is possible to extract the dominant structural mode shapes creating high sound pressure in the automotive interior acoustic field. This method is referred to as experimental structure mode participation to the noise of the acoustic field in the vibro-acoustic coupling analysis.

This paper describes a control method to improve straight-line stability without sacrificing natural steering feel, utilizing a newly developed steering system controlling the steering force and the wheel angle independently. It cancels drifting by a road cant and suppresses the yaw angle induced by road surface irregularities or a side wind. Therefore drivers can keep the car straight with such a little steering input adjustment, thus reducing the driver's workload greatly. In this control method, a camera mounted behind the windshield recognizes the forward lane and calculate the discrepancy between the vehicle direction and the driving lane. This method has been applied to the test car, and the reduction of the driver's workload was confirmed. This paper presents an outline of the method and describes its advantages.

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.

Electric Vehicles are very quiet at low speeds therefore people (especially the visually impaired) have difficulty recognizing that these vehicles are approaching. To address this concern, Approaching Vehicle Sound for Pedestrians system development has been discussed worldwide. In Japan, USA, Europe and China, government regulation is currently under study. As a solution to meet this concern, Nissan has developed the VSP (Approaching Vehicle Sound for Pedestrians) system for implementation on Nissan's first mass production Electric Vehicle. Nissan VSP emits a futuristic sound to satisfy 3 key stakeholders' concerns; for pedestrians to provide detectability, for drivers and neighborhoods to maintain a quiet environment. The sound emitted during forward motion has a “twin peaks and one dip” frequency signature, with modulation (or rhythmic structure) to accommodate human-beings ear frequency sensitivity, hearing loss due to aging and ambient noise conditions.

At Nissan we have developed a new parallel hybrid system for rear-wheel-drive hybrid vehicles. As the main components of the hybrid system, both the motor and the inverter have been developed and are manufactured in house to attain high power density for providing responsive acceleration, a quiet EV drive mode and improved fuel economy. Because the motor is located between the engine and the transmission, it had to be shortened to be within the length allowed for the powertrain. Therefore, new technologies have been developed such as high-density, square-shaped windings and an optimized magnetic circuit specially designed for concentrated winding motors. The inverter is sized to a 12V battery, which it replaces in the engine compartment. Despite its compact size, the inverter must have rather large current capacity to drive a high-power motor. Heat management is critical to the design of a small but high-power inverter.

This paper deals with a blind spot assistance system that assists the driver by generating yaw-moment when a driver's lane change maneuver is detected and when there is an object present in the blind spot area of the adjacent lane. The system combines lane information and driver's maneuver information for estimating the driver's lane change. If the millimeter wave sensor detects an object in the rear blind spot area in the event of a lane change, direct yaw-moment in the opposite direction of the lane change maneuver is generated. The unique method of detecting driver's lane change, control method of assisting the driver to recover back in lane, and the system design to maintain driver compatibility is mentioned, together with the effectiveness of the system.

This paper describes the motor and inverter system developed for the Nissan LEAF that has been specifically designed as a mass-produced electric vehicle. The system produces maximum torque of 280 Nm and maximum power of 80 kW. The motor achieves a small size, high power, and high efficiency as a result of adopting the following in-house technologies. The magnetic circuit design was optimized for an interior magnet synchronous motor to attain the maximum performance figures noted here. The material technologies of the rotor and the stator facilitate high efficiency and the production technology achieves high density winding. The cooling mechanism is optimally designed for a mass-produced electric vehicle. The inverter incorporates the following original technologies and application-specific parts to obtain cost reductions combined with reliability improvements. The power module has an original structure with the power devices mounted directly on the busbars.

In making driver workload assessments, it is important to evaluate the driver's level of brain activity because the operation of a motor vehicle presumably involves higher-order brain functions. Driving on narrow roads in particular probably imposes a load on the driver's brain functions because of the need to be cognizant of the tight space and to pay close attention to the surroundings. Test vehicles were fitted with a functional near-infrared spectroscopy (fNIRS) system for measuring bloodstream concentrations at 32 locations in the frontal lobe of the participating drivers in order to evaluate their levels of mental activity while driving on narrow roads. The results revealed significant increases in cerebral blood flow corresponding to the perceived workload. This suggests that increases in cerebral blood flow can be used as an effective index for estimating mental workloads.

This paper describes the HMI, navigation and telematics systems developed specifically for the Nissan LEAF electric vehicle to dispel drivers' anxieties about operating an EV. Drivers of EVs will need to understand various new kinds of information about the vehicle's operational status that differ from conventional gasoline-engine vehicles. Additionally, owing to the current driving range of EVs and limited availability of charging stations, drivers will want to know acccurate the remaining driving range, amount of power and the latest information about charging station locations. It will also be important to ensure that people unfamiliar with EVs will be able to operate them easily as rental cars or in car-sharing systems without experiencing any inconvenience.

The probability or risk of traffic accidents must be estimated quantitatively in order to implement effective traffic safety measures. In this study, various statistical data and probability theory were used to examine a method for predicting the risk of crossing-collisions, representing a typical type of accident at intersections in Japan. Crossing-collisions are caused by a variety of factors, including the road geometry and traffic environment at intersections and the awareness and intentions of the drivers of the striking and struck vehicles. Bayes' theorem was applied to find the accident probability of each factor separately. Specifically, the probability of various factors being present at the time of a crossing-collision was estimated on the basis of traffic accident data and observation survey data.

Analyses were performed using field data for belted drivers of light vehicles in frontal crashes to examine the frequency and severity of frontal crashes with narrow objects. This study examined the distribution of injuries by body region, crash severity, and single- versus multiple-vehicle crashes for narrow object and all other crashes. Factors influencing injuries in different types of frontal crashes were identified, and risk of injury to belted drivers in narrow object crashes versus other frontal crashes was examined. A detailed review of about 400 NASS cases involving narrow object crashes was also performed. Results indicate frontal crashes involving impact with poles, posts, or trees are relatively infrequent. Overall, the fatal risk for belted drivers is lower in narrow object crashes than in other types of frontal crashes.

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.

Various driver assistance systems have been developed for maintaining a vehicle in its lane. For example, systems that apply force to the steering gear to help the driver keep the vehicle in the center of its lane are becoming popular. On the other hand, direct yaw-moment control systems have been developed for use in stabilizing the vehicle in extremer conditions where loss of control is detected. This paper deals with a system for aiding drivers in recovering from lane departure and maintaining the vehicle in its lane by utilizing direct yaw-moment control. The system uses a CCD camera to recognize lane makers and detects lane departures based on the positional relationship between the vehicle and the lane. When a lane departure is detected, the system applies force to the wheels to generate a yaw moment in the vehicle, so that it turns towards the original lane.

A research prototype driver support system to augment perception to enhance situation awareness and control for car following applications is introduced and results from a field test evaluation are reported. The support system applies driving and braking force control and a gas-pedal push-back force whose magnitudes depend on the degree to which an undesirable region in a perceptual control space of time headway (THW) and time to collision (TTC) is penetrated (i.e. criticality based on perceptually meaningful cues [4]).