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Driving comfort and automotive product quality are strongly associated with the vibration that is transmitted to the occupants of a vehicle at the points of contact to the human body, including the seat, steering wheel, and pedals. Of these three contact locations, the seats have the most general importance, as all occupants of a vehicle experience seat vibration. Particularly relevant to driving comfort is the way in which vehicle occupants perceive seat vibration, which may be different than expected considering sensor measured vibration levels. Much of the interest in seat vibration has been focused on internal combustion engine powertrain vibration, especially idle vibration. However, electrification of vehicles changes the focus from low frequency idle vibration to higher frequency vibration sources.

Environmental perception is considered an essential module for autonomous driving and Advanced Driver Assistance System (ADAS). Recently, deep Convolutional Neural Networks (CNNs) have become the State-of-the-Art with many different architectures in various object detection problems. However, performances of existing CNNs have been dropping when detecting small objects at a large distance. To deploy any environmental perception system in real world applications, it is important that the system achieves high accuracy regardless of the size of the object, distance, and weather conditions. In this paper, a robust sensor fused object detection system is proposed by utilizing the advantages of both vision and automotive radar sensors. The proposed system consists of three major components: 1) the Coordinate Conversion module, 2) Multi level-Sensor Fusion Detection (MSFD) system, and 3) Temporal Correlation filtering module.

The fatal automobile accidents can be attributed to fatigued and distracted driving by drivers. Driver Monitoring Systems alert the distracted drivers by raising alarms. Most of the image based driver drowsiness detection systems face the challenge of failure proof performance in real time applications. Failure in face detection and other important part (eyes, nose and mouth) detections in real time cause the system to skip detections of blinking and yawning in few frames. In this paper, a real time robust and failure proof driver drowsiness detection system is proposed. The proposed system deploys a set of detection systems to detect face, blinking and yawning sequentially. A robust Multi-Task Convolutional Neural Network (MTCNN) with the capability of face alignment is used for face detection. This system attained 97% recall in the real time driving dataset collected. The detected face is passed on to ensemble of regression trees to detect the 68 facial landmarks.

The objective of this study was to determine risk of injury to the driver during a frontal impact in a Formula SAE vehicle. Formula SAE is a collegiate student design competition where every year universities worldwide build and compete with open-wheel formula-style race cars. Formula SAE 2006 rules stipulate the use of an impact attenuator to absorb energy in the event of a frontal impact. These rules mandated an average deceleration not to exceed 20-g from a speed of 7.0 m/s (23 ft/s), but do not specify a specific time or pulse shape of the deceleration. The pulse shapes tested in this study included an early high-g, constant-g, and late high-g pulse. The tests were performed using the deceleration sled at the Kettering University Crash Safety Center. Using industry standard practices, this study examined the driver's risk of injury with regard to neck and femur loads, head and chest accelerations, as well as kinematic analysis using high speed video.

FMVSS 223 and 224 set standards for “Rear Impact Protection” for trailers and semi-trailers with a gross weight rating greater than 10000 pounds. A limited amount of experimental data is available for evaluating the different performance attributes of rear impact guards. The crash tests are usually limited to fixed parameters such as impact speed, guard height, strength and energy absorption, etc. There also seems to be some misunderstanding of the interdependence of guard strength and energy absorption, and their combined effect on the guard's ability to limit underride while keeping occupant acceleration forces in a safe range. In this paper, we validated the Finite Element (FE) model of an existing rear impact guard against actual FMVSS 223 tests. We also modified a previously evaluated FE model of a 1990 Ford Taurus by updating its hood geometry and material properties.

Rollover crashes are responsible for a significant proportion of traffic fatalities each year, while they represent a relatively small proportion of all motor vehicle collisions. The purpose of this study was to focus on rollover events from an occupant's perspective to understand what type of industry test method, ATD, computer based model, and injury assessment measures are required to provide occupant protection during rollovers. Specific injuries most commonly experienced in rollovers along with the associated injury sources were obtained by review of 1998-2000 NASS-CDS records. These data suggest that models capable of predicting the likelihood of brain injuries, specifically subarachnoid and subdural hemorrhage, are desirable. Ideally, the model should also be capable of predicting the likelihood of rib fractures, lung contusions and shoulder (clavicular and scapular) fractures, and facet, pedicle, and vertebral body fractures in the cervical spine.

A variety of test methodologies currently exist to assess the propensity of a vehicle to roll laterally, the vehicle performance during a rollover event, and the associated risk of injury to the occupant. There are indications as to which tests are appropriate when attempting to replicate rollover events observed in the field. Due to the complexity of a rollover, test repeatability is a concern as well as cost, and field relevance. Since revisions to governmental rollover regulations are currently being considered, an assessment of currently available rollover test methodologies would provide a context to compare the different experimental designs. Additionally, the design of injury prevention strategies such as side air curtains, 4-point belts, etc. will also require the establishment of repeatable, robust, and economical test methods.

Doors inside an automotive HVAC module are essential components to ensure occupant comfort by controlling the cabin temperature and directing the air flow. For temperature control, the function of a door is not only to close/block the airflow path via the door seal that presses against HVAC wall, but also control the amount of hot and cold airflow to maintain cabin temperature. To meet the stringent OEM sealing requirement while maintaining a cost-effective product, a “V-Shape” soft rubber seal is commonly used. However, in certain conditions when the door is in the position other than closed which creates a small gap, this “V-Shape” seal is susceptible to the generation of objectionable whistle noise for the vehicle passengers. This nuisance can easily reduce end-customer satisfaction to the overall HVAC performance.

Cylinder air/fuel ratio distribution is an important factor affecting the economy, power, vibration, and emissions of an internal combustion engine. Currently, production automobiles utilize an exhaust gas sensor located in the main exhaust stream in order to regulate air/fuel mixtures. By measuring the oxygen content of the exhaust gas for each cylinder independently, the degree of air/fuel variation between cylinders can be determined. This information can be used to determine the mixture quality of specific cylinders. Knowing these variances can lead to design changes in the intake and exhaust manifolds as well as better control of fuel metering which will improve the output of the engine. This study was carried out using a 1991 3.8L Buick V-6 engine with customized exhaust manifolds utilizing exhaust gas oxygen sensors for each cylinder in addition to the sensor located in the main combined exhaust gas stream. Production level, ZrO2 sensors were used for this experimental study.

The safety of ground vehicles is a matter of critical importance. Vehicle safety is enhanced with the use of control systems that mitigate the effect of unachievable demands from the driver, especially demands for tire forces that cannot be developed. This paper presents the results of a smart tire prototyping and validation study, which is an investigation of a smart tire system that can be used as part of these mitigation efforts. The smart tire can monitor itself using in-tire sensors and provide information regarding its own tire forces and moments, which can be transmitted to a vehicle control system for improved safety. The smart tire is designed to estimate the three orthogonal tire forces and the tire aligning moment at least once per wheel revolution during all modes of vehicle operation, with high accuracy. The prototype includes two in-tire piezoelectric deformation sensors and a rotary encoder.

To improve the safety of automated driving, the paramount target of this intelligent system is to detect and segment the obstacle such as car and pedestrian, precisely. Object detection in self-driving vehicle has chiefly accomplished by making decision and detecting objects through each frame of video. However, there are diverse group of methods in both machine learning and machine vision to improve the performance of system. It is significant to factor in the function of the time in the detection phase. In other word, considering the inputs of system, which have been emitted from eclectic type of sensors such as camera, radar, and LIDAR, as time-varying signals, can be helpful to engross ‘time’ as a fundamental feature in modeling for forecasting the object, while car is moving on the way. In this paper, we focus on eliciting a model through the time to increase the accuracy of object detection in self-driving vehicles.

Automakers looking to remake their traditional vehicle line-up into autonomous vehicles, Noise, Vibration, and Harshness (NVH) considerations for autonomous vehicles are soon to follow. While traditional NVH considerations still must be applied to carry-over systems, additional components are required for an autonomous vehicle to operate. These additional components needed for autonomy also require NVH analysis and optimization. Autonomous vehicles rely on a suite of sensors, including Light Detection and Ranging (LiDAR) and cameras placed at optimal points on the vehicle for maximum coverage and utilization. In this study, the NVH considerations of autonomous vehicles are examined, focusing on the additional perception equipment installed in autonomous vehicles.

Government accident statistics show that approximately 35% of all car accident victims suffer an injury to the head and face. Such injuries are common during frontal, side, and rollover accidents as the head may impact the steering wheel, side pillars, windshield, or roof. Further, non-threatening injuries (i.e abrasions) may be suffered due to contact with the deployed airbag, or, in the case of an out-of-position occupant, a deploying airbag. While the forces and accelerations measured internal to the head are known to correlate with serious head injury (i.e. concussion, skull fracture, diffuse axonal injury), it is currently not possible to record how the loads are distributed over the head and face with the current ATD. Ultimately, such data could eventually be used to provide improved resolution as to the probability of superficial, soft tissue damage since past cadaver studies show that the distribution of contact pressures are related to such injuries.

A hardware and software architecture suitable for a safety-critical steer-by-wire systems is presented. The architecture supports three major failure modes and features several safety protocols and mechanisms. Failures due to component failures, software errors, and human errors are handled by the architecture and safety protocols. A test implementation using replicated communication channels, controllers, sensors, and actuators has been performed. The test implementation uses the CAN protocol, Motorola S12 microcontrollers, and Microchip MCP250XX components with a steering wheel and road wheel simulator. The focus of the paper is on the application level, using system engineering principles which incorporate a holistic approach to achieve safety at various levels.

Objection detection using a camera sensor is essential for developing Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) vehicles. Due to the recent advancement in deep Convolution Neural Networks (CNNs), object detection based on CNNs has achieved state-of-the-art performance during daytime. However, using an RGB camera alone in object detection under poor lighting conditions, such as sun flare, snow, and foggy nights, causes the system's performance to drop and increases the likelihood of a crash. In addition, the object detection system based on an RGB camera performs poorly during nighttime because the camera sensors are susceptible to lighting conditions. This paper explores different pedestrian detection systems at low-lighting conditions and proposes a sensor-fused pedestrian detection system under low-lighting conditions, including nighttime. The proposed system fuses RGB and infrared (IR) thermal camera information.