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Particle image velocimetry measurements of the in-cylinder flow in an optically accessible single-cylinder research engine were taken to better understand the effects of intake pressure variations on the flow field. At a speed of 1500 rpm, the engine was run at six different intake pressure loads from 0.4 to 0.95 bar under motored operation. The average velocity fields show that the tumble center position is located closer to the piston and velocity magnitudes decrease with increasing pressure load. A closer investigation of the intake flow near the valves reveals sharp temporal gradients and differences in maximum and minimum velocity with varying intake pressure load which are attributed to intake pressure oscillations. Despite measures to eliminate acoustic oscillations in the intake system, high-frequency pressure oscillations are shown to be caused by the backflow of air from the exhaust to the intake pipe when the valves open, exciting acoustic modes in the fluid volume.

This paper presents a system designed to develop a high-resolution map of public roads by capturing high-resolution surface images. Unlike conventional system, the proposed system applies a field programmable gate array (FPGA) to synchronize camera, Inertial Measurement Unit (IMU), and Global Positioning System (GPS) by using FPGA’s high clock frequency and flexibility to multiple devices. The proposed system, which can be mounted on a regular vehicle, contains a Complementary Metal–Oxide–Semiconductor (CMOS) camera which can achieve 0.006 ms shutter speed and 150 fps frame rate. This camera’s high shutter speed and high frame rate can help capturing images with overlapping region at fast driving speed so that no area is missing from road surface image capturing.

Restrictions on track testing, combined with advances in technology, have contributed to an increased dependence on sensors and data acquisition for diagnosing problems and improving performance in motorsports vehicles. This dependence has created a new set of challenges for race engineers to collect quality data from a vehicle at the track. Successful 7- or 8-post shaker rig testing is highly dependent on the quality of the data acquired at the track. An improperly configured data acquisition system can actually be worse than a faulty sensor. This paper highlights a few of the most common problems in motorsports data acquisition: aliasing and sample rate selection. The effects of these problems are described for typical suspension sensors such as accelerometers, shock potentiometers, load cells, and laser ride height sensors. An experimental case study is presented to explain the implications of these problems.

This study outlines a protocol for image data collection acquired from human volunteers. The data set will serve as the foundation of a consolidated effort to develop the next generation full-body Finite Element Analysis (FEA) models for injury prediction and prevention. The geometry of these models will be based off the anatomy of four individuals meeting extensive prescreening requirements and representing the 5th and 50th percentile female, and the 50th and 95th percentile male. Target values for anthropometry are determined by literature sources. Because of the relative strengths of various modalities commonly in use today in the clinical and engineering worlds, a multi-modality approach is outlined. This approach involves the use of Computed Tomography (CT), upright and closed-bore Magnetic Resonance Imaging (MRI), and external anthropometric measurements.

The spatial distribution of internal exhaust gas recirculation (EGR) is evaluated in an optically accessible direct injection spark ignition engine using near infrared laser absorption to visualize the distribution of the H2O molecule. The obtained overall internal exhaust gas recirculation compares well to gas-exchange cycle calculations and the spatial distributions are consistent with those measured with inverse LIF. The experimental procedures described in this report are designed to be simple and rapidly implemented without the need to resort to unusual optical components. The necessary spectral data of the selected absorption line is obtained from the HITEMP database and is validated with prior experiments carried out in a reference cell. Laser speckle in the images is effectively reduced using a ballistic diffuser.

This paper presents a novel infrastructural traffic monitoring approach that estimates traffic information by combining two sensing techniques. The traffic information can be obtained from the presented approach includes passing vehicle counts, corresponding speed estimation and vehicle classification based on size. This approach uses measurement from an array of Lidars and video frames from a camera and derives traffic information using two techniques. The first technique detects passing vehicles by using Lidars to constantly measure the distance from laser transmitter to the target road surface. When a vehicle or other objects pass by, the measurement of the distance to road surface reduces in each targeting spot, and triggers detection event. The second technique utilizes video frames from camera and performs background subtraction algorithm in each selected Region of Interest (ROI), which also triggers detection when vehicle travels through each ROI.

Vehicle headlamps and roadway lighting are the major sources of illumination at night. These sources affect contrast - defined as the luminance difference of an object from its background - which drives visibility at night. However, the combined effect of vehicle headlamps and intersection lighting on object contrast has not been reported previously. In this study, the interactive effects of vehicle headlamps and overhead lighting on object contrast were explored based on earlier work that examined drivers’ visibility under three intersection lighting designs (illuminated approach, illuminated box, and illuminated approach + box). The goals of this study were to: 1) quantify object luminance and contrast as a function of a vehicle’s headlamps and its distance to an intersection using the three lighting designs; and, 2) to assess whether contrast influences visual performance and perceived visibility in a highly dynamic intersection environment.

Driver Assistance and Autonomous Driving features are becoming nearly ubiquitous in new vehicles. The intent of the Driver Assistant features is to assist the driver in making safer decisions. The intent of Autonomous Driving features is to execute vehicle maneuvers, without human intervention, in a safe manner. The overall goal of Driver Assistance and Autonomous Driving features is to reduce accidents, injuries, and deaths with a comforting driving experience. However, different drivers can react differently to advanced automated driving technology. It is therefore important to consider and improve the adaptability of these advances based on driver behavior. In this paper, a human-centric approach is adopted to provide an enriching driving experience. We perform data analysis of the naturalistic behavior of drivers when performing lane change maneuvers by extracting features from extensive Second Strategic Highway Research Program (SHRP2) data of over 5,400,000 data files.

In the evolving landscape of automated driving systems, the critical role of vehicle localization within the autonomous driving stack is increasingly evident. Traditional reliance on Global Navigation Satellite Systems (GNSS) proves to be inadequate, especially in urban areas where signal obstruction and multipath effects degrade accuracy. Addressing this challenge, this paper details the enhancement of a localization system for autonomous public transport vehicles, focusing on mitigating GNSS errors through the integration of a LiDAR sensor. The approach involves creating a 3D map using the factor graph-based LIO-SAM algorithm based on GNSS, vehicle odometry, IMU and LiDAR data. The algorithm is adapted to the use-case by adding a velocity factor and altitude data from a Digital Terrain model. Based on the map a state estimator is proposed, which combines high-frequency LiDAR odometry based on FAST-LIO with low-frequency absolute multiscale ICP-based LiDAR position estimation.