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Internal audits are a requirement of the AS9100, AS 13100 and RM 13005 and are intended to verify the compliance and effectiveness of an organization's quality management system. The methods and techniques for performing internal audits have significantly changed in the aviation, space and defense industries, and internal auditors must be knowledgeable of these requirements and the expectations as identified in the standard.

Many technical projects, most vehicle and component testing, and all accident reconstructions, product failure analyses, and other forensic investigations, require photographic documentation. Roadway evidence disappears, tested or wrecked vehicles are repaired, disassembled, or scrapped, and components can be tested for failure. Photographs are frequently the only evidence that remains of a wreck, or the only records of subjects before or during tests. Making consistently good images during any inspection is a critical part of the evaluation process. 

Crash reconstruction is a scientific process that utilizes principles of physics and empirical data to analyze the physical, electronic, video, audio, and testimonial evidence from a crash to determine how and why the crash occurred. This course will introduce this reconstruction process as it gets applied to various crash types - in-line and intersection collisions, pedestrian collisions, motorcycle crashes, rollover crashes, and heavy truck crashes. Methods of evidence documentation will be covered. Analysis methods will also be presented for electronic data from event data recorders and for video.

Photographs and video recordings of vehicle crashes and accident sites are more prevalent than ever, with dash mounted cameras, surveillance footage, and personal cell phones now ubiquitous. The information contained in these pictures and videos provide critical information to understanding how crashes occurred, and  analyze physical evidence. This course teaches the theory and techniques for getting the most out of digital media, including correctly processing raw video and photographs, correcting for lens distortion, and using photogrammetric techniques to convert the information in digital media to usable scaled three-dimensional data.

Convolutional neural networks are the de facto method of processing camera, radar, and lidar data for use in perception in ADAS and L4 vehicles, yet their operation is a black box to many engineers. Unlike traditional rules-based approaches to coding intelligent systems, networks are trained and the internal structure created during the training process is too complex to be understood by humans, yet in operation networks are able to classify objects of interest at error rates better than rates achieved by humans viewing the same input data.

This 4-week virtual-only experience, conducted by leading experts in the autonomous vehicle industry and academia, provides an in-depth look at the most common sensor types used in autonomous vehicle applications. By reviewing the theory, working through examples, viewing sensor data, and programming movement of a turtlebot, you will develop a solid, hands-on understanding of the common sensors and data provided by each. This course consists of asynchronous videos you will work through at your own pace throughout each week, followed by a live-online synchronous experience each Friday. The videos are led by Dr.

Due to its physical and chemical properties, hydrogen is an attractive fuel for internal combustion engines, providing grounds for studies on hydrogen engines. It is common practice to use a mathematical model for basic engine design and an essential part of this is the simulation of the combustion cycle, which is the subject of the work presented here. One of the most widely used models for describing combustion in gasoline and diesel engines is the Wiebe model. However, for cases of hydrogen combustion in DI engines, which are characterized by mixture stratification and in some cases significant incomplete combustion, practically no data can be found in the literature on the application of the Wiebe model. Based on Wiebe's formulas, a mathematical model of hydrogen combustion has been developed. The model allows making computations for both DI and PFI hydrogen engines. The parameters of the Wiebe model were assessed for three different engines in a total of 26 operating modes.

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.

Autonomous Driving is being utilized in various settings, including indoor areas such as industrial halls. Additionally, LIDAR sensors are currently popular due to their superior spatial resolution and accuracy compared to RADAR, as well as their robustness to varying lighting conditions compared to cameras. They enable precise and real-time perception of the surrounding environment. Several datasets for on-road scenarios such as KITTI or Waymo are publicly available. However, there is a notable lack of open-source datasets specifically designed for industrial hall scenarios, particularly for 3D LIDAR data. Furthermore, for industrial areas where vehicle platforms with omnidirectional drive are often used, 360° FOV LIDAR sensors are necessary to monitor all critical objects. Although high-resolution sensors would be optimal, mechanical LIDAR sensors with 360° FOV exhibit a significant price increase with increasing resolution.

The Single Cylinder Research Engine (SCRE) at the Institute of Internal Combustion Engines and Powertrain Systems is equipped with a variable valve train that allows to switch between regular intake valve lift and early intake valve closing (Miller). On the exhaust side, a secondary valve lift on each valve is possible with adjustable back pressure and thus the possibility of realising internal EGR. In combination with alternative fuels, even if they are Drop-In capable as HVO, properties differ and can influence the emission and efficiency behaviour. The investigations of this paper are focusing on regenerative Drop-In fuel (HVO), fossil fuel (B7), and an oxygenate (OME), that needs adaptions at the engine control unit, but offers further emission potential. By commissioning a 2-stage boost system, it is possible to fully equalize the air mass in Miller mode compared to the normal valve lift.

In pursuit of safety validation of automated driving functions, efforts are being made to accompany real world test drives by test drives in virtual environments. To be able to transfer highly automated driving functions into a simulation, models of the vehicle’s perception sensors such as lidar, radar and camera are required. In addition to the classic pulsed time-of-flight (ToF) lidars, the growing availability of commercial frequency modulated continuous wave (FMCW) lidars sparks interest in the field of environment perception. This is due to advanced capabilities such as directly measuring the target’s relative radial velocity based on the Doppler effect. In this work, an FMCW lidar sensor simulation model is introduced, which is divided into the components of signal propagation and signal processing. The signal propagation is modeled by a ray tracing approach simulating the interaction of light waves with the environment.

Engine and aftertreatment solutions have been identified to meet the upcoming ultra-low NOX regulations on heavy duty vehicles in the United States and Europe. These standards will require changes to current conventional aftertreatment systems for dealing with low exhaust temperature scenarios while increasing the useful life of the engine and aftertreatment system. Previous studies have shown feasibility of meeting the US EPA and California Air Resource Board (CARB) requirements. This work includes a 15L diesel engine equipped with cylinder deactivation (CDA) and an aftertreatment system that was fully DAAAC aged to 800,000 miles. The aftertreatment system includes an e-heater (electric heater), light-off Selective Catalytic Reduction (LO-SCR) followed by a primary aftertreatment system containing a DPF and SCR.