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EDR's were first installed in 1994 and are now installed in 99% of new light vehicles sold in the US. In the US EDR’s are not required, but vehicles with EDR’s made after 9/1/2012 must meet minimum standardized content requirements of 49 CFR, Part 563 including speed, throttle, brake on/off and Delta V.  Data must be retrievable with a publicly available tool.  Only a few manufacturers install EDR’s worldwide currently, but the EU and China are adopting regulations to require them in the next few years.  

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.

Safety continues to be one of the most important factors in motor vehicle design, manufacturing, and marketing.  This course provides a comprehensive overview of these critical automotive safety considerations: injury and anatomy; human tolerance and biomechanics; occupant protection; testing; and federal legislation. The knowledge shared at this course enables participants to be more aware of safety considerations and to better understand and interact with safety experts. This course has been approved by the Accreditation Commission for Traffic Accident Reconstruction (ACTAR) for 18 Continuing Education Units (CEUs).

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.

Design and development of a modern steering system influences vehicle response to steering wheel input, driver effort, comfort, safety and fuel economy. In this interactive course participants will analyze the steering system from the road wheel to the steering wheel. Day one will begin with a deep dive into the anatomy and architecture of the lower steering system (wheel end, suspension geometry, linkages and steering gear), its effect on vehicle response and how forces and moments at the contact patch are converted to a torque at the pinion.

Driving simulators allow the testing of driving functions, vehicle models and acceptance assessment at an early stage. For a real driving experience, it's necessary that all immersions are depicted as realistically as possible. When driving manually, the perceived haptic steering wheel torque plays a key role in conveying a realistic steering feel. To ensure this, complex multi-body systems are used with numerous of parameters that are difficult to identify. Therefore, this study shows a method how to generate a realistic steering feel with a nonlinear open-loop model which only contains significant parameters, particularly the friction of the steering gear. This is suitable for the steering feel in the most driving on-center area. Measurements from test benches and real test drives with an Electric Power Steering (EPS) were used for the Identification and Validation of the model.

The optimization and further development of automated driving functions offers great potential to relieve the driver in various driving situations and increase road safety. Simulative testing in particular is an indispensable tool in this process, allowing conclusions to be drawn about the design of automated driving functions at a very early stage of development. In this context, the use of driving simulators provides support so that the driving functions of tomorrow can be experienced in a very safe and reproducible environment. The focus of the acceptance and optimization of automated driving functions is particularly on vehicle lateral control functions. As part of this paper, a test person study was carried out regarding manual vehicle lateral control on the dynamic vehicle road simulator at the Institute of Automotive Engineering.

Investigating human driver behavior enhances the acceptance of the autonomous driving and increases road safety in heterogeneous environments with human-operated and autonomous vehicles. The previously established driver fingerprint model, focuses on the classification of driving style based on CAN bus signals. However, driving styles are inherently complex and influenced by multiple factors, including changing driving environments and driver states. To comprehensively create a driver profile, an in-car measurement system based on the Driver-Driven vehicle-Driving environment (3D) framework is developed. The measurement system records emotional and physiological signals from the driver, including ECG signal and heart rate. A Raspberry Pi camera is utilized on the dashboard to capture the driver's facial expressions and a trained convolutional neural network (CNN) recognizes emotion. To conduct unobtrusive ECG measurements, an ECG sensor is integrated into the steering wheel.

As part of the safety validation of advanced driver assistance systems (ADAS) and automated driving (AD) functions, it is necessary to demonstrate that the frequency at which the system exhibits hazardous behavior (HB) in the field is below an acceptable threshold. This is typically tested by observation of the system behavior in a field operational test (FOT). For situations in which the system under test (SUT) actively intervenes in the dynamic driving behavior of the vehicle, it is assessed whether the SUT exhibits HB. Since the accepted threshold values are generally small, the amount of data required for this strategy is usually very large. This publication proposes an approach to reduce the amount of data required for the evaluation of emergency intervention systems with a state machine based intervention logic by including the time periods between intervention events in the validation process.

The automotive industry is continuously evolving, demanding innovative approaches to enhance testing methodologies and preventive identify potential issues. This paper proposes an advancement test approach in the area of the overall vehicle system included steering system and power train on a “Road to Rig” test bench. The research aims to revolutionize the conventional testing process by identifying faults at an early stage and eliminating the need to rely solely on field tests. The motivation behind this research is to optimize the test bench setup and bring it even closer to real field tests. Key highlights of the publication include the introduction of an expanded load spectrum, incorporating both steering angle and speed parameters along the test track. The load includes different route and driving profiles like on a freeway, overland and city drive in combination with the steering angles.

In the course of the U-Shift project, an automated, driverless and electrically driven vehicle concept is developed. By separating the vehicle into a drive module and a transport capsule, a novel form of mobility is created. The autonomous driving module, the so-called Driveboard, is able to change the transport capsules independently and thus serves both passenger and goods transport. In order to be able to use the vehicle effectively, especially in urban areas, the space required for manoeuvring and loading or unloading the capsules must be kept as small as possible. This poses special challenges for the steering system. In this paper, a novel steering system is presented that enables both same-direction and opposite-direction wheel steering. First, the fundamental concept of the steering system is presented. After that, the design is explained and the assembled steering system is shown. During normal cornering, there is a mechanical coupling between the wheels.

This title includes the technical papers developed for the 2023 Stapp Car Crash Conference, the premier forum for the presentation of research in impact biomechanics, human injury tolerance, and related fields, advancing the knowledge of land-vehicle crash injury protection. The conference provides an opportunity to participate in open discussion about the causes and mechanisms of injury, experimental methods and tools for use in impact biomechanics research, and the development of new concepts for reducing injuries and fatalities in automobile crashes.

This course introduces basic tire mechanics, including tire construction components based on application type, required sidewall stamping in accordance with DoT/ECE regulations, tread patterns, regulatory and research testing on quality, tire inspections and basic tire failure identification. The course will provide you with information that you can use immediately on-the-job and apply to your own vehicle. This course is practical in nature and supplemented with samples and hands-on activities.

This research aims presents the method classifying the noise source and evaluating the sound quality of the noise caused by operating of electric power steering wheel in an electric vehicle. The steering wheel has been operated by the motor drive by electric power and it called motor-driven electric power (MDPS) system. If the motor is attached to the steering column of the steering device, it is called C-MDPS system. The steering device of the C-MDPS system comprises of motor, bearings, steering column, steering wheel and worm shaft. Among these components the motor and bearings are main noise sources of C-MDPS system. When the steering wheel is operated in an electric vehicle, the operating noise of the steering device inside the vehicle is more annoying than that in a gasoline engine vehicle since the operating noise is not masked by engine noise. Defects in the C-MDPS system worsen the operating noise of the steering system.

Nowadays, electrification is largely acknowledged as a crucial strategy to mitigate climate change, especially for the transportation sector through the transition from conventional vehicles to electric vehicles (EVs). As the demand for EVs continues to rise, the development of a robust and widespread charging infrastructure has become a top priority for governments and decision-makers. In this context, innovative approaches to energy management and sustainability, such as Vehicle-to-Grid (V2G), are gradually being employed, leading to new challenges, like grid service integration, charge scheduling and public acceptance. For instance, the planned use scenario, the user’s behaviour, and the reachability of the geographical position influence the optimal energy management strategies both maintain user satisfaction and optimize grid impact.

The NVH performance of electric vehicles is a key indicator of vehicle quality, being the structure-borne transmission predominating at low frequencies. Many issues are typically generated by high vibrations, transmitted through different paths, and then radiated acoustically into the cabin. A combined analysis, with both finite-element and multi-body models, enables to predict the interior vehicle noise and vibration earlier in the development phases, to reduce the development time and moreover to optimize components with an increased efficiency level. In the present work, a simulation of a Hyundai electric vehicle has been performed in IDIADA VPG with a full vehicle multibody (MBD) model, followed by vibration/acoustic simulations with a Finite elements model (FEM) in MSC. Nastran to analyze the comfort. Firstly, a full vehicle MBD model has been developed in MSC. ADAMS/Car including representative flexible bodies (generated from FEM part models).