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The manner in which a motor vehicle fire is initiated and subsequently spreads is dependent on a number of complex, interdependent, phenomena including combustion kinetics, heat transfer and fluid dynamics. Because the damage caused by a fire is coupled to these phenomena, damage patterns can sometimes be used to understand certain characteristics about the fire. In many cases, the goal is to determine the cause and origin of the fire.

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).

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.

Autonomous driving is a hot topic in the automotive domain, and there is an increasing need to prove its reliability. They use machine learning techniques, which are themselves stochastic techniques based on some kind of statistical inference. The occurrence of incorrect decisions is part of this approach and often not directly related to correctable errors. The quality of the systems is indicated by statistical key figures such as accuracy and precision. Numerous driving tests and simulations in simulators are extensively used to provide evidence. However, the basis of all descriptive statistics is a random selection from a probability space. The difficulty in testing or constructing the training and test data set is that this probability space is usually not well defined. To systematically address this shortcoming, ontologies have been and are being developed to capture the various concepts and properties of the operational design domain.

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.

In pursuing sustainable automotive technologies, exploring alternative fuels for hybrid vehicles is crucial in reducing environmental impact and aligning with global carbon emission reduction goals. This work compares methanol and naphtha as potential suitable alternative fuels for running in a battery-driven light-duty hybrid vehicle by comparing their performance with the diesel baseline engine. This work employs a 0-D vehicle simulation model within the GT-Power suite to replicate vehicle dynamics under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The vehicle choice enables the assessment of a delivery application scenario using distinct payload capacities: 0%, 25%, 50%, and 100%. The model is fed with engine maps derived from previous experimental work conducted in the same engine, in which a full calibration was obtained that ensures the engine's operability in a wide region of rotational speed and loads.

Computer modelling, virtual prototyping and simulation is widely used in the automotive industry to optimize the development process. While the use of CAE is widespread, on its own it lacks the ability to provide observable acoustics or tactile vibrations for decision makers to assess, and hence optimize the customer experience. Subjective assessment using Driver-in-Loop simulators to experience data has been shown to improve the quality of vehicles and reduce development time and uncertainty. Efficient development processes require a seamless interface from detailed CAE simulation to subjective evaluations suitable for high level decision makers. In the context of perceived vehicle vibration, the need for a bridge between complex CAE data and realistic subjective evaluation of tactile response is most compelling. A suite of VI-grade noise and vibration simulators have been developed to meet this challenge.

Abstract : In any human space flight program, safety of the crew is of utmost priority. In case of exigency during atmospheric flight, the crew is safely and quickly rescued from the launch vehicle using Crew escape system. Crew escape system is a crucial part of the Human Space flight vehicle which carries the crew module away from the ascending launch vehicle by firing its rocket motors (Pitch Motor (PM), Low altitude Escape Motor (LEM) and High altitude Escape Motor (HEM)). The structural loads experienced by the crew escape system during the mission abort are severe as the propulsive forces, aerodynamic forces and inertial forces on the vehicle are significantly high. Since the mission abort can occur at anytime during the ascent phase of the launch vehicle, trajectory profiles are generated for abort at every one second interval of ascent flight time considering several combinations of dispersions on various propulsive parameters of abort motors and aero parameters.

A novel method for Single Event Effect (SEE) Radiation Testing using Built-In Self-Test (BIST) feature of indigenously developed Vikram1601 processor is discussed. The novelty is that the usage of BIST avoids need of exhaustive test vectors to ensure test coverage of all the internal registers and physical memory to store them. So processor is the only element vulnerable to radiation damage during testing. The test design was carried out at VSSC, Trivandrum and the testing was carried out at IUAC, Delhi. In the first part, a brief introduction, need and methods of radiation testing of electronics especially SEE of radiation on Silicon based devices, different radiation effects, radiation damage mechanisms and testing methods are described. A brief introduction to Vikram1601 processor, the instruction – TST, used as BIST and testing scheme implementation using TST for studying the SEE is explained.

Uncrewed Aerial vehicles are useful for a multitude of applications in today’s age, covering a wide variety of fields such as defense, environmental science, meteorology, emergency responders, search and rescue operations, entertainment robotics, etc. Different types of aircrafts such as fixed wing UAVs, rotor wing UAVs are used for the mentioned applications depending upon the application requirements. One such category of UAVs is the lighter-than-air aircrafts, that provide their own set of advantages over the other types of UAVs. Blimps are among the participants of the lighter-than-air category that are expected to offer advantages such as higher endurance and range, and safer and more comfortable Human-machine-Interaction, etc. as compared to fixed wing and rotor wing UAVs due to their design. A ROS (Robot Operating System) based control system was developed for controlling the blimp.

The evaluation of aircraft characteristics through flight test maneuvers is fundamental to aviation safety and understanding flight attributes. This research project proposes a comprehensive methodology to detect and analyze aircraft maneuvers using full flight data, combining signal processing and machine learning techniques. Leveraging the Wavelet Transform, we unveil intricate temporal details within flight data, uncovering critical time-frequency insights essential for aviation safety. The integration of Long Short-Term Memory (LSTM) models enhances our ability to capture temporal dependencies, surpassing the capabilities of machine learning in isolation. These extracted maneuvers not only aid in safety but also find practical applications in system identification, air-data calibration, and performance analysis, significantly reducing pre-processing time for analysts.

Launch vehicle structures in course of its flight will be subjected to dynamic forces over a range of frequencies up to 2000 Hz. These loads can be steady, transient or random in nature. The dynamic excitations like aerodynamic gust, motor oscillations and transients, sudden application of control force are capable of exciting the low frequency structural modes and cause significant responses at the interface of launch vehicle and satellite. The satellite interface responses to these low frequency excitations are estimated through Coupled Load Analysis (CLA). The analysis plays a crucial role in mission as the satellite design loads and Sine vibration test levels are defined based on this. The perquisite of CLA is to predict the responses with considerable accuracy so that the design loads are not exceeded in the flight. CLA validation is possible by simulating the flight experienced responses through the analysis.

Industries have been increasingly adopting AI based computer vision models for automated asset defect inspection. A challenging aspect within this domain is the inspection of composite assets consisting of multiple components, each of which is an object of interest for inspection, with its own structural variations, defect types and signatures. Training vision models for such an inspection process involves numerous challenges around data acquisition such as insufficient volume, inconsistent positioning, poor quality and imbalance owing to inadequate image samples of infrequently occurring defects. Approaches to augmenting the dataset through Standard Data Augmentation (SDA) methods (image transformations such as flipping, rotation, contrast adjustment, etc.) have had limited success. When dealing with images of such composite assets, it is challenging to correct the data imbalance at the component level using image transformations as they apply to all the components within an image.

Faults if not detected and processed will create catastrophe in closed loop system for safety critical applications in automotive, space, medical, nuclear, and aerospace domains. In aerospace applications such as stall warning and protection/prevention system (SWPS), algorithms detect stall condition and provide protection by deploying the elevator stick pusher. Failure to detect and prevent stall leads to loss of lives and aircraft. Traditional Functional Hazard and Fault Tree analyses are inadequate to capture all failures due to the complex hardware-software interactions for stall warning and protection system. Hence, an improved methodology for failure detection and identification is proposed. This paper discusses a hybrid formal method and model-based technique using STPA to identify and diagnose faults and provide monitors to process the identified faults to ensure robust design of the indigenous stall warning and protection system (SWPS).