Browse Topic: Education and training
For mature virtual development, enlarging coverage of performances and driving conditions comparable with physical prototype is important. The subjective evaluation on various driving conditions to find abnormal or nonlinear phenomena as well as objective evaluation becomes indispensable even in virtual development stage. From the previous research, the road noise had been successfully predicted and replayed from the synthesis of system models. In this study, model based NVH simulator dedicated to virtual development have been implemented. At first, in addition to road noise, motor noise was predicted from experimental models such as blocked force and transfer function of motor, mount and body according to various vehicle conditions such as speed and torque. Next, to convert driver’s inputs such as acceleration and brake pedal, mode selection button and steering wheel to vehicle’s driving conditions, 1-D performance model was generated and calibrated. Finally, the audio and visual
There are some paradoxical keys to NVH engineering success that are not taught in engineering schools. This paper will describe these in detail and provide examples to add context. The first unexpected key is that a good generalist makes a better expert. The more you understand the complete product development process, and the better contacts you have throughout the product development organization, the easier it will be for you to find cost effective solutions to your specific issues. Next, you need to know your customers, and that means both internal and external customers. If you work for a supplier, it means knowing original equipment manufacturer (OEM) and end user customers. The more you understand the customers’ needs, the better you can address them and make your product stand out. Another key is to try to turn a crazy idea into something practical. Sometimes you might find a completely insane solution to your problem, such as making a major component out of solid gold. If you
A test and signal processing strategy was developed to allow a tire manufacturer to predict vehicle-level interior response based on component-level testing of a single tire. The approach leveraged time-domain Source-Path-Contribution (SPC) techniques to build an experimental model of an existing single tire tested on a dynamometer and substitute into a simulator vehicle to predict vehicle-level performance. The component-level single tire was characterized by its acoustic source strength and structural forces estimated by means of virtual point transformation and a matrix inversion approach. These source strengths and forces were then inserted into a simulator vehicle model to predict the acoustic signature, in time-domain, at the passenger’s ears. This approach was validated by comparing the vehicle-level prediction to vehicle-level measured response. The experimental model building procedure can then be adopted as a standard procedure to aid in vehicle development programs.
The implementation of active sound design models in vehicles requires precise tuning of synthetic sounds to harmonize with existing interior noise, driving conditions, and driver preferences. This tuning process is often time-consuming and intricate, especially facing various driving styles and preferences of target customers. Incorporating user feedback into the tuning process of Electric Vehicle Sound Enhancement (EVSE) offers a solution. A user-focused empirical test drive approach can be assessed, providing a comprehensive understanding of the EVSE characteristics and highlighting areas for improvement. Although effective, the process includes many manual tasks, such as transcribing driver comments, classifying feedback, and identifying clusters. By integrating driving simulator technology to the test drive assessment method and employing machine learning algorithms for evaluation, the EVSE workflow can be more seamlessly integrated. But do the simulated test drive results
In February, the Joint Interagency Field Experimentation (JIFX) team at the Naval Postgraduate School (NPS) executed another highly collaborative week of rapid prototyping and defense demonstrations with dozens of emerging technology companies. Conducted alongside NPS’ operationally experienced warfighter-students, the event is a win-win providing insight to accelerate potential dual-use applications.
Drone show accidents highlight the challenges of maintaining safety in what engineers call “multiagent systems” — systems of multiple coordinated, collaborative, and computer-programmed agents, such as robots, drones, and self-driving cars.
This paper presents a new regression model-based method for accurate predictions of stiffness of different glass laminate constructions with a point-load bending test setup. Numerical FEA models have been developed and validated with experimental data, then used to provide training data required for the statistical model. The multi-variable regression method considered six input variables of total glass thickness, thickness ratio of glass plies as well as high-order terms. Highly asymmetrical, hybrid laminates combining a relatively thick soda-lime glass (SLG) ply joined with a relatively thin Corning® Gorilla® Glass (GG) ply were analyzed and compared to standard symmetrical SLG-SLG constructions or a monolithic SLG with the same total glass thickness. Both stiffness of the asymmetrical laminates and the improvement percentage over the standard symmetrical design can be predicted through the model with high precision.
Reproducing driving scenarios involving near-collisions and collisions in a simulator can be useful in the development and testing of autonomous vehicles, as it provides a safe environment to explore detailed vehicular behavior during these critical events. CARLA, an open-source driving simulator, has been widely used for reproducing driving scenarios. CARLA allows for both manual control and traffic manager control (the module that controls vehicles in autopilot manner in the simulation). However, current versions of CARLA are limited to setting the start and destination points for vehicles that are controlled by traffic manager, and are unable to replay precise waypoint paths that are collected from real-world collision and near-collision scenarios, due to the fact that the collision-free pathfinding modules are built into the system. This paper presents an extension to CARLA’s source code, enabling the replay of exact vehicle trajectories, irrespective of safety implications
In the automotive industry, there have been many efforts of late in using Machine Learning tools to aid crash virtual simulations and further decrease product development time and cost. As the simulation world grapples with how best to incorporate ML techniques, two main challenges are evident. There is the risk of giving flawed recommendations to the design engineer if the training data has some suspect data. In addition, the complexity of porting simulation data back and forth to a Machine Learning software can make the process cumbersome for the average CAE engineer to set up and execute a ML project. We would like to put forth a ML workflow/platform that a typical CAE engineer can use to create training data, train a PINN (Physics Informed Neural Network) ML model and use it to predict, optimize and even synthesize for any given crash problem. The key enabler is the use of an industry first data structure named mwplot that can store diverse types of training data - scalars, vectors
The ISO TR 5469 Technical Report provides a framework to classify the AI/ML technology based on usage level and the properties and requirements to mitigate cyber and functional safety risks for the technology. This paper provides an overview of the approach used by ISO TR 5469 as well as an example of how one of the six ISO TR 5469 desirable properties (resilience to adversarial and intentional malicious input) can be analyzed for adversarial attacks. This paper will also show how a vehicle testbed can be used to provide a student with an AI model that can be used to simulate a non-targeted cyber security attack. The testbed can be used to simulate a poisoning attack where the student can manipulate a training data set to deceive the AI model during a simulated deployment.1 The University of Detroit Mercy (UDM) has developed Cyber-security Labs as a Service (CLaaS) to support teaching students how to understand and mitigate cyber security attacks. The UDM Vehicle Cyber Engineering (VCE
The research activity aims at defining specific Operational Design Domains (ODDs) representative of Italian traffic environments. The paper focuses on the human-machine interaction in Automated Driving (AD), with a focus on take-over scenarios. The study, part of the European/Italian project “Interaction of Humans with Level 4 AVs in an Italian Environment - HL4IT”, describes suitable methods to investigate the effect of the Take-Over Request (TOR) on the human driver’s psychophysiological response. The DriSMI dynamic driving simulator at Politecnico di Milano has been used to analyse three different take-over situations. Participants are required to regain control of the vehicle, after a take-over request, and to navigate through a urban, suburban and highway scenario. The psychophysiological characterization of the drivers, through psychological questionnaires and physiological measures, allows for analyzing human factors in automated vehicles interactions and for contributing to
Drivers present diverse landscapes with their distinct personalities, preferences, and driving habits influenced by many factors. Though drivers' behavior is highly variable, they can exhibit clear patterns that make sorting them into one category or another possible. Discrete segmentation provides an effective way to categorize and address the differences in driving style. The segmentation approach offers many benefits, including simplification, measurement, proven methodology, customization, and safety. Numerous studies have investigated driving style classification using real-world vehicle data. These studies employed various methods to identify and categorize distinct driving patterns, including naturalist differences in driving and field operational tests. This paper presents a novel hybrid approach for segmenting driver behavior based on their driving patterns. We leverage vehicle acceleration data to create granular driver segments by combining event and trip-based methodologies
For years, Proffesor Bozhi Tian’s lab has been learning how to integrate the world of electronics — rigid, metallic, bulky — with the world of the body — soft, flexible, delicate.
Monitoring the rotor temperature of drive machines is crucial for the safety and performance of electric vehicles. However, due to the complex operating conditions of electric vehicles, the thermal parameters of vehicular induction machines (IMs) vary significantly and are difficult to identify accurately. This article first establishes a concise but effective thermal network for IMs and analyzes the influencing factors of thermal parameters. Then, a parameter identification network (PIN) with multiple parallel branches is constructed to learn the mapping relationship between electromechanical variables and thermal parameters. Afterward, temperature datasets for network training are built through bench testing. Finally, the effectiveness of identified parameters for rotor temperature estimation application is verified, demonstrating improved interpretability, generalization ability, and accuracy compared to an end-to-end neural network.
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