Browse Topic: People and personalities
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
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.
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
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
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
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
Model-based developers are turning to DevOps principles and toolchains to increase engineering efficiency, improve model quality and to facilitate collaboration between large teams. Mature DevOps processes achieve these through automation. This paper demonstrates how integrating modern version control (Git) with collaborative development practices and automated quality enforcement can streamline workflows for large teams using Simulink. The focus is on enhancing model consistency, enabling team collaboration, and development speed.
Nikola announced on February 19 that it had filed for Chapter 11 bankruptcy and had begun pursuing “value-maximizing sale transactions” for its operations. Also a maker of battery-electric heavy-duty trucks, the company began back in 2015 with an emphasis on hydrogen fuel cell technology for long-haul transport and began serial production of the Tre FCEV in 2023. The company also aspired to establish an extensive hydrogen fueling network through its HYLA brand. In its filing, Nikola stated that it intended to continue certain service and support operations for trucks currently in the field, including certain HYLA fueling operations, through the end of March 2025. The company would need one or more partners to support such activities beyond that point.
It is a fool's errand to make timely comments - in print! - about our current political turmoil. Even so, it feels important to place a marker in the sand to note the ongoing political reign of tariff threats, the upheaval potential of a demolished regulatory state affecting road and vehicle safety, and the damage that cuts to electric vehicle support might do to American automakers attempting to keep technological pace with their global automaker peers. It's a lot. The mainstream press is reporting the broad strokes of the industry's reaction to the new president. Ford CEO Jim Farley said Trump's erratic threats and changes are adding “a lot of cost and a lot of chaos” to the automotive industry and that a 25% tariff would “blow a hole in the U.S. industry that we've never seen.” Volvo Cars CEO Jim Rowan said that profitability would suffer under any tariffs, whether those are the general 25% tariffs on Canada and Mexico (now seemingly canceled after Trump backed down), just-announced
Since the 1860 Hippomobile, hydrogen has been a part of powered mobility. Today, most hydrogen storage applications use cylindrical tanks, but other solutions are available. At a recent Bosch-sponsored event, SAE Media noted Linamar's Flexform conformable storage, which the company says uses the same or less material for a given storage volume while delivering anywhere from 5-25% more volumetric efficiency than conventional cylindrical tanks within that volume. “We see space as a regular bounding box where all you're losing is this area around the corners, closer to five to 10% [loss]. Where Flexform really shines and where the value proposition really is, is irregular spaces, such as between frame rails,” said representatives from the Linamar engineering team.
Nestled in a commercial park in Sunnyvale, California, sits the Mercedes-Benz research and development North America office. A spinning star sits in the front of the building. It is one of six locations across North America and joins research facilities in Asia and Europe. During a recent media roundtable, Mercedes-Benz CEO Ola Källenius told journalists that the original purpose for the facility 30 years ago was because it recognized that Silicon Valley was a unique place where top academia meets with venture capital and where smart people from around the world gather. “So the very first intent with the first few baby steps of coming to Silicon Valley was like, it's almost like you send out a group of people to do reconnaissance, create contact, be part of the conversation, and figure out what's going on,” Källenius said.
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