Browse Topic: Vehicle occupants
ABSTRACT One of the main thrusts in current Army Science & Technology (S&T) activities is the development of occupant-centric vehicle structures that make the operation of the vehicle both comfortable and safe for the soldiers. Furthermore, a lighter weight vehicle structure is an enabling factor for faster transport, higher mobility, greater fuel conservation, higher payload, and a reduced ground footprint of supporting forces. Therefore, a key design challenge is to develop lightweight occupant-centric vehicle structures that can provide high levels of protection against explosive threats. In this paper, concepts for using materials, damping and other mechanisms to design structures with unique dynamic characteristics for mitigating blast loads are investigated. The Dynamic Response Index (DRI) metric [1] is employed as an occupant injury measure for determining the effectiveness of the each blast mitigation configuration that is considered. A model of the TARDEC Generic V-Hull
Abstract On the Mobile Detection Assessment Response System (MDARS) production program, General Dynamics Robotics Systems (GDRS) and International Logistics Systems (ILS), are working with the US Army’s Product Manager – Force Protection Systems (PM-FPS) to reduce system costs throughout the production lifecycle. Under this process, GDRS works through an Engineering Change Proposal (ECP) process to improve the reliability and maintainability of subsystem designs with the goal of making the entire system more producible at a lower cost. In addition, GDRS recommends substitutions of Government requirements that are cost drivers with those that reduce cost impact but do not result in reduced capability for the end user. This paper describes the production lifecycle process for the MDARS system and recommends future considerations for fielding of complex autonomous robotic systems
In recent years, battery electric vehicles (BEVs) have experienced significant sales growth, marked by advancements in features and market delivery. This evolution intersects with innovative software-defined vehicles, which have transformed automotive supply chains, introducing new BEV brands from both emerging and mature markets. The critical role of software in software-defined battery electric vehicles (SD-BEVs) is pivotal for enhancing user experience and ensuring adherence to rigorous safety, performance, and quality standards. Effective governance and management are crucial, as failures can mar corporate reputations and jeopardize safety-critical systems like advanced driver assistance systems. Product Governance and Management for Software-defined Battery Electric Vehicles addresses the complexities of SD-BEV product governance and management to facilitate safer vehicle deployments. By exploring these challenges, it aims to enhance internal processes and foster cross
Road safety remains a critical concern globally, with millions of lives lost annually due to road accidents. In India alone, the year 2021 witnessed over 4,12,432 road accidents resulting in 1,53,972 fatalities and 3,84,448 injuries. The age group most affected by these accidents is 18-45 years, constituting approximately 67% of total deaths. Factors such as speeding, distracted driving, and neglect to use safety gear increases the severity of these incidents. This paper presents a novel approach to address these challenges by introducing a driver safety system aimed at promoting good driving etiquette and mitigating distractions and fatigue. Leveraging Raspberry Pi and computer vision techniques, the system monitors driver behavior in real-time, including head position, eye blinks, mouth opening and closing, hand position, and internal audio levels to detect signs of distraction and drowsiness. The system operates in both passive and active modes, providing alerts and alarms to the
The integration of Vehicle-to-Everything (V2X) communication technologies holds immense potential to revolutionize the automotive industry by enabling vehicles to communicate with each other (V2V) and with infrastructure (V2I). This paper investigates the feasibility of V2X and V2I communication, exploring available communication methods for vehicles to communicate. Many a times people like to travel together and it involves more than one vehicle travelling together, in such cases they often get lost the information about fellow vehicles due to the traffic condition and different driving behaviors of the individual driver. In such cases they communicate over phones to get to know the location of fellow vehicle or keep sharing their live locations. In such cases they don’t just follow the destination in maps also they should be continuously monitoring their fellow vehicles position. It is important for vehicles travelling in group to have communication and be connected so that they know
In an influential essay in 2019, entitled ‘The Bitter Lesson’, machine learning researcher Richard Sutton observed that the main driver of progress in artificial intelligence (AI) is the continued scaling up of computational power1. This view predicts that while manual approaches that embed human knowledge and understanding in AI agents lead to satisfying advances in the short term, in the long run they only stand in the way of developing more general, scalable methods. This provocative conclusion has led to heated debates about the role of human ingenuity, but the ‘bitter lesson’ paradigm has more or less played out in the area of natural language processing. By using scaled-up neural networks and as many text examples from the internet as possible as training data, researchers could solve previously complex problems of producing human language without syntactical errors. Further scaling has produced general-purpose and multimodal models with billions of parameters such as GPT-4
This SAE Recommended Practice establishes uniform procedures for assuring the manufactured quality, installed utility, and service performance of manual automotive adaptive products, other than those provided by the OEM, intended to provide driving capability for persons with physical disabilities. These devices function as adaptive appliances to compensate for lost or reduced performance in the drivers’ arms or legs, or both. Some of the devices are designed to transfer foot functions to the hands, hand functions to the feet, or functions from one side of the body to the other. This document applies only to primary controls as defined in 3.4.1 and in the Foreword. In particular, this document is specifically concerned with those mechanical and hybrid products that are intended by the manufacturer of the adaptive product to: Be installed within the occupant space of the vehicle Be operated by a vehicle driver with a physical disability Be added to, or substituted for, the OEM vehicle
Continental's Georg Fässler, executive chair of the 2024 SAE COMVEC, details efforts to future-proof forthcoming vehicles. Severe driver shortages, rising fuel and material costs, escalating demand for freight transport, higher sustainability requirements - there is no shortage of challenges facing the transport sector. Commercial vehicle manufacturers and industry suppliers are devoting significant resources to develop, test and bring to market the technological advances that will help alleviate these pressure points. “The digitalization of commercial vehicles and the whole logistics chain is a necessary response and one of the most important developments in the CV industry in my view,” said Continental Automotive's head of commercial and special vehicles, Georg Fässler, in a recent interview with SAE International
Occupant packaging is one of the key tasks involved in the early architectural phase of a vehicle. Accommodation, as a convention, is generally considered related to a car’s interior. Typical roominess metrics of the occupant like hip room, shoulder room, and elbow room are defined with the door in its closed condition. Several other roominess metrics like knee room, leg room, head room, and the like are also specified. While all the guidelines are defined with doors in their closed condition, it is also important to consider the dynamics that exist while the occupant is entering the vehicle. This article expands the traditional understanding of occupant accommodation beyond conventionally considering the vehicle interior’s ability to accommodate anthropometry. It broadens the scope to include dynamic conditions, such as when doors are opened, providing a more realistic and practical perspective. As a luxury car manufacturer, it is important to ensure the best overall customer
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. The basis for this is the route generation as a result of the evaluation of curve radii from several hundred thousand kilometers of real measurement
Modine exec says EV thermal management systems have evolved significantly from the technology used by ICE vehicles just five years ago. A rarity only a few years ago, electric vehicles (EVs) are becoming part of the daily lives of constantly increasing numbers of drivers. In the first quarter of 2024 alone, passenger EV sales soared by about 25% compared to the same period in 2023, according to the IEA's annual Global EV Outlook. While the passenger EV market charges ahead toward widespread adoption, the off-highway vehicle segment lags in electrification. The burly and rugged workhorses that do the heavy lifting in construction and agriculture have been slower in embracing electrification due to their heavier workloads and duty cycles. In addition to larger batteries, traction motors and countless other components, the electrification of this class of vehicles also requires a steep learning curve, all of which impact stakeholders up and down the value chain. For example, navigating
Kodiak Robotics launched its first autonomous military prototype vehicle in December 2023 - a Ford F-150 upfitted with the Kodiak Driver autonomous system. Developed for the Department of Defense (DoD), the vehicle runs the same software as Kodiak's autonomous long-haul trucks but with more robust DefensePod enclosures for the sensors. Now the company is collaborating with Textron Systems to develop a purpose-built uncrewed military vehicle designed without space for a driver and intended for advanced terrain environments. The companies plan to demonstrate driverless operations later in 2024. “The initial integration work is largely being done at a Textron Systems facility in Maine, with testing planned at Kodiak facilities,” Kodiak's chief technology officer Andreas Wendel told Truck & Off-Highway Engineering. He shared his thoughts on the “immense” potential for autonomous technology in tactical vehicles
iMotions employs neuroscience and AI-powered analysis tools to enhance the tracking, assessment and design of human-machine interfaces inside vehicles. The advancement of vehicles with enhanced safety and infotainment features has made evaluating human-machine interfaces (HMI) in modern commercial and industrial vehicles crucial. Drivers face a steep learning curve due to the complexities of these new technologies. Additionally, the interaction with advanced driver-assistance systems (ADAS) increases concerns about cognitive impact and driver distraction in both passenger and commercial vehicles. As vehicles incorporate more automation, many clients are turning to biosensor technology to monitor drivers' attention and the effects of various systems and interfaces. Utilizing neuroscientific principles and AI, data from eye-tracking, facial expressions and heart rate are informing more effective system and interface design strategies. This approach ensures that automation advancements
Artificial intelligence (AI)-based solutions are slowly making their way into mobile devices and other parts of our lives on a daily basis. By integrating AI into vehicles, many manufacturers are looking forward to developing autonomous cars. However, as of today, no existing autonomous vehicles (AVs) that are consumer ready have reached SAE Level 5 automation. To develop a consumer-ready AV, numerous problems need to be addressed. In this chapter we present a few of these unaddressed issues related to human-machine interaction design. They include interface implementation, speech interaction, emotion regulation, emotion detection, and driver trust. For each of these aspects, we present the subject in detail—including the area’s current state of research and development, its current challenges, and proposed solutions worth exploring
Force torque sensors are gaining more and more popularity in robotics applications — a clear trend is evident. The key drivers include the growing use of robots in unstructured environments, where they are required to perform more complex and demanding tasks, while working in cooperation with human collaborators
With further development of autonomous vehicles additional challenges appear. One of these challenges arises in the context of mixed traffic scenarios where automated and autonomous vehicles coexist with manually operated vehicles as well as other road users such as cyclists and pedestrians. In this evolving landscape, understanding, predicting, and mimicking human driving behavior is becoming not only a challenging but also a compelling facet of autonomous driving research. This is necessary not only for safety reasons, but also to promote trust in artificial intelligence (AI), especially in self-driving cars where trust is often compromised by the opacity of neural network models. The central goal of this study is therefore to address this trust issue. A common approach to imitate human driving behavior through expert demonstrations is imitation learning (IL). However, balancing performance and explainability in these models is a major challenge. To efficiently generate training data
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