Browse Topic: Injuries
The persistent rate of accidents and fatalities involving legacy tactical military vehicles underscores a critical need for Enhanced Situational Awareness (ESA) technologies. However, the prohibitive cost and lengthy development cycles associated with full MIL-STD ruggedization often prevent these safety systems from reaching the in-service non-combat vehicles with limited driver visibility. This paper suggests a strategic shift in procurement policy: The adoption of relaxed ruggedization standards for vehicles operating in non-combat, administrative, and training roles. By deriving requirements from high-stress commercial sectors— such as heavy mining, steel production, and NASCAR racing—the military can utilize electronics designed for "extreme industrial" rather than "battlefield" environments. The principal objectives of this relaxation is cost reduction, lowering the barrier to entry and increasing the likelihood of ESA deployment across the legacy fleet. Furthermore, this approach aligns with Modular Open Systems Approach (MOSA) principles by enabling the integration of non-proprietary commercial devices. Utilizing these accessible technologies on legacy platforms creates a real-world testbed to evaluate technological advances rapidly. These insights can then inform and accelerate the development of future MIL-STD systems for combat vehicles, effectively shortening the traditional development life cycle while prioritizing the immediate enhanced protection of service member lives.
Since 2019, sex equity in traffic crashes has been a highly debated topic in vehicle safety, especially following the 2019 study by Forman et al. (1) claiming that female occupants face a 73 percent greater risk of serious injury in frontal crashes compared to male occupants. This was soon followed by a Consumer Reports Article by Keith Barry (2), which attempted to identify underlying factors contributing to the higher risk. These have been embraced by several parties since 2019. Firstly, it was alleged that vehicle design practice over the last four decades considered safety for the male population only and ignored that of the female as evidenced by the exclusive use of the mid-sized male Anthropomorphic Test Devices (ATDs) in Regulatory and Safety Ratings tests and not with an average sized female ATD. The absence of such an ATD for testing of vehicles “set the course for four decades’ worth of car safety design, with deadly consequences” (2). Secondly, although there is a recognition of the fact that Regulatory testing with a Small Female ATD, the Hybrid III-05F, was introduced in the FMVSS208 in 2003, this ATD was only a scaled version of the average male ATD of the 1970’s implying that this ATD is incapable of driving the design of restraint systems for females due to “They’re put together differently. Their material properties—their structure—is different” (2). Thirdly, according to a quote “These same trends have been observed in many, many studies in the past.” We assume that the trends refer to the apparent disparity in safety of females when compared to those of males. This document aims to outline historical activities, associated research and the development of countermeasures addressing crashworthiness concerns related to vehicle safety for females, as well as factors affecting both males and females, such as age-related impacts. This paper deals mainly with the frontal crash modes, mentions side impacts briefly as it affected designs of inflatable restraints for side impact to protect the smaller portion of the population from inflation induced injuries but the history behind the use of female ATDs by IIHS and NHTSA in full scale testing is not covered. Where ever possible, the time periods of reported activities related to female safety have been divided to pre-1997 corresponding to a change in US frontal crash regulation to address serious-to-fatal injuries to females and children, between 1997 to 2003 corresponding to the proposal by Canada for its frontal impact standard, and between 2003 and 2006 when the Advanced Restraint Regulation in the US FMVSS 208 was promulgated. This was followed by activities between 2007 and 2019, and post 2019 period.
Researchers recently helped Skydio, the leading U.S. drone manufacturer, demonstrate compliance to the Federal Aviation Administration's rules for safe flights over people and vehicles. Virginia Polytechnic Institute and State University, Blacksburg, VA Operators using a drone from the leading manufacturer in the U.S. can now conduct missions over people and vehicles much easier and with even greater confidence in their safety. In January, the Federal Aviation Administration (FAA) accepted a declaration of compliance for such flights for the parachute-equipped Skydio X10 drone from Skydio, a San Mateo, California-based company that supplies its drones to customers in public safety, utilities, and national security. The acceptance came as the result of working with Virginia Tech's Mid-Atlantic Aviation Partnership (MAAP) and Center for Injury Biomechanics to complete their FAA-approved means of compliance testing.
Pilots, drivers, and automated safety systems in cars and airplanes could be alerted to icy hazards by a pair of sensors developed at the University of Michigan. Ice on roads causes around 20 percent of weather-related car crashes each year, and ice build-up on planes causes roughly 10 percent of all fatal air carrier crashes by interfering with aerodynamics and controls. For instance, a flight from Brazilian airliner Voepass Linhas Aéreas crashed near São Paulo on August 9, 2024, after the plane’s de-icing systems failed, according to a report in the Aviation Safety Network. An Air France flight also crashed in the Atlantic Ocean on June 1, 2009, after ice blocked the probes that measure the plane’s speed. In both cases, all occupants died.
Industries are following a tedious product development cycle for developing their product. In product development major steps includes design ideas, Drawings, CAD, CAE, Testing and design improvement cycle. This is a monotonous process and takes time which impacts on its time to deliver product and cost on development. Now a days industries are fast growing and targeting to reduce development cycle time and cost. AI&ML is impacting almost all areas in the industry and significantly reducing efforts time and cost. To make use of AI&ML in CAE, Altair Physics AI is an effective tool. To ensure the design of product traditional way is to develop a CAD of the product, develop, perform CAE and analyze performance. If we consider CAE procedure it is time consuming process which includes FEA model build, applying boundary conditions, running simulation and analyzing results which could take minutes to hours. By using ML with Physics AI we can make predictions on new design of the product in seconds and significantly save time and cost. To demonstrate the CAE acceleration process with physic AI we have solved two case studies. The first case study is head impact on hood where ML tool will predict deformation contour of the hood, acceleration and displacement curve of the impactor. The second case study is Tube crush analysis where prediction of tube deformation pattern, force and energy curve for different tube length and impact velocity is carried out. For both Case studies we have used TCS inhouse data to train test and prediction of the ML model. For Head impact case study, it gives lower training loss with more than 90 percent prediction accuracy. Similarly for tube crush study it gives good accuracy and predicts comparable behavior patten with CAE results. Physic AI ML tool accelerates the design and development cycle and can be utilized in different product development. Implementation of ML accelerates the CAE process in design and development of products. It saves a lot of time in multiple design iteration study. Similar method can be implemented for different CAE cases.
Despite remarkable advances in vehicle technology - enhancing comfort, safety, and automation – productivity of transportation over the road continues to decline. Stop-and-go driving remains one of the most persistent inefficiencies in modern mobility systems, leading to greater travel delays, energy waste, emissions, and accident risk. As vehicle volumes rise, these effects compound into systemic challenges, including driver frustration, unstable flow dynamics, and elevated greenhouse gas (GHG) emissions. To address these issues, an extensive data-driven evaluation was performed characterizing the underlying causes of traffic instability and uncovering hidden behavioral parameters influencing traffic flow. This research led to the identification of a previously unrecognized metric - the Driver Comfort Index (DCI) - which quantifies an inter-vehicle spacing behavior that reflects intrinsic human driving behavior. Building on this discovery, mixed traffic is explored to identify its phenomena, where human-driven and machine-controlled vehicles coexist and share the road. It appears that adaptive cruise control (ACC) and connected autonomous vehicles (CAV) are controlled by a non-intrinsic parameter so that traffic mix suffers from a mismatch of vehicle dynamics. This mismatch is explored, and it is proposed to harmonize traffic dynamics by adopting the natural DCI parameter as the single control mechanism. Analytical studies demonstrate that DCI-based traffic flow orchestration, applied integrally to human- and machine-controlled vehicles, enhances traffic flow stability, mitigates stop-and-go oscillations, and significantly improves network efficiency, safety, and environmental performance.
Avoiding and mitigating any potential collision is dependent on (1) road user ability to avoid entering into a conflict (conflict avoidance effect) and (2) road user response should a conflict be entered (collision avoidance effect). This study examined the collision avoidance effect of the Waymo Driver, a currently deployed SAE level 4 automated driving system (ADS), using a human behavior reference model, designed to be representative of a human driver that is non-impaired, with eyes on the conflict (NIEON). Reliable performance benchmarking methodologies for assessing ADS performance are an essential component of determining system readiness. This consistently performing, always-attentive driver does not exist in the human population. Counterfactual simulations were run on responder collision scenarios based on reconstructions from a 10-year period of human fatal crashes from the Operational Design Domain of the Waymo ADS in Chandler, Arizona. Of 16 simulated conflicts entered, 12 (75%) were prevented by the Waymo Driver, and 10 (62.5%) were prevented by the NIEON model. The NIEON Model mitigated an additional 5 collisions and did not mitigate 1 collision. In these 16 conflicts entered, 93% of serious injury risk was reduced by the Waymo Driver, whereas 84% of serious injury risk was reduced by the NIEON model. Further, in a case-by-case evaluation, the Waymo Driver’s collision avoidance led to reduced serious injury risk when compared to the NIEON model in every simulated event. The results of this paper demonstrate that a reference model like NIEON can be used to benchmark ADS responder performance in response to high-risk initiating behaviors performed by the current driving population.
Researchers have developed a fully customizable finger brace that can, with the push or flex of a finger, easily switch from stiff to flexible. Along with its versatility, the brace can be 3D printed and requires no assembly.
As vehicles are becoming more complex, maintaining the effectiveness of safety critical systems like adaptive cruise control, lane keep assist, electronic breaking and airbag deployment extends far beyond the initial design and manufacturing. In the automotive industry these safety systems must perform reliably over the years under varying environmental conditions. This paper examines the critical role of periodic maintenance in sustaining the long-term safety and functional integrity of these systems throughout the lifecycle. As per the latest data from the Ministry of Road Transport and Highways (MoRTH), in 2022, India reported a total of 4.61 lakh road accidents, resulting in 1.68 lakh fatalities and 4.43 lakh injuries. The number of fatalities could have been reduced by the intervention of periodic services and monitoring the health of safety critical systems. While periodic maintenance has contributed to long term safety of the vehicles, there are a lot of vehicles on the road which are not serviced regularly. This paper aims to fill this critical gap by proposing a system where the government agencies actively collect and monitor vehicle maintenance data and diagnostics data ensuring that all vehicles on the road undergo mandatory periodic servicing to uphold the integrity of safety-critical systems. This paper concludes by proposing a centralized framework for data sharing and proactive monitoring to ensure the sustained performance of safety-critical systems—ultimately reducing preventable road fatalities and improving overall vehicular safety across India.
Pedestrian safety is a critical concern in India, where rapid urbanization, increased vehicular traffic, and inadequate infrastructure pose significant risks to pedestrians. This study aims to analyze pedestrian accidents across various regions in India, drawing insights from comprehensive accident data. By examining accident patterns, risk factors, and contributing variables, we seek to inform policy recommendations and enhance pedestrian safety measures.
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