Browse Topic: Fatal injuries
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.
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.
Severe rear-impact collisions can cause significant intrusion into the occupant compartment when the structural integrity of the rear survival space is insufficient. Intrusion patterns are influenced by impact configuration—underride, in-line, or override—with underride collisions channeling forces below the beltline through the rear wheels as a primary load path. This force concentration rapidly propels the rear seat-pan forward, contacting the rearward-rotating front seatback. The resulting bottoming-out phenomenon produces a forward impulse that amplifies loading on the front occupant’s upper torso, increasing the risk of thoracic injury even when the head is properly supported by the head restraint. This study analyzes a real-world rear-impact collision that resulted in fatal thoracic injuries to the driver, attributed to the interaction between the driver’s seatback and the forward-moving rear seat pan. A vehicle-to-vehicle crash test was conducted to replicate similar intrusion characteristics and assess the relative kinematics between the seatback and rear seat structure. Results demonstrate that seatback bottoming out under intrusion conditions significantly elevates thoracic loading. These findings highlight the need for improved rear structural design strategies to manage load paths in underride scenarios and to minimize front seatback rearward collapse and associated occupant loading.
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.
Single motorcycle accidents are common in Nagano Prefecture where is mountainous areas in Japan. In a previous study, analysis of traffic accident statistics data suggested that the fatality and serious injury rates for uphill right curves and downhill left curves are high, however the true causes of these accidents remain unclear. In this study, a motorcycle simulator was used to evaluate the driving characteristics due to these road alignments. Evaluation courses based on combinations of uphill/downhill slopes and left/right curves were created, and experiments were conducted. The subjects of the study were expert riders and novice riders. The results showed that right curves are even more difficult to see near the entrance of the curve when accompanied by an uphill slope, making it easier to delay recognition and judgment of the curve. Expert riders recognized curves faster than novice riders. Additionally, expert riders take a large lean of the vehicle body, actively attempted to ride on the inside corner, while that of novice riders was less. On the other hand, for downhill left curves, there was a tendency for delayed judgment of sharp curves, and riders were more likely to increase their speed. Also, the expert riders recognized curve curvature earlier and had a greater lean angle of the vehicle body than the novice riders, but there was no significant difference. From these results, the road alignment combination of uphill/downhill slopes and left/right curve has a significant impact on the risk of motorcycle accidents.
Rotorcraft continue to experience higher fatal accident rates compared to fixed-wing aircraft, primarily due to low altitude flight operations and reduced situational awareness in complex environments. A critical factor is the limited availability of accurate, up-to-date information on helipads and surrounding obstacles - such as trees, poles, and buildings - that pose significant risks during takeoff and landing. Existing resources, including the Federal Aviation Administration's heliport registry, are often outdated and incomplete, particularly for private or state-operated sites, and fail to report nearby obstacles. This lack of up-to-date data is largely due to privacy restrictions at certain locations and the high cost associated with comprehensive obstacle surveys. To address this challenge, we develop a deep learning (DL) framework that automatically detects helipads and nearby obstacles from high-resolution satellite imagery. Our approach combines Mask R-CNN for precise pixel-level helipad segmentation with Grounding DINO, a zero-shot vision-language model that identifies obstacles using flexible text prompts (e.g., "Pole", "Tree") without task-specific training. This text-guided, scalable detection method adapts to diverse and evolving operational settings. We validate our framework across helipads in the United States, and demonstrate strong performance in both helipad localization and obstacle detection. In addition, we build a web-based application that automates image processing, updates incorrect heliport coordinates, and provides obstacle reports. This work aims to enhance aviation safety, modernize infrastructure records, and deliver scalable tools to the aviation and machine learning communities.
Every year, more than 5 million people in the United States are diagnosed with heart valve disease, but this condition has no effective long-term treatment. When a person’s heart valve is severely damaged by a birth defect, lifestyle, or aging, blood flow is disrupted. If left untreated, there can be fatal complications.
Head injuries from interior impacts during vehicle accidents are a significant cause of fatalities in India. Data from the National Crime Records Bureau (NCRB) for 2023 reveals that approximately 15% of the total 150,000 road fatalities were due to head impacts on vehicle interiors, resulting in about 22,500 deaths. Thus, head impact protection in a car crash is key during the design of vehicle interiors. IS 15223 and ECE-R21 provide specific guidelines for head impact testing of instrument panels and consoles in vehicles to ensure compliance with safety standards and minimize the risk of head injury during collisions. By systematically addressing each aspect of IS 15223 and ECE- R21 in the design, testing, and documentation phases, manufacturers can ensure that console armrests are optimized for safety. This approach not only helps meet regulatory standards but also enhances overall occupant protection in vehicles during collisions. The objective of this paper is to design a console armrest that meets stringent head impact testing requirements and thereby enhances occupant safety in automotive applications. The research focuses on optimizing the armrest’s structural integrity to withstand dynamic loads and to transfer or dissipate that impact energy effectively.
In India, Driver Drowsiness and Attention Warning (DDAW) system-based technologies are rising due to anticipation on mandatory regulation for DDAW. However, readiness of the system to introduce to Indian market requires validations to meet standard (Automotive Industry Standard 184) for the system are complex and sometimes subjective in nature. Furthermore, the evaluation procedure to map the system accuracy with the Karolinska sleepiness scale (KSS) requirement involves manual interpretation which can lead to false reading. In certain scenarios, KSS validation may entail to fatal risks also. Currently, there is no effective mechanism so far available to compare the performance of different DDAW systems which are coming up in Indian market. This lack of comparative investigation channel can be a concerning factor for the automotive manufactures as well as for the end-customers. In this paper, a robust validation setup using motion drive simulator with 3 degree of freedom (DOF) is proposed to overcome the KSS validation and performance of multiple DDAW systems. Moreover, designing and testing methodology are explained in stage-by-stage approach. In addition, this study details on integration of multiple DDAW technologies and real time testing of those solutions under simulated road scenario. Thereby, data of multiple driving behavior are fetched to analyze drowsiness pattern and performance comparison study.
In nature, flying animals sense coming changes in their surroundings, including the onset of sudden turbulence, and quickly adjust to stay safe. Engineers who design aircraft would like to give their vehicles the same ability to predict incoming disturbances and respond appropriately. Indeed, disasters such as the fatal Singapore Airlines flight this past May in which more than 100 passengers were injured after the plane encountered severe turbulence, could be avoided if aircraft had such automatic sensing and prediction capabilities combined with mechanisms to stabilize the vehicle.
Prior to 1950, use of the helicopter for evacuation was extremely limited, as military top brass often considered it a worthless contraption; thus, rescue was uncertain at best for downed pilots and wounded soldiers stranded behind enemy lines. However, this all changed in Korea, where twelve U.S. Army helicopters from three detachments, working in tandem with seven, newly created Mobile Army Surgical Hospital (MASH) units, would fundamentally change the Army's medical-evacuation doctrine forever. Using several models of the Bell H-13, the Hiller H-23, and the Sikorsky H-5 and H-19, this small band of courageous pilots pushed themselves and their aircraft to their limits, transporting 21,212 critically wounded soldiers for life-saving surgery to various MASH units, cutting the fatality rate from World War II in half. Adopting the 3rd Air Rescue Squadron's motto, "That Others May Live," these pilots and their helicopters were affectionately known to the wounded as "Angels of Mercy."
Background: The Indian automobile industry, including the auto component industry, is a significant part of the country’s economy and has experienced growth over the years. India is now the world’s 3rd largest passenger car market and the world’s second-largest two-wheeler market. Along with the boon, the bane of road accident fatalities is also a reality that needs urgent attention, as per a study titled ‘Estimation of Socio-Economic Loss due to Road Traffic Accidents in India’, the socio-economic loss due to road accidents is estimated to be around 0.55% to 1.35% of India’s GDP [27] Ministry of road transport and highways (MoRTH) accident data shows that the total number of fatalities on the road are the highest (in number terms) in the world. Though passenger car occupant fatalities have decreased over the years, the fatalities of vulnerable road users are showing an increasing trend. India has committed to reduce road fatalities by 50% by 2030. In this context, the automotive industry as well as MoRTH have been taking multiple initiatives including those related to vehicular and road engineering as well as educational measures for raising awareness in the field of road safety. With the intent of lowering road traffic accidents, the government and the ministry are constantly upgrading the requirements related to vehicles, infrastructures, and their implementation thereof. In this context, various future regulations are being deliberated. One such regulation in discussion pertains to requirements for the protection of the passenger car occupants in the event of a full-frontal collision with a focus on restraint systems (AIS 201). This will push manufacturers to design more sophisticated restraints that would help to reduce restraint-induced injuries. The draft version of the standard is available for public input/ discussion. The key objective of the study documented herein is to consider various aspects of the draft standard (AIS 201) from the Indian perspective. This study investigates the research questions, enumerated below: 1 What should be the appropriate test speed for the full-frontal test based on Indian accident data? 2 What is the suitable dummy configuration in terms of gender, seating position, and age to maximize occupant safety in full frontal accidents? 3 Is the proposed ATD’s anthropometry (weight and height) suitable, based on the people involved in full frontal cases in India? 4 What are occupant injury attributes in full-frontal accidents? Key findings/ expected research findings: India has a very different mix of road traffic users and road traffic fatalities compared to that in Europe (Refer to Table 1). Further, issues like seat belt usage are of utmost concern. The demographic for India is also very different from those of developed economies. With India being a country with a rather young population, male and female dummies for driver and front passenger positions should be looked upon. This calls for India-specific safety regulations and research work on automotive test dummies. Practical implications: The study can potentially provide a suggestion for alignment of the regulatory requirements with the actual situations, thereby increasing the effectiveness of the standard.
Road traffic fatalities in India have been increasing, reaching around 150,000 fatalities a year. To reduce fatalities, some prospective studies suggested using active safety technologies such as Forward Collision Warning (FCW), and Autonomous Emergency Braking (AEB). However, the effectiveness of FCW and AEB on Indian roads using retrospective studies is not known. Vehicle data such as radar, and controller area network signals could be used for the evaluation of the systems (FCW and AEB). However, these data are not readily accessible. This exploratory study aims to explore the opportunities and limitations of using simple dashboard cameras for a Field Operational Test. One European car with state-of-the-art FCW and AEB systems was rented. Fifteen drivers shared the vehicle, driving almost 10,000 km over 29 days. The vehicle was mounted with a set of dashboard cameras. The navigator noted the “system activated” events and “no activation” events in the logbook during the drive. Post completion of the driving activity, the system activated events: single event (only FCW) or combined event (FCW + AEB), and no activation events were analyzed. Three evaluators classified each system activated event as either a true positive or a false positive. Further, no activation events, where the driver felt the FCW should but did not activate, were classified as false negatives. A total of 79 single and combined events were identified. The AEB system produced 2.9 true positives and 0.2 false positives per 1000 km, while the FCW system produced 6.3 true positives and 2.1 false positives per 1000 km. For the FCW system, there were also 0.3 false negatives per 1000 km. The inter-rater reliability for the three evaluators was moderate indicating that not enough data is provided to accurately understand and classify the events. Reliable performance evaluation with the chosen simple approach seems highly challenging.
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