Browse Topic: Fatal injuries
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
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
Genesys Aerosystems, a Moog company, offers a line of avionics specifically designed for the military/special-mission market. Originally, the system was developed as part of the FAA's Capstone Program - first established in 1999 - to reduce the excessively high number of controlled flight into terrain (CFIT) accidents in the southeast region of Alaska. Implementation of this technology by pilots in the southeast Alaska region immediately reduced the CFIT accident rate from an average of one fatality every nine days to zero among commercial aircraft. Twenty years later, the Capstone equipment continues to provide exceptional safety, and Genesys has become a leading avionics supplier to military and special-mission fleet operators around the world, including the U.S. Navy, U.S. Army, and over 35 foreign militaries and other government operators
Motorcyclists are about 29 times more likely than passenger vehicle occupants to die in a motor vehicle crash and are 4 times more likely to be injured. Safe motorcycling takes balance, co-ordination, and good judgement. As per NHTSA, per 100,000 registered vehicles motorcycle fatality and injury rate stand at 58.33 and 975 and that of passenger vehicles stand at 9.42 and 1152. With such rates of fatality and injury of motorcyclists, there is strong need for motorcycle solutions that help to minimize traffic fatalities and improve road safety scenarios. Helmets are estimated to be 37% effective in preventing fatal injuries to motorcycle riders and 41% for motorcycle passengers but there is little to no post-crash assistance available on board the motorcycles. Post-crash emergency response is time sensitive and can be broken down into a subset of activities beginning with discovery of crash, notification, and activation of emergency medical service (EMS), response time, on-scene time
Consideration for the damaging effects to aircraft from the failure of wheels and tires should be evaluated. This document discusses the types of problems in-service aircraft have experienced and methodology in place to assist the designers when evaluating threats for new aircraft design. The purpose of this document is to provide a history of in-service problems, provide a historical summary of the design improvements made to wheels and tires during the past 40 years, and to offer methodology which has been used to help designers assess the threat to ensure the functionality of systems and equipment located in and around the landing gear and in wheel wells
Vehicles that start moving from a stationary position can cause fatal traffic accidents involving pedestrians. Ultrasonic sensors installed in the vehicle front are an active technology designed to alert drivers to the presence of stationary objects such as rigid walls in front of their vehicles. However, the ability of such sensors to detect humans has not yet been established. Therefore, this study aims to ascertain whether these sensor systems can successfully detect humans. First, we conducted experiments using four vehicles equipped with ultrasonic sensor systems for vehicle-forward moving-off maneuvers and investigated the detection distances between the vehicles and a pipe (1 m long and having a diameter of 75 mm), child, adult female, or adult male. The detections of human volunteers were evaluated under two different conditions: front-facing and side-facing toward the front of each vehicle. Front-facing is defined as the condition where the human faces the vehicle front, while
Methanol is sometimes referred to as ethanol's deadly twin. While the latter is the intoxicating ingredient in wine, beer, and liquor, the former is a chemical that becomes highly toxic when metabolized by the human body. Even a relatively small amount of methanol can cause blindness or prove fatal if left untreated
The on-vehicle automation system is primarily designed to replace the human driver during driving to enhance the performance and avoid possible fatalities. However, current implementations in automated vehicles (AVs) generally neglect that human imperfection and preference do not always lead to negative consequences, which prevents achieving optimized vehicle performance and maximized road safety. Human-like Decision-making and Control for Automated Driving takes a step forward to address breaking through the limitation of future automation applications, investigating in depth: Human driving feature modeling and analysis Personalized motion control for AVs Human-like decision making for AVs Click here to access the full SAE EDGETM Research Report portfolio
India witnessed 151,113 road deaths in the year 2019 and this alarming number is due to increased urbanization, motorization and per capita income. India is home to the 2nd largest road network in the world and accounts for the highest number of road deaths globally. Curbing the menace of road accidents requires tactical road safety policies and their effective implementation. The meagre availability of factual data regarding socio-economic loss due to road accidents is proving to be a hindrance to the ideation and implementation of the policies. The Planning Commission estimated the social costs of road accidents to be 7.9 billion $ in 1999/2000 which was roughly 3% of the country’s GDP and this value was revised to 14.3 billion $ in 2011. Absence of data regarding the loss due to road accidents in the recent times, has been a motivating factor to estimate the socio economic loss due to accidents on Indian roads. Road traffic accident casualties bring about a great deal of human
Child safety in the back seat during a rear-impact chiefly depends on how well the survival space is maintained at their location. Collapsing front seatback pose a foreseeable hazard as it intrudes into the survival space of the child on the backseat. Furthermore, the condition gets worse in the presence of a structural intrusion from the rear that tends to push the occupant further closer to the backward collapsing seatbacks. This paper reports two real-world rear impact collisions resulting severe to fatal injuries to the child occupant seating behind the driver. Each collision shows the dangers of seatback collapse into the survival space of the child. Furthermore, the paper demonstrates safety through design concept by employing seats with strong seatback design resisting collapse into the survival space of the child. The crash sled-testing are conducted to show the importance of front seatback strength preventing its collapse and occupant ramping up into the child’s survival space
As per the 2018 MoRTH accident report, there were 467,044 accidents, out of which 137,726 were fatal which resulted in 151,417 fatalities. In order to get an idea of the reasons for injuries and estimate the benefits of any intervention, a mathematical model should go a long way. This study is aimed at the development of such a model to predict the injuries sustained by the occupants of an M1 vehicle. We used a detailed accident database of 'Road Accident Sampling System India' (RASSI). RASSI, since 2011, has been collecting traffic accident data scientific across various locations in India. In the data, the occupant injuries are classified as No injury, Minor, Serious and Fatal We used the data of about 4700+ M1 occupants for the study & used almost 40 input parameters to determine the outcome. Based on the data, an algorithm was developed with an overall accuracy of about 67%. The parameters represented human, infrastructure, and environment. In 67% of the cases, the injuries were
As per WHO 2018 report, pedestrian fatalities account for 23% of world road accident fatalities. Every day 850 pedestrians lose their lives in the world. As per MoRTH 2018 report, 16% of road accident fatalities are of pedestrians in India. Everyday 64 pedestrians lose their lives in India. Based on accident data, one of the most common reason for the pedestrian fatality is head injury due to primary contact from vehicle front-end structure. Pedestrian head injury performance highly depends on front-end styling, bonnet stiffness, clearance with aggregates underneath the bonnet and hard contact points. During concept stage of vehicle development, safety recommendation on front-end design is provided based on geometric assessment of the class A surface. This paper presents the novel approach of using machine-learning algorithms to predict the head injury performance at the early stage of vehicle design using the knowledge of existing vehicle simulation data and new vehicle design
While a safe driving eco-system based on co-operation is being thought off as a possible solution to make Autonomous Driving (AD) a reality - it makes it mandatory to have every car equipped with Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technology, depends upon every driver’s proactiveness to understand their responsibility on the road when they are being warned to avoid certain maneuvers from these modules and also questions the existence of older cars on the road which are in good driving condition but it is not feasible enough or cost-effective to install V2V and V2I communication technology within. This paper provides a solution to help keep a balance within the autonomous safe driving environment where potential hazardous vehicles (e.g. manually driven cars without any V2V, V2I modules, cars with V2V, V2I modules being driven by drivers not sincere enough to follow warnings from these modules) can co-exist and the autonomous vehicles can be kept
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