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In 2021, 412,432 road accidents were reported in India, resulting in 153,972 deaths and 384,448 injuries. India has the highest number of road fatalities, accounting for 11% of the global road fatalities. Therefore, it is important to explore the underlying causes of accidents on Indian roads. The objective of this study is to identify the factors inherent in accidents in India using clustering analysis based on self-organizing maps (SOM). It also attempts to recommend some countermeasures based on the identified factors. The study used Indian accident data collected by members of ICAT-ADAC (International Centre for Automotive Technology - Accident Data Analysis Centre) under the ICAT-RNTBCI joint project approved by the Ministry of Heavy Industries, Government of India. 210 cases were collected from the National Highway between Jaipur and Gurgaon and 239 cases from urban and semi-urban roads around Chennai were used for the analysis.

This study focused on European NCAP activities of introducing a new head protection evaluation procedure, as proposed by BASt (Federal Highway Research Institute - GERMANY). Various kinds of E-bikes are available in the market, ranging from E-bikes that have a small motor to assist the rider’s pedal-power i.e., pedelecs to somewhat more powerful E-bikes which is similar to a moped-style scooter. This paper focused on identifying the factors influencing bicyclist head kinematics during bicycle vs. passenger vehicle (PV) collisions at the intersection. Two AM50 bicyclist FE models are developed using i) GHBMC Human Body Model (HBM) and ii) WorldSID (WS) side impact dummy. Head kinematics of bicyclists of pedal-assist E-bike and normal bike were compared using CAE simulation. It is found that the vehicle’s impact velocity, type of bicycle, the mass of E-bike and bicycle traveling speed will influence the head kinematics.

In total, 865 intersection car-to-car crashes (NASS-CDS CY 2004-2014) are analyzed in detail to determine the injury level outcome based on different crash factors, such as delta-V, age, airbag deployment, number of events, impact locations (F,Y,P,Z,D,B-regions based on CDC codes), amount of compartment intrusion and impact angle. A multivariate logistic regression test was performed to predict the probability of MAIS3+ serious injuries using lateral delta-V, location of maximum deformation from B-PLR, age (0: <60/1: ≥60 years), number of events (0: single/ 1: multiple), intrusion (0: <16cm/ 1: ≥16cm), side airbag deployment (yes/no) and direction of impact (0: 9/ 1: 10 o’clock). It is found that direction of impact is one of the significant (p<0.05) parameters and 10 o’clock angle impact has more influence than 9 o’clock perpendicular lateral impact. Frequency of AIS3+ injuries was high in Y-region impact cases.

Many active safety systems are being developed with the intent of protecting pedestrians namely; pedestrian airbags, active hood, active emergency braking (AEB), etc. Effectiveness of such protection system relies on the efficiency of the sensing systems. The pop-uphood system was developed to help reduce pedestrian head injuries. A pop-up system is expected to make full deployment of the hood before the pedestrian’s head could hit the hood. The system should have the capability to detect most road users ranging from a six year old (6YO) child to a large male. To test the sensing system, an impactor model (PDI-2) was developed. Sensor response varies for vehicles with different front end profile dimensions.

Logistic regression analysis for accident cases of NASS-PCDS (National Automotive Sampling System-Pedestrian Crash Data Study) clearly shows that the extent and the degree of pedestrian's lower extremity injury depend on various factors such as the impact speed, the ratio of the pedestrian height to that of the bonnet leading edge (BLE) of the striking vehicle, bumper to knee ratio, bumper lead angle, age of the pedestrian, and posture of the pedestrian at the time of impact. The pedestrian population is divided in 3 groups, equivalent to small-shorter, medium-height and large-taller pedestrian with respect to the “pedestrian to BLE height-ratio” in order to quantify the degree of influence of lower leg injuries in each group. Large adult male finite element model (95th percentile male: 190 cm and 103 kg) was developed by morphing the Japan Automobile Manufacturers Association (JAMA) 50th percentile male.

A comprehensive analysis was performed to evaluate the effect of BMI on different body region injuries for side impact. The accident data for this study was taken from the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS). It was found that the mean BMI values for driver and front passengers increases over the years in the US. To study the effect of BMI, the range was divided into three groups: Thin (BMI<21), Normal (BMI 24-27) and Obese (BMI>30). Other important variables considered for this study were model year (MY1995-99 for old vehicles & MY2000-08 for newer vehicles), impact location (side-front F, side-center P & side-distributed Y) and direction of force (8-10 o'clock for nearside & 2-4 o'clock for far-side). Accident cases involving older occupants above 60 years was omitted in order to minimize the bone strength depreciation effect. Results of the present study indicated that the Model Year has influence on lower extremity injuries.

A logistic regression analysis of accident cases in the NASS-PCDS (National Automotive Sampling System-Pedestrian Crash Data Study) database clearly shows that pedestrian pelvis injuries tend to be complex and depend on various factors such as the impact speed, the ratio of the pedestrian height to that of the bonnet leading edge (BLE) of the striking vehicle, and the gender and age of the pedestrian. Adult female models (50th %ile female AF50: 161 cm and 61 kg; 5th %ile female AF05: 154 cm and 50 kg) were developed by morphing the JAMA 50th %ile male AM50 and substituting the pelvis of the GHBMC AM50 model. The fine-meshed pelvis model thus obtained is capable of predicting pelvis fractures. Simulations conducted with these models indicate that the characteristics of pelvis injury patterns in male and female pedestrians are influenced by the hip/BLE height ratio and to some extent by the pelvis bone shape.