Collision Speed Research Articles

Road traffic incidents involving pedestrians in areas with limited visibility

In this article, road traffic incidents involving pedestrians are determined by means of research in areas where the visibility of highways is not limited. First of all, it is determined how many meters the driver was from the time the driver saw the pedestrians crossing the road to the place where the accident occurred. Based on the information obtained from the driver, pedestrian and witnesses of the incident, the length of the stopping distance of the car is calculated as So and compared with So<Sa is clear and allows to determine whether the road is technically capable of preventing traffic accidents by applying sharp brakes in time by the driver. As a result of the research, they can come to such conclusions that the driver during the road traffic accident had the opportunity to prevent the road traffic accident, due to his lack of driving skills, i.e. experience, he violated the traffic rules. to clarify what may have been the cause of the incident and to study the collision mechanisms and speed of movement in this case, to study the above cases and to draw conclusions from the research conducted.

A method for estimating the vehicle impact speed in collision with stationary vehicle or barrier using camera video record

In Korea, most drivers equip their vehicles with a black box camera, used to estimate the speed of the vehicle in case of traffic accidents such as car-to-car collisions, single-car accidents, and car-to-pedestrian accidents. Traditionally, speed estimation using the camera video has been done by using the distance between the road lane, surrounding structures, etc.However, when the following vehicle collides with a stationary leading vehicle or barrier, and no road lanes or structures are visible around the driving path in the black box camera video of the following vehicle, the speed of the following vehicle could be just estimated according the deformations on the collided vehicles or objects.Therefore, in this study, when a vehicle collision occurs, a virtual parallel line is drawn between the leading vehicle and the following vehicle in the black box camera image of the following vehicle and analyzed geometrically to estimate the distance and the speed of the following vehicle.After the vehicle collision experiment, the speed was estimated using the method proposed in this study using the captured black box video. As a result, it was confirmed that the accuracy was high when the collision speed was low. So The method proposed in this study is expected to be used to estimate collision speed in the cases of low impact speed in the rear end collisions.

Cloud–cloud collisions triggering star formation in galaxy simulations

ABSTRACT Cloud–cloud collisions (CCCs) are expected to compress gas and trigger star formation. However, it is not well understood how the collisions and the induced star formation affect galactic-scale properties. By developing an on-the-fly algorithm to identify CCCs at each time-step in a galaxy simulation and a model that relates CCC-triggered star formation to collision speeds, we perform simulations of isolated galaxies to study the evolution of galaxies and giant molecular clouds (GMCs) with prescriptions of self-consistent CCC-driven star formation and stellar feedback. We find that the simulation with the CCC-triggered star formation produces slightly higher star formation rates and a steeper Kennicutt–Schmidt relation than that with a more standard star formation recipe, although collision speeds and frequencies are insensitive to the star formation models. In the simulation with the CCC model, about $70{{\ \rm per\ cent}}$ of the stars are born via CCCs, and colliding GMCs with masses of $\approx 10^{5.5}\, \mbox{$\rm M_{\odot}$}$ are the main drivers of CCC-driven star formation. In the simulation with the standard star formation recipe, about 50 per cent of stars are born in colliding GMCs even without the CCC-triggered star formation model. These results suggest that CCCs may be one of the most important star formation processes in galaxy evolution. Furthermore, we find that a post-processing analysis of CCCs, as used in previous studies in galaxy simulations, may lead to slightly greater collision speeds and significantly lower collision frequencies than the on-the-fly analysis.

Injury predictions for an electric-two-wheeler rider in simulated collisions with a car

Based on LS-DYNA’s finite element simulation platform and the THUMS (Total Human Model for Safety) human body model, this paper mainly focuses on analyzing and predicting for injury situations of an electric-two-wheeler (E2W) rider in simulated collisions with a car. The effective car-E2W-rider simulation model has been established to conduct simulation tests of riders’ injuries. The simulation test results indicate that there is a linear relationship between car speeds and the severity of injuries to riders across different body parts when the configuration of crash is front of cars to side of E2Ws. While the correlation between E2W speeds and human injury is relatively weak. The edge of the engine hood and the A-pillar are more harmful to the human body under the same car collision speed; The head, chest and abdomen are more likely to suffer serious injuries when speeds of two participating vehicles are relatively high. Even if at the lower speed, there is also a significant risk of tibia fractures when the lower limbs collide with the front bumper.

A computational investigation of the dynamic factors governing severity of head injury to pedestrians involved in e-scooter collisions

A rapid rise in the popularity of e-scooters has brought forth an increasing number of e-scooter-related conflicts, crashes, and injuries to pedestrians in many cities. There is a pressing need to understand the factors influencing the severity of injury to pedestrians involved in e-scooter collisions. This paper investigates the dynamics of e-scooter-pedestrian collisions and presents a new method for relating the probability of severe head injury to collision speed in e-scooter-pedestrian collisions. A total of 160 computer simulations representing different collision scenarios have been analyzed. Our results have shown that e-scooter speed is the main determinant of the severity of pedestrian head injury. E-scooter speed ranging from 10 to 15 km/h is found to be critical for pedestrian safety as the probability of severe head injury rises rapidly within this speed range. Moreover, an e-scooter-pedestrian collision is more likely to cause severe head injury to the pedestrian than a bicycle-pedestrian collision within the same speed range. It has also been found that the weight of the e-scooter and the direction of impact do not have a strong influence on the collision metrics, especially on the probability of severe head injury. The study has also investigated the post-collision fall mechanism for different pedestrian profiles and the influence of different impact angles. Finally, some recommendations have been proposed, including a speed limit of not more than 11 km/h for e-scooterist on shared paths where the likelihood of pedestrian and e-scooter conflicts is higher. The recommendations could help authorities develop legislation for safe micro-mobility.

Structural Dynamic Response of a Frame Tunnel under Coupled Impact-Explosion Loading

As the main structural bodies of underground facilities such as subway stations, frame tunnel structures play an important role in urban development and construction. In recent years, with the frequent occurrence of explosions after vehicle impacts in tunnels, which have caused severe damage to frame tunnels, the lack of relevant papers makes it necessary to develop a dynamic response analysis of frame tunnels. In this paper, by optimizing the design of an original frame tunnel structure, a dynamic response numerical model of the frame tunnel considering the impact effect of a 3D solidified vehicle and the joint effect is investigated. The deformation trend and damage evolution of the frame tunnel are analysed under different collision masses (1.4, 2.4, 6 and 15 t), collision angles (5, 10, 15 and 75°), collision speeds (60, 80 and 100 km/h), blast delay times (10, 20 and 30 ms) and blast equivalents (50, 100 and 200 kg TNT). This paper improves the active and passive robustness of frame tunnels using both improved operational management and the optimal design of the tunnel structure as a way to ensure the structural safety and functional resilience of frame tunnels.

A Novel Approach for Car Accident Detection and Analysis Using AIML Techniques

Abstract: In the context of escalating concerns regarding road safety, this study introduces an automated traffic accident detection and analysis framework that utilizes surveillance footage. The approach synergistically combines the Motion Interaction Field (MIF) model and YOLO v5 algorithm to achieve accurate crash detection and precise vehicle classification. The primary objective is to develop an autonomous system for identifying and evaluating traffic events captured by security cameras. The MIF model extracts valuable insights from interactions among moving objects within images and videos, tailored explicitly for detecting damaged vehicles. Localization of damaged vehicles is achieved using the YOLO v5 algorithm, complemented by perspective transformation to enable vertical image representation. The unbiased finite impulse response (UFIR) technique, notable for its insensitivity to statistical noise data, effectively determines vehicle motion. Furthermore, this work delves into an in-depth analysis of car crashes, estimating impact speed and point of collision from an aerial perspective. This work encompasses meticulous accident clip analysis, precise car direction determination, and error identification. The outcomes underscore the potential of the proposed framework to revolutionize accident detection and analysis, contributing to heightened road safety and streamlined traffic management strategies.

Theoretical foundations for intensifying onion set harvesting

The paper shows that upgrading the design of onion harvesting machines through the integration of diverse separation intensifiers demonstrates limited efficacy in improving the quality of cleaning commercial produce. They noted the need to comply with strict technological parameters for setting up separating systems (feeding bulbs to a straight section of the pin web surface in the absence of losses, reducing the maximum speed of collision of the bulbs with the working elements of the cleaning unit to reduce damage, as well as improving the completeness of cleaning). The study reveals that one of the options for intensifying the process of onion set cleaning from soil and soil clods can be ultrasonic-assisted heap moistening during separation. (Research purpose) The research aims to determine theoretical principles driving the intensification of onion set cleaning from mechanical impurities. (Materials and methods) The research employed system analysis and synthesis methods, physical modeling based on probability theory and mathematical statistics, numerical techniques for solving analytical dependencies, classical mechanics methods - fundamental principles of fracture theory, soil mechanics. (Results and discussion) The paper justifies the necessary optimum regime for intensifying onion set harvesting and cleansing them from mechanical impurities. It substantiates the functional diagram of a digging-type onion harvester featuring an ultrasonic cleaning module, taking into account the functions of the state, external and control actions, as well as performance indicators. (Conclusions) The research resulted in developing a design concept for an ultrasonic-equipped separating module of a harvester to improve the onion set cleaning process. This design conforms to the agrotechnical requirements, ensuring maximum soil and impurity sieving (at least 98 percent), minimizing losses (not exceeding 2 percent) and bulb damage (not exceeding 2 percent).

Experimental Evaluation and Validation of Pressure Distributions in Ice–Structure Collisions Using a Pendulum Apparatus

This study introduced an experimental setup designed to predict collision pressure exerted on a structure due to ice impacts. The primary aim was to forecast the pressure distribution and structural behavior following an ice collision on a laboratory scale. The apparatus comprises a double-pendulum system situated within a cold chamber, facilitating the prediction of collision forces by varying both the collision energy and velocity. Experiments were conducted at collision velocities of 5, 7, and 10 knots, representative of conditions in Arctic regions. By altering these velocities, we measured both the strain experienced by a steel plate and the pressure due to the ice collision. The pressure distribution on the steel plate surface was also recorded by using pressure-sensitive film. This setup effectively captured the pressure distribution across the collision contact area. The measured pressure and force were compared with that of previous studies to determine the validity of the ice collision force measurement. This methodology successfully captures the relationship between the increasing collision speed and force, providing valuable insights into ice-induced forces and structural strain. It offers an effective measurement technique beneficial for designing ice-resistant structures. In conclusion, this paper enhances our understanding of ice–structure interactions, particularly in the domains of collision force and strain metrics.

An experimental study of triboelectrostatic particle charging behavior and its associated fundamentals

Triboelectrostatic separation is a unique dry particle separation process that has been applied to many industrial applications. However, little attention has been directed to the process of charge transfer during the particle collision and friction which is critical for the success of triboelectrostatic separation. The present study has been conducted to quantify the effects of particle size, collision speed, contact mode (collision or friction), etc. on the charge density, surface potential, morphology, etc. of quartz particles based on macroscopic triboelectrostatic charge measurements and microscopic atomic force microscopy (AFM) characterization. The macroscopic charging performance results showed that the charge to mass ratio of quartz particles depended on process parameters such as feed rate and charger rotation speed and the highest charge to mass ratio reached −56.21 μC/g. The microscopic AFM characterization data reveals that the increase in particle size and collision velocity fundamentally enhanced the triboelectrostatic charging process. The surface potential of quartz increased from −60.60 mV to −477.15 mV with increasing PVC particle size from 280 μm to 1250 μm and similar behavior was observed with increasing the collision velocity from 0.99 m/s to 2.62 m/s. The change in contact mode only changed the magnitude of charge transfer but did not change the polarity of charged particle. The process of charge transfer was accompanied with the change of surface morphology and the degree of change in surface morphology was not directly related to the amount of charge transfer.

DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles

This article proposes a novel learning-based control policy with strong generalizability to new environments that enables a mobile robot to navigate autonomously through spaces filled with both static obstacles and dense crowds of pedestrians. The policy uses a unique combination of input data to generate the desired steering angle and forward velocity: a short history of lidar data, kinematic data about nearby pedestrians, and a subgoal point. The policy is trained in a reinforcement learning setting using a reward function that contains a novel term based on velocity obstacles to guide the robot to actively avoid pedestrians and move toward the goal. Through a series of 3-D simulated experiments with up to 55 pedestrians, this control policy is able to achieve a better balance between collision avoidance and speed (i.e., higher success rate and faster average speed) than state-of-the-art model-based and learning-based policies, and it also generalizes better to different crowd sizes and unseen environments. An extensive series of hardware experiments demonstrate the ability of this policy to directly work in different real-world environments with different crowd sizes with zero retraining. Furthermore, a series of simulated and hardware experiments show that the control policy also works in highly constrained static environments on a different robot platform without any additional training. Lastly, several important lessons that can be applied to other robot learning systems are summarized.

Collision speed: detailed analysis of structural and mechanical deformation energy in a car and a motorcycle

In this work, a detailed assessment of the structural and mechanical deformation energies caused by a traffic accident, involving a motorcycle and the passenger vehicle is carried out, with the objective of estimating the collision speed. Were, also verified vector orientations, braking marks and occupant launch projection for further comparative analysis with the photogrammetric method of estimating displacement speed. Finally, with the velocity values thus obtained, we compare with the result photogrammetric was performed. Thus, for the type of accident on the screen, we concluded that the best method of estimating the collision speed involving a car and a motorcycle was the combination between the method of reduction between axles and the method of mechanical deformations, resulting in the best approximation in relation to the photogrammetric result reaching 93% approximation to the photogrammetric result obtained.

Understanding Head Injury Risks During Car-to-Pedestrian Collisions Using Realistic Vehicle and Detailed Human Body Models.

Traumatic brain injury (TBI) is the leading cause of death and long-term disability in road traffic accidents (RTAs). Researchers have examined the effect of vehicle front shape and pedestrian body size on the risk of pedestrian head injury. On the other hand, the relationship between vehicle front shape parameters and pedestrian TBI risks involving a diverse population with varying body sizes has yet to be investigated. Thus, the purpose of this study was to comprehensively study the effect of vehicle front shape parameters and various pedestrian bodies ranging from 95th percentile male (AM95) to 6 years old (YO) child on the dynamic response of the head and the risk of TBIs during primary (vehicle) impact. At three different collision speeds (30, 40, and 50 km/h), a total of 36 car-to-pedestrian collisions (CPCs) were reconstructed using three different vehicle types (Subcompact passenger sedan, mid-sedan, and sports utility vehicle (SUV)) and four distinct THUMS pedestrian finite element (FE) models (AM50, AM95, AF05, and 6YO). We assessed skull stress and brain strains besides head linear and rotational kinematics. Our findings indicate that vehicle shape parameters especially bonnet leading edge height (BLEH), when being divided by the height of the Center of Gravity of the human body, correlated positively to head kinematics. The data from this study using realistic vehicle structures and detailed human body models showed that smaller BLEH/CG ratios reduced head injury criteria (HIC) and brain injury criteria (BrIC) values for the car center to mid-stance walking pedestrian impacts but with low-to-moderate R squared values between 0.2 to 0.5. Smaller BLEH/CG reduced head lateral bending velocities with R squared values of 0.57 to 0.63 for all impact velocities, and reduced HIC with R squared value of 0.62 for 50 km/h cases. In the future, simulations with realistic car structures and detailed human body models will be further used to simulate impacts at different locations and with various body shapes/postures.

Passenger and heavy vehicle collisions with pedestrians: Assessment of injury mechanisms and risk

ObjectiveAutomated Driving System (ADS) fleets are currently being deployed in several dense-urban operational design domains within the United States. In these dense-urban areas, pedestrians have historically comprised a significant portion, and sometimes the majority, of injury and fatal collisions. An expanded understanding of the injury risk in collision events involving pedestrians and human-driven vehicles can inform continued ADS development and safety benefits evaluation. There is no current systematic investigation of United States pedestrian collisions, so this study used reconstruction data from the German In-Depth Accident Study (GIDAS) to develop mechanistic injury risk models for pedestrians involved in collisions with vehicles. Data sourceThe study queried the GIDAS database for cases from 1999 to 2021 involving passenger vehicle or heavy vehicle collisions with pedestrians. MethodsWe describe the injury patterns and frequencies for passenger vehicle-to-pedestrian and heavy vehicle-to-pedestrian collisions, where heavy vehicles included heavy trucks and buses. Injury risk functions were developed at the AIS2+, 3+, 4+ and 5+ levels for pedestrians involved in frontal collisions with passenger vehicles and separately for frontal collisions with heavy vehicles. Model predictors included mechanistic factors of collision speed, pedestrian age, sex, pedestrian height relative to vehicle bumper height, and vehicle acceleration before impact. Children (≤17 y.o.) and elderly (≥65 y.o.) pedestrians were included. We further conducted weighted and imputed analyses to understand the effects of missing data elements and of weighting towards the overall population of German pedestrian crashes. ResultsWe identified 3,112 pedestrians involved in collisions with passenger vehicles, where 2,524 of those collisions were frontal vehicle strikes. Furthermore, we determined 154 pedestrians involved in collisions with heavy vehicles, where 87 of those identified collisions were frontal vehicle strikes. Children were found to be at higher risk of injury compared to young adults, and the highest risk of serious injuries (AIS 3+) existed for the oldest pedestrians in the dataset. Collisions with heavy vehicles were more likely to produce serious (AIS 3+) injuries at low speeds than collisions with passenger vehicles. Injury mechanisms differed between collisions with passenger vehicles and with heavy vehicles. The initial engagement caused 36% of pedestrians' most-severe injuries in passenger vehicle collisions, compared with 23% in heavy vehicles collisions. Conversely, the vehicle underside caused 6% of the most-severe injuries in passenger vehicle collisions and 20% in heavy vehicles collisions. SignificanceU.S. pedestrian fatalities have risen 59% since their recent recorded low in 2009. It is imperative that we understand and describe injury risk so that we can target effective strategies for injury and fatality reduction. This study builds on previous analyses by including the most modern vehicles, including children and elderly pedestrians, incorporating additional mechanistic predictors, broadening the scope of included crashes, and using multiple imputation and weighting to better estimate these effects relative to the entire population of German pedestrian collisions. This study is the first to investigate the risk of injury to pedestrians in collisions with heavy vehicles based on field data.

A predictive model to analyze the factors affecting the presence of serious chest injury in the occupants on motor vehicle crashes: Logistic regression approach

Objective Chest injuries that occur in motor vehicle crashes (MVCs) include rib fractures, pneumothorax, hemothorax, and hemothorax depending on the injury mechanism. Many risk factors are associated with serious chest injuries from MVCs. The Korean In-Depth Accident Study database was analyzed to identify risk factors associated with motor vehicle occupants’ serious chest injury. Methods Among 3,697 patients who visited the emergency room in regional emergency medical centers after MVCs between 2011 and 2018, we analyzed data from 1,226 patients with chest injuries. Vehicle damage was assessed using the Collision Deformation Classification (CDC) code and images of the damaged vehicle, and trauma scores were used to determine injury severity. Serious chest injury was defined as an Abbreviated Injury Scale (AIS) score for the chest code was more than 3. The patients were divided into two groups: serious chest injury patients with MAIS ≥ 3 and those with non-serious chest injury with MAIS < 3. A predictive model to analyze the factors affecting the presence of serious chest injury in the occupants on MVCs was constructed by a logistic regression analysis. Results Among the 1,226 patients with chest injuries, 484 (39.5%) had serious chest injuries. Patients in the serious group were older than those in the non-serious group (p=.001). In analyses based on vehicle type, the proportion of light truck occupants was higher in the serious group than in the non-serious group (p=.026). The rate of seatbelt use was lower in the serious group than in the non-serious group (p=.008). The median crush extent (seventh column of the CDC code) was higher in the serious group than in the non-serious group (p<.001). Emergency room data showed that the rates of intensive care unit (ICU) admission and death were higher among patients with serious injuries (p<.001). Similarly, the general ward/ICU admission data showed that the transfer and death rates were higher in patients with serious injuries (p<.001). The median ISS was higher in the serious group than in the non-serious group (p<.001). A predictive model was derived based on sex, age, vehicle type, seating row, belt status, collision type, and crush extent. This predictive model had an explanatory power of 67.2% for serious chest injuries. The model was estimated for external validation using the confusion matrix by applying the predictive model to the 2019 and 2020 data of the same structure as the data at the time of model development in the KIDAS database. Conclusions Although this study had a major limitation in that the explanatory power of the predictive model was weak due to the small number of samples and many exclusion conditions, it was meaningful in that it suggested a model that could predict serious chest injuries in motor vehicle occupants (MVOs) based on actual accident investigation data in Korea. Future studies should yield more meaningful results, for example, if the chest compression depth value is derived through the reconstruction of MVCs using accurate collision speed values, and better models can be developed to predict the relationship between these values and the occurrence of serious chest injury.

Massive core/star formation triggered by cloud–cloud collision – II. High-speed collisions of magnetized clouds

ABSTRACT We present a study of the effects of magnetic fields on the formation of massive, self-gravitationally bound cores (MBCs) in high-speed cloud–cloud collisions (CCCs). Extending our previous work, we perform magnetohydrodynamic simulations of the high-speed (20–40 km s−1) collisions between two magnetized (initial 4 $\mu\!$ G), turbulent clouds of different sizes in the range of 7–20 pc. We show that a magnetic field hinders the growth of cores, particularly in the case of short-duration collisions where cores are not seen to reach a highly bound state. In such cases, a shocked region created by the collision rapidly expands into the ambient medium owing to the enhanced magnetic pressure, resulting in the destruction of the highly unbound cores and suppression of gas accretion. This negative effect on the formation of MBCs has not been reported in previous hydrodynamic simulations of CCCs. Together with our previous work, we conclude that magnetic fields provide two competing effects on the MBC formation in CCCs; while they promote the mass accumulation onto cores during a collision, they operate to destroy cores or hinder the core growth after the collision. The duration of such collisions determines which effect prevails, resulting in a maximum collision speed for the MBC formation within given colliding clouds. Our results agree with the observed trend among CCC samples in the column density range probed in the simulations presented here; CCCs with higher relative velocities require higher column densities for the formation of massive stars.

Analysis of speed reductions and crash risk of aggressive drivers during emergent pre-crash scenarios at unsignalized intersections

Aggressive driver behavior (ADB) is often linked with road crashes, especially during crash imminent situations. Previous studies demonstrated that ADB was positively correlated with collision risk; however, this relationship has not quantified evidently. This study aimed to analyze drivers’ collision risk and speed reduction behavior during an emergent pre-crash scenario (such as a conflict encroaching into an unsignalized intersection at different critical time gaps) using a driving simulator. The effect of ADB on crash risk is investigated using the time to collision (TTC). Further, drivers’ collision evasive behavior is analyzed using speed reduction time (SRT) survival probabilities. Fifty-eight Indian drivers are identified as aggressive, moderately aggressive, and, non-aggressive based on aggressive indicators such as vehicle kinematics (percentage of the time spent in speeding and rapid accelerations, maximum brake pressure, etc.). Two separate models are built to analyze ADB effects on TTC and SRT using a Generalized Linear Mixed Model (GLMM) and a Weibull Accelerated Failure Time (AFT) model, respectively. From the results, it can be observed that aggressive drivers’ TTC and SRT are reduced by 82% and 38%, respectively. Compared to a 7 sec conflict approaching time gap, TTC is reduced by 18%, 39%, 51%, and 58% for 6 sec, 5 sec, 4 sec, and 3 sec conflict approaching time gaps, respectively. The estimated SRT survival probabilities for aggressive, moderately aggressive and non-aggressive drivers are 0%, 3% and 68% at 3 sec of conflict approaching time gap, respectively. SRT survival probability increased by 25% for matured drivers and decreased by 48% for drivers who tend to engage in frequent speeding. Important implications of the study findings are discussed.

Effects of an integrated safety system for swivel seat arrangements in frontal crash.

Objective: Autonomous vehicles (Avs) have paved the way for the arrangement of swivel seats in vehicles, which could pose a challenge to traditional safety systems. The integration of automated emergency braking (AEB) and pre-pretension (PPT) seatbelts improves protection for a vehicle's occupant. The objective of this study is to explore the control strategies of an integrated safety system for swiveled seating orientations. Methods: Occupant restraints were examined in various seating configurations using a single-seat model with a seat-mounted seatbelt. Seat orientation was set at different angles, from -45° to 45° with 15° increments. A pretension was used on the shoulder belt to represent an active belt force cooperating with AEB. A generic full frontal vehicle pulse of 20mph was applied to the sled. The occupant's kinematics response under various integrated safety system control strategies was analyzed by extracting a head pre-crash kinematics envelope. The injury values were calculated for various seating directions with or without an integrated safety system at the collision speed of 20mph. Results: In a lateral movement, the excursions of the dummy head were 100mm and 70mm in the global coordinate system for negative and positive seat orientations, respectively. In the axial movement, the head traveled 150mm and 180mm in the global coordinate system for positive and negative seating directions, respectively. The 3-point seatbelt did not restrain the occupant symmetrically. The occupant experienced greater y-axis excursion and smaller x-axis excursion in the negative seat position. Various integrated safety system control strategies led to significant differences in head movement in the y direction. The integrated safety system reduced the occupant's potential injury risks in different seating positions. When the AEB and PPT were activated, the absolute HIC15, brain injury criteria (BrIC), neck injury (Nij), and chest deflection were reduced in most seating directions. However, the pre-crash increased the injury risks at some seating positions. Conclusion: The pre-pretension seatbelt could reduce the occupant's forward movement in the rotating seat positions in a pre-crash period. The occupant's pre-crash motion envelope was generated, which could be beneficial to future restraint systems and vehicle interior design. The integrated safety system could reduce injuries in different seating orientations.

Research on Minimum Non-Collision Distance and Protection Strategy for Normal Train to Avoid Rear-End Accidents With Braking-Fault Train

Generally, a train adopts the method of emergency stop to avoid risks if there is any fault in the railway system. However, for braking system faults in a train, the relevant documents and specifications don’t provide a theoretical basis or solutions to ensure the parking safety of the breaking-fault train. For the tracking scenario, when a braking-fault train tracks the normal train running ahead, a calculation model of the tracking operation between trains with a failure coefficient is proposed. By detecting the braking-fault train information and the operation parameters of the train ahead, the model can calculate the minimum non-collision distance or predict the collision speed and position of these two trains. Considering the Yumen station of the Lanzhou-Xinjiang high-speed railway as an example, a series of experimental analyses are conducted under multiple fault scenarios, and the operation schemes that can avoid collision accidents are investigated. The experimental results show that the actual distance between the faulty and the front trains and the maximum acceleration that the front train could achieve are the two most critical factors. Finally, based on the information obtained from the above model results, the process of calculating the anti-collision scheme of the front train is summarized, and a protection strategy is designed, which provides the calculation basis and overall execution framework for the dispatching system to ensure the safety of these two trains.

Development of fishing boat collision models in extreme weather using computer simulation

The incidence of fishing boat accidents in Indonesia is very worrying, with 342 people dying during 2018–2020. Based on this, it is crucial to investigate the construction strength of fishing vessels against the possibility of a collision. In this study, the fishing boat due to the impact load was investigated in extreme weather conditions using Finite Element Method (FEM) analysis. Traditional fishing boat was constructed by measuring the thickness of the hull, deck, keel, frames, and longitudinal structure of the fishing boat. The collision model is carried out with an impactor in the form of a mooring pole during extreme weather with a wave height of 6 meters and wind speeds 30 knots. Variations in velocity and frame spacing as in actual conditions are modeled to obtain differences in deformation, absorption energy values, and plasticity of boat construction due to collisions. The collision speed of 30 and 20 knots are set on the extreme weather conditions, while the collision speed of 7 knots is set on operating speed. Frame spacing of 0.5 and 0.6 meters is built according to the boat's frame spacing in the field. Computer simulation is carried out using application software ANSYS Research License. The fishing boat material used is mahogany wood with tested by using impact test with a toughness value of 39.1 kJ/m2. Based on the simulations results, the impact velocity has an effect deformation wider crash area and hull stress value. The speed of the ship collision was 7 knots, the collision did not damage the hull, but the construction failed at speeds of 20 and 30 knots. The closer of frame spacing, the higher collision performance of structure to withstand impact are indicated by the higher energy absorption. At a ship collision speed of 30 knots, the absorption energy of the construction at 0.5-meter frame spacing is 49.8 kJ, greater than 0.6-meter frame spacing with a value of 29.6 kJ