Vehicle-pedestrian Collisions Research Articles

A Collision Avoidance Strategy Based on Entropy-Increasing Risk Perception in a Vehicle–Pedestrian-Integrated Reaction Space

Ensuring pedestrian safety is one of the most significant challenges for autonomous driving systems in urban scenarios due to the non-cooperative and unpredictable nature of pedestrian movements. To tackle this problem, firstly, we propose a collision avoidance strategy based on entropy-increasing risk perception in a vehicle–pedestrian reaction space. Our approach combines a limited range of reaction space regions with entropy to quantify the risk of pedestrian–vehicle collision. Then, multi-vehicle candidate trajectories are generated using the path and speed sequence method, and the uncertain states of pedestrians are predicted based on the social force model and Markov model accordingly. Finally, to determine the optimal collision avoidance trajectory, we use quantitative reaction-space entropy as a new “cost function” to measure potential risk and perform multi-objective trajectory optimization based on the elitist non-dominated-sorting genetic algorithm region-focused (NSGA-RF) approach. Simulation results show that our proposed strategy can enhance the safety of the planned trajectory interaction between vehicles and pedestrians for autonomous driving under normal and emergency conditions.

Analysis of the elderly pedestrian traffic accidents in urban scenarios: the case of the Spanish municipalities

As the elderly population grows, there is a greater concern for their safety on the roads. This is particularly important for elderly pedestrians who are more vulnerable to accidents. In Spain, one of the most aged countries in the world, the elderly accounted for 70% of all pedestrian deaths in 2019. In this study, the focus was on analysing the occurrence of elderly pedestrian-vehicle collisions in Spanish municipalities and how it is related to the built environment. The study used the hurdle negative binomial model to analyse the number of elderly and non-elderly pedestrian accidents per municipality in 2016-2019. The exploratory analysis showed that cities above 50,000 inhabitants were safer for the elderly, and larger provincial capitals had lower elderly pedestrian traffic accident rates. The occurrence of all pedestrian traffic accidents was linked to the socio-demographic features. For elderly pedestrians, land use was found to be influential, with a lower proportion of land covered by manufacturing and service activities linked to a smaller number of accidents. Results showed that improving road safety for older pedestrians may not necessarily compromise the situation for the rest of population. Hence, policymakers should focus on infrastructure improvements adapted to the needs of elderly pedestrians.

Pedestrian auditory perception of approaching vehicles from behind in shared space: the impact of quietness of electric vehicles

The reduced auditory detectability of electric vehicles (EVs) at low speeds has elicited concerns for pedestrian safety. In shared space where traffic regulations and physical separations are absent, the auditory perception of approaching vehicles from behind is a critical factor in preventing pedestrian-vehicle collisions, for both sighted and (blind) visually impaired people. This study explored the effects of the sound pressure level of background noise, vehicle type, vehicle manoeuvre and approaching direction on auditory perception. Vehicle sounds were recorded in a car park under controlled driving behaviour, and the background noise was recorded in a typical shared space. These sounds were then synthesized and reproduced in an anechoic chamber. 9 participants were presented with 48 sound cues. Their auditory perception was assessed by detection rate, direction determination, and detection distance of approaching vehicles. Results indicate that only 6% sound cues were missed by participants and it happened mostly at the highest background noise level (69 dBA), and all reported instances of approaching direction were accurately identified by the participants. All aforementioned factors were found to have a significant impact on detection distance. A regression model was also conducted and shows relative high prediction power for the detection distance (adjusted R2=0.703). The comparison between stopping distance and detection distance showed that in many experimental conditions, approaching conventional vehicles could be detected away from the stopping distance while electric vehicles could not. This paper proposes a conceptual framework for understanding the role of auditory perception in EV-pedestrian interactions, and the pilot lab experiment suggests that the quietness of EV may cause safety issue for pedestrians in shared space.

Analysis of performance properties of retro-reflective woven fabrics made with retro-reflective yarns and natural yarns

Retro-reflective (RR) materials have been the subject of recent interest largely due to their ability to ensure personal safety on the road. Their use limits vehicle–pedestrian collisions, especially at night-time. Studies have mainly developed RR textiles for aesthetics and reported on their appearance performance accordingly. This study, however, investigates the mechanical and functional properties of RR fabrics produced with RR yarns and natural yarns. The results reveal that the woven fabrics have radiative cooling effects to transmit body heat to the environment, and a good ultraviolet protection rating (15–35) with a transmittance rate below 6% depending on the weave structure. The contact angle measurements reveal that the surface of these fabrics is hydrophobic or has low wettability. The total hand value for the fabric shows their suitability for men’s dress shirts in the winter or thin women’s dresses.

Risk assessment of pedestrian red-light violation behavior using surrogate safety measures: Influence of human, road, vehicle, and environmental factors

Pedestrian red-light violation is one of the crucial causes of pedestrian crashes at urban intersections, which cause considerable injuries and casualties to this vulnerable road group of road users. The objective of this study is to evaluate the risk of pedestrian-vehicle collisions by clustering the pedestrians' red-light violations using surrogate safety measures. The present study utilized surveillance camera footage to collect data on pedestrians' red-light violations at two urban intersections in Babol City. Based on critical thresholds of post-encroachment time (PET), Time to Collision (TTC), and Gap Time (GT), three different risk levels of red-light violations were identified through the use of a K-means algorithm. Moreover, structural equation models were developed for each of the risk levels considering variables that are associated with four major components: human, environment, road, and vehicle. Lastly, policy insights into amending pedestrian behavior and promoting traffic safety culture were proposed, with an overarching emphasis on the human factor, due to its identified greater influence on the propensity for red-light violations.

Factors associated with pedestrian-vehicle collision hotspots involving seniors and children: a deep learning analysis of street-level images

ABSTRACT This study aimed to examine the factors associated with pedestrian–vehicle collision hotspots involving seniors and children. For the empirical analysis, we first quantified street-level images of collision hotspots involving seniors and children and non-collision hotspots in the Seoul Metropolitan Area, Korea, using deep learning analysis. Thereafter, we examined the risk factors associated with collision hotspots through logistic analyses. This study has two major findings. First, the effects of risk factors (e.g. share of sky and green space) differ between collision hotspots involving seniors and children. Second, some pedestrian safety treatments (i.e. traffic lights and sidewalks) are positively associated with collision risks. The findings suggest that varied approaches to enhancing pedestrian safety among different age groups should be considered for more effective pedestrian safety interventions. In addition, the quality of pedestrian safety measures should be examined to improve pedestrian safety for seniors and children.

Developing A New Model for Assessment of Heavy Vehicle-Pedestrian Collisions

The treatment and analysis of accidents involving heavy transport vehicles and pedestrians include the identification and treatment of a certain number of factors that may differ from the cases of passenger vehicle-pedestrian accidents. The aim of this paper is to develop a new model with better performance for speed estimation and reconstruction of accidents involving heavy vehicles and pedestrians. In a large number of cases during the research, it was observed that the experts used the same models for passenger vehicles as for transport vehicles. Likewise, a number of factors that have an impact on heavy vehicle accidents with pedestrians are not included as factors that have an impact on other accidents. The newly developed model, which has better performance than other models, can help experts in the case of analysis, speed determination, and reconstruction of accidents involving heavy vehicles and pedestrians. The model describes more than 94% of the most influential factors in the model (R2 = 0.945). This model will provide a novel way to examine crashes involving heavy vehicles and pedestrians, generating highly precise results for speed calculation which can be used to recreate the technical aspects of the accident. Additionally, it will help specialists in the field when preparing their expert opinion, specifically when heavy vehicles and pedestrians are involved, by providing a model which is different from the standard approach and yields more reliable outcomes.

The Role for Preperitoneal Pelvic Packing in Low-to-Middle-Income Countries: A 16-Year Experience at a Johannesburg Trauma Unit.

Preperitoneal pelvic packing for early pelvic haemorrhage control reduces mortality. Bleeding noted with pelvis fractures is predominantly due to associated venous complex injuries. More studies are advocating for angiography as first-line therapy for haemodynamic instability in pelvic fractures; however, these facilities are not in abundance in middle- and low-income countries. We hypothesized that PPP improves outcomes under these circumstances. Retrospective analysis of data from the patients charts over a period of 16years from 01 January, 2005 to 31 December, 2020. All patients over the age of 18years who presented with haemodynamic instability from a pelvic fracture and required PPP were included. The demographics, physiological parameter in emergency department, blood products transfused, morbidity and mortality were analysed. There were 110 patients identified in the study period who underwent pelvic preperitoneal packing for refractory shock or ongoing bleeding. The majority (75.5%) of patients were men (n = 83). The median age was 38years. The most common mechanism of injury was pedestrian vehicle collision (51%), followed by motor vehicle collisions (27.3%). The median ISS and NISS were 35 and 40, respectively. The median RTS in ED was 4.8(3-6.8). None of our patients rebleed after pack removal and no one needed repacking or adjunct angioembolization in our study group. The in-hospital mortality rate was 43.6% (n = 48) in patients who underwent preperitoneal pelvic packing. The operating room table mortality was 20% (n = 22/110), and the mortality rate of those who survived to ICU transfer was 29.5% (n = 26/88). Pelvic preperitoneal packing has a role in the acute management of haemodynamically abnormal patients with pelvic fractures in our environment. In the absence of immediate angioembolization, preperitoneal packing can be lifesaving.

Reconstruction of a Car-Running Pedestrian Accident Based on a Humanoid Robot Method.

Due to the characteristics of multibody (MB) and finite element (FE) digital human body models (HBMs), the reconstruction of running pedestrians (RPs) remains a major challenge in traffic accidents (TAs) and new innovative methods are needed. This study presents a novel approach for reconstructing moving pedestrian TAs based on a humanoid robot method to improve the accuracy of analyzing dynamic vehicle-pedestrian collision accidents. Firstly, we applied the theory of humanoid robots to the corresponding joints and centroids of the TNO HBM and implemented the pedestrian running process. Secondly, we used rigid-flexible coupling HBMs to build pedestrians, which can not only simulate running but also analyze human injuries. Then, we validated the feasibility of the RP reconstruction method by comparing the simulated dynamics with the pedestrian in the accident. Next, we extracted the velocity and posture of the pedestrian at the moment of collision and further validated the modeling method through a comparison of human injuries and forensic autopsy results. Finally, by comparing two other cases, we can conclude that there are relative errors in both the pedestrian injury results and the rest position. This comparative analysis is helpful for understanding the differences in injury characteristics between the running pedestrian and the other two cases in TAs.

ASSESSMENT OF AUTONOMOUS EMERGENCY BRAKING (AEB) PEDESTRIAN SYSTEM IMPACT ON 2016 – 2020 MALAYSIAN ANIMAL-CROSSINGS ACCIDENTS DATA

The trend of animal-vehicle collision (AVCs) occurrences over the past years demonstrates increasing numbers, and this call for a proper mitigation plan by appropriate authority bodies. Autonomous Emergency Braking (AEB) pedestrian system - proven effective in collision prevention and mitigation for vehicle-pedestrian collision – can potentially expand its original functionality to AVCs avoidance. This study presents a new data assessment method to predict the impact of AEB pedestrian system implementation on vehicles to reduce AVCs cases from 2016 to 2020. In general, a new scoring system is introduced whereby fitment rating points of 1, 0.5 and 0 are given to describe successful crash avoidance, crash mitigation with reduced damage and failed crash avoidance. Several noteworthy findings were discovered in assessing impact data from five significant AEB-AVCs. The effectiveness of AEB is found to be correlated with camera detection, system working speed range, frequent collision time, human casualties, and heavy vehicles. In general, the results indicate overall positive consequences of AEB implementation to reduce AVCs, providing concrete reasoning for standardising AEB pedestrian systems in all manufactured road-legal vehicles for upcoming years.

Strengthening Pedestrian Safety: An Evaluation of Signals at Major Intersections in Lahore, Pakistan

Pedestrians’ safe mobility at intersections is associated with the facilities provided at the crossings. Lahore is one of the most populous cities in Pakistan. Too many road accidents occur daily at various areas of Lahore in which pedestrian-vehicle collision has a major ratio. To reduce the pedestrian-vehicle collisions, pedestrian signals are installed at major intersections of Lahore city. This paper examines the relationship between pedestrian signals and Level of Service (LOS), with a focus on enhancing awareness of pedestrian signal operation and investigates the impact of pedestrian signals on the LOS of intersections in Lahore in terms of pedestrian movement. Research shows that the poor level of awareness about how pedestrian signals work contributes to the inadequate level of service of intersections in terms of pedestrian movement. The results also provide valuable insights for policymakers and practitioners in developing effective strategies to improve the pedestrian experience and reduce pedestrian-vehicle collisions at intersections.

Design and Performance Research of a Wearable Airbag for the Human Body

In this paper, an integrated wearable airbag is proposed to protect the vulnerable pedestrian during a vehicle–pedestrian collision accident. To evaluate the protection performance of this newly proposed integrated wearable airbag, the airbag inflator finite element model is first verified via an inflator tank test, and the simulated results agree very well with the test. Next, the finite element model of the vehicle–pedestrian model is established, including a simplified vehicle model and a biomechanical HBM with the integrated wearable airbag. The numerical results show the newly proposed wearable airbag can effectively protect the pedestrian by significantly reducing injuries to the head and chest. Since head injury is the most important injury type, a complete set of injury indexes is adopted to evaluate severity of head injury, including HIC15, 3 ms resultant acceleration, and intracranial pressure. To further improve the protection performance of the airbag on the HBM’s head, a parametric study is conducted towards two design parameters of the wearable airbag: inflator gas mass and pulling strap length. The parametric investigation shows 26 g gas and a 20 mm pulling strap can provide the best protection for the HBM’s head.

Investigating the application of deep learning to identify pedestrian collision-prone zones

The main objective of this study is to understand the factors that contribute to the frequency of both the total pedestrian-vehicle collisions and collisions that involve pedestrian violations and identify collision-prone areas. The two Full Bayes (FB) macro-level models were applied to historical collision records of the City of Hamilton to identify the collision-prone zones and the key factors that contribute to collision occurrence in TAZs. Finally, a self-organizing map (SOM) deep learning model was developed to identify collision-prone zones for the two collision classes. The results showed that the SOM model identified collision-prone zones with a high accuracy that exceeded the traditional Bayesian approach, based on the developed consistency test. As for the total collisions, the SOM model revealed that intersection density is the most important factor in distinguishing between collision-prone and non-collision-prone zones, followed by the pedestrian network directness and the proportion of residential land uses. As for the collisions that involved pedestrian violations, intersection density was also found to be the most important factor, followed by the density of bike-share stations and parking lots in a TAZ. The results of this study could aid planners in designing pedestrian-friendly networks and develop specific recommendations to enhance safety in unsafe zones.

The effect of built environment features on vehicle-pedestrian collisions in big cities: a comparison between Lisbon and Madrid

Pedestrian safety has become an important concern for urban planners. However, some factors affecting that safety remain unexplored, especially those affecting urban infrastructure and macro-level land-use features.Lisbon and Madrid are cities with similar population densities and activities. This paper compares both cities for accidents involving pedestrians, considering the location of the fatal, severely injured, and slightly injured accidents for 2015-2019 and the main characteristics of the location neighbourhoods. Furthermore, an ad-hoc database linked to the accident location has been built to model the occurrence of accidents involving pedestrians through a negative binomial regression. Results show that accidents in Lisbon are much more affected by infrastructure variables than in Madrid, where land use macro-level variables have a significant role.

Forecasting Future Trajectories with an Improved Transformer Network

An increase in car ownership brings convenience to people’s life. However, it also leads to frequent traffic accidents. Precisely forecasting surrounding agents’ future trajectories could effectively decrease vehicle-vehicle and vehicle-pedestrian collisions. Long-short-term memory (LSTM) network is often used for vehicle trajectory prediction, but it has some shortages such as gradient explosion and low efficiency. A trajectory prediction method based on an improved Transformer network is proposed to forecast agents’ future trajectories in a complex traffic environment. It realizes the transformation from sequential step processing of LSTM to parallel processing of Transformer based on attention mechanism. To perform trajectory prediction more efficiently, a probabilistic sparse self-attention mechanism is introduced to reduce attention complexity by reducing the number of queried values in the attention mechanism. Activate or not (ACON) activation function is adopted to select whether to activate or not, hence improving model flexibility. The proposed method is evaluated on the publicly available benchmarks next-generation simulation (NGSIM) and ETH/UCY. The experimental results indicate that the proposed method can accurately and efficiently predict agents’ trajectories.

Determination of vehicle speed from recorded video using the open-source software Kinovea.

Video devices of different kind often record traffic accidents, including vehicle-pedestrian collisions and hit-and-run accidents. In these cases, the vehicle speed is valuable information because it can assist the investigators in an accident reconstruction. This paper examines the use of Kinovea, an open-source video annotation tool designed for sport analysis, to estimate vehicle speed in forensic videos. Kinovea does not require a complex methodology, and it can be used to make the calculation easily. A series of vehicle driving experiments using an appropriately calibrated speed radar system (so called Scout Speed) were carried out, and measurements were compared with the estimated speed. In controlled conditions, the comparison of Scout reference speed and calculated average vehicle speed by means of Kinovea found an average difference of 0.43 km/h, with a margin of error of ±0.64 km/h. In addition, further preliminary tests were carried out to check the reliability of the measurements under lower resolution conditions. Also, in these cases the calculations were in line with the ground truth. Therefore, in the tested conditions, Kinovea demonstrated to be an easy and reliable tool available for forensic video examiners. Further tests need to be conducted in order to address the applicability of the measurement technique with true CCTV/surveillance video recordings.

Multiobjective optimization algorithm for accurate MADYMO reconstruction of vehicle-pedestrian accidents.

In vehicle-pedestrian accidents, the preimpact conditions of pedestrians and vehicles are frequently uncertain. The incident data for a crash, such as vehicle deformation, injury of the victim, distance of initial position and rest position of accident participants, are useful for verification in MAthematical DYnamic MOdels (MADYMO) simulations. The purpose of this study is to explore the use of an improved optimization algorithm combined with MADYMO multibody simulations and crash data to conduct accurate reconstructions of vehicle-pedestrian accidents. The objective function of the optimization problem was defined as the Euclidean distance between the known vehicle, human and ground contact points, and multiobjective optimization algorithms were employed to obtain the local minima of the objective function. Three common multiobjective optimization algorithms-nondominated sorting genetic algorithm-II (NSGA-II), neighbourhood cultivation genetic algorithm (NCGA), and multiobjective particle swarm optimization (MOPSO)-were compared. The effect of the number of objective functions, the choice of different objective functions and the optimal number of iterations were also considered. The final reconstructed results were compared with the process of a real accident. Based on the results of the reconstruction of a real-world accident, the present study indicated that NSGA-II had better convergence and generated more noninferior solutions and better final solutions than NCGA and MOPSO. In addition, when all vehicle-pedestrian-ground contacts were considered, the results showed a better match in terms of kinematic response. NSGA-II converged within 100 generations. This study indicated that multibody simulations coupled with optimization algorithms can be used to accurately reconstruct vehicle-pedestrian collisions.

Pedestrian Safety and Transit Corridors

This research examines the relationship between pedestrian accident locations on state-owned facilities (highways and urban arterials) and the presence of riders loading and alighting from bus transit. Many state facilities are important metropolitan transit corridors with large numbers of bus stops users, resulting in increased exposure of pedestrians to traffic and in increased numbers of collisions. The research also examines the association between pedestrian collisions and other travel generators (concentrations of retail activity and housing) as well as environmental conditions (wide roadways, high traffic volumes, and high speed limits).Based on a retrospective sampling approach and logistic regression models, the study shows that bus stop usage is associated with pedestrian collisions along state facilities. Less strong, but significant associations exist between retail location and size, traffic volume, and number of traffic lanes, and locations with high levels of pedestrian-vehicle collisions. The findings suggest that facilities with high numbers of bus riders need to accommodate people walking safely along and across the roadway. They support the development of state DOT programs for multimodal facilities, which integrate travel modes in major regional facilities within local suburban communities and pay specific attention to the role of transit in shaping the demand for nonmotorized travel on the facilities.

Reconstruction of a real-world car-to-pedestrian collision using geomatics techniques and numerical simulations

The aim of this study is to provide an improved method for traffic accident reconstruction based on geomatics techniques and numerical simulations. A combination of various techniques was used. First, an unmanned aerial vehicle (UAV), laser scanner and structured-light scanner were used to obtain information on the accident scene, vehicle and victim. The collected traces provided detailed initial impact conditions for subsequent numerical simulations. Then, multi-body system (MBS) simulations were conducted to reconstruct the kinematics of the car-to-pedestrian collision. Finally, a finite element (FE) simulation using the THUMS model was performed to predict injuries. A real-life vehicle-pedestrian collision was used to verify the feasibility and effectiveness of this method. The reconstruction result revealed that the kinematic and injury predictions of the numerical simulations effectively conformed to the surveillance video and investigation of the actual accident. UAV photogrammetry was demonstrated to be more efficient in accident data collection than hand sketch and measurement, and 3D laser scanning enabled an easier and more accurate modeling process of vehicle. The present study shows the feasibility of this method for use in traffic accident reconstruction.

Analysis of Vehicle-Pedestrian Accident Risk Based on Simulation Experiments

Vehicle-pedestrian accidents are one of the main types of road traffic accidents in China because of their mixed traffic features. By analyzing the characteristics of vehicle-pedestrian accidents, the head injury criterion (HIC) was selected as a quantitative index of pedestrian head injury risk, and vehicle-pedestrian collision simulation tests were carried out using PC-Crash. From the collected test data, the multivariate relationship models between the HIC, vehicle speed, and collision angle were fitted for different vehicle types. A risk assessment method for vehicle-pedestrian accidents based on the HIC was proposed by the Fisher optimal segmentation algorithm. Finally, a new index for evaluating the accuracy of accident risk classification, the degree of error classification, was proposed to verify the validity of the accident risk assessment method. The results show that vehicle speed, collision angle, and vehicle type play a key role in pedestrian injury. Flat-headed vehicles are more likely to cause head injuries to pedestrians than high-headed and low-headed vehicles. Rear-end collisions cause more injuries to pedestrians than side collisions. The research results can provide guidance and a basis for accident liability determination, speed limit management, vehicle safety design, and human injury mechanism analysis.