







































Lingxiao Sun /Future Technology                                                                                    November 2025| Volume 04 | Issue 04 | Pages 
311-317 

311 

 

 

 

Review 

Innovative applications of big data and simulation 

technologies in the optimization design of crash 

safety for autonomous vehicles: a systematic 

review from a biomechanical aspect 
Lingxiao Sun*  

School of Information Engineering, Chang'an University, Xian, 710018, China 

A R T I C L E   I N F O 
 

Article history: 
Received 10 July 2025  
Received in revised form 
29 August 2025 
Accepted 18 September 2025 
 
Keywords:  
Autonomous vehicles, Collision safety, 
Biomechanics, Big data, Simulation technology, 
Optimization design, Machine learning 
 
*Corresponding author 
Email address: 
lingxiaosunedu@163.com 
 
DOI: 10.55670/fpll.futech.4.4.25 

A B S T R A C T 
 

The rapid development of autonomous vehicles (AVs) has intensified the 
demand for advanced strategies to guarantee crash safety in increasingly 
complex traffic environments. Traditional design methods, reliant on physical 
crash tests and limited empirical data, are insufficient to capture the full 
spectrum of biomechanical responses during collisions. This systematic review 
synthesizes recent advances in the integration of big data analytics and 
simulation technologies for optimizing collision safety, with a particular focus 
on biomechanical modeling. Big data enables the large-scale collection and 
analysis of heterogeneous data sources- including vehicle sensors, physiological 
signals, and traffic dynamics- supporting the construction of high-fidelity injury 
prediction models. Simulation methods, such as finite element analysis (FEA), 
multi-body dynamics (MBD), and parametric optimization, facilitate precise 
evaluation of occupant kinematics, stress distributions, and tissue-level injury 
mechanisms. Furthermore, emerging applications of machine learning, digital 
twin systems, and biomimetic design demonstrate substantial potential for 
improving active and passive safety. This review highlights the synergistic role 
of biomechanics, data science, and simulation technologies in shaping the next 
generation of collision protection systems. Finally, it identifies key challenges—

including data privacy, model accuracy, and computational efficiency — and 

proposes future directions toward multi-scale biomechanical modeling, AI-
driven optimization, and cross-disciplinary integration for safer and more 
adaptive autonomous driving systems. 

1. Introduction 

Since the 1990s, pioneering projects such as ALVINN at 
Carnegie Mellon University demonstrated the feasibility of 
neural networks for lane-keeping in autonomous vehicles 
(AVs) [1]. The subsequent DARPA Grand Challenge further 
catalyzed advancements in perception, decision-making, and 
control technologies, driving global progress in AV 
development [2]. Today, AVs promise safer and more efficient 
transportation systems; however, ensuring crash safety in 
unpredictable real-world conditions remains a fundamental 
challenge. Traditional vehicle safety design has relied heavily 
on physical crash tests and restraint system evaluations. 
While effective in conventional contexts, these methods 
struggle to address the complex dynamic responses of the 
human body and the variability of AV operating 
environments. As a result, novel approaches that combine 
biomechanics, big data analytics, and advanced simulations 

are increasingly essential [3]. Biomechanics offers critical 
insights into the kinematic and physiological responses of 
occupants during collisions, including joint motion, tissue 
deformation, and energy transfer pathways. When integrated 
with vehicle dynamics models, biomechanics enables a 
deeper understanding of injury mechanisms and supports the 
design of more adaptive safety systems [4]. At the same time, 
big data provides the foundation for capturing multi-source 
information—from in-vehicle sensors and traffic networks to 

physiological and behavioral parameters—allowing for more 
personalized and context-aware safety solutions [5]. 
Simulation technologies, such as finite element analysis (FEA) 
and multi-body dynamics (MBD), further expand the design 
space by enabling detailed modeling of structural 
deformation, occupant kinematics, and tissue-level stresses 
[6]. When coupled with machine learning, digital twin 
platforms, and biomimetic design strategies, these methods 

 

 Open Access Journal 

ISSN 2832-0379 

November 2025| Volume 04 | Issue 04 | Pages 311-317 

https://doi.org/10.55670/fpll.futech.4.4.25 

 

 

 

Journal homepage: https://fupubco.com/futech 

 

Future Technology 

mailto:lingxiaosunedu@163.com
https://doi.org/10.55670/fpll.futech.4.4.25
https://fupubco.com/futech


Lingxiao Sun /Future Technology                                                                                    November 2025| Volume 04 | Issue 04 | Pages 311-317 

312 

 

present unprecedented opportunities to optimize both active 
and passive safety performance [7]. This review 
systematically examines the innovative applications of big 
data and simulation technologies in collision safety design, 
with a particular emphasis on their contributions from a 
biomechanical perspective. By analyzing recent progress in 
data-driven injury modeling, advanced simulation 
methodologies, and optimization frameworks, this study aims 
to establish a comprehensive roadmap for future research. 
Unlike earlier reviews that considered big data, simulation, or 
biomechanics in isolation, this paper provides an integrative 
perspective that explicitly connects large-scale data analytics, 
machine learning models, simulation technologies, and 
biomechanical validation into a unified framework. This 
novelty ensures a more complete understanding of collision 
safety and distinguishes this work from existing literature. 

1.1 Research objectives 
• To identify how big-data analytics contribute to crash risk 

assessment and severity prediction. 
• To examine the role of machine learning models in 

perception, prediction, and decision-making for safety-
critical scenarios. 

• To evaluate simulation technologies for traffic-level and 
occupant-level safety analysis. 

• To integrate biomechanics and human factors into system-
level evaluations of crash safety. 

• To propose future directions for digital twins, AI-driven 
optimization, and regulatory applications in AV safety. 

2. Related works 

Research on autonomous vehicle safety has developed 
rapidly in recent years, with efforts spanning sensing 
technologies, big-data analytics, machine learning, 
simulation, and occupant biomechanics. Early breakthroughs 
in perception and control established the foundations of AV 
research. For example, Carnegie Mellon’s ALVINN system 
demonstrated lane-keeping with neural networks [1], while 
the DARPA Grand Challenge stimulated advances in 
autonomous navigation and decision-making [2]. Subsequent 
work has expanded toward robustness in localization, 
communication, and safety assurance, each of which 
contributes to collision prevention and mitigation. 

2.1 Sensing, localization, and communication 
Accurate perception and positioning are indispensable 

for collision safety. Robust localization under GNSS-denied 
conditions has been achieved using LiDAR and visual sensing 
in high-dynamics environments [6]. Fusion of multiple 
sensors, including inertial and visual inputs, has improved 
real-time loop closure performance in SLAM-based 
navigation [7-9]. On the communication side, the 
development of 5G network slicing has been proposed to 
support ultra-low-latency vehicular services, which are 
critical for collision avoidance and cooperative safety 
applications. At the same time, resilience against adversarial 
interference has been explored, with approaches designed to 
maintain safety even when sensor attacks compromise 
normal operation. These studies highlight that perception and 
communication layers form the essential substrate for 
reliable safety functions. 

2.2 Big-data analytics for crash risk 
The growth of large-scale traffic datasets has enabled 

new approaches to crash risk modeling. Data-mining 
techniques have been applied to discover unrecorded 
highway incidents and improve accident databases [10]. 

Urban-scale analyses have used spatial grid modeling to 
identify pedestrian collision hotspots and the factors 
contributing to them [11]. More advanced statistical 
techniques, such as spatio-temporal kernel density 
estimation, have been employed to analyze accident 
distributions, including those involving electric vehicles [12]. 
Machine learning has also been introduced into crash data 
analysis. For instance, comparative studies of classifiers 
demonstrated that random forests achieved superior 
accuracy in predicting collision severity, outperforming 
Bayesian and k-nearest neighbor models [13]. Time-series 
based models have further been proposed to capture spatio-
temporal features of traffic flow for dynamic risk prediction 
[14]. Together, these studies illustrate how big data can serve 
as a basis for more proactive and context-aware collision 
safety assessment. 

2.3 Machine learning for prediction and decision-
making 
Beyond data mining, machine learning techniques have 

been widely applied to prediction and decision-making in 
safety-critical AV tasks. Vehicle–pedestrian interaction has 
been modeled using spatio-temporal learning frameworks, 
enabling more accurate risk assessment in urban settings 
[14]. VR-based simulation environments have been combined 
with decision-tree models to predict pedestrian collision risks 
under different scenarios [15]. Advances in pedestrian 
detection and classification have also been reported, 
including the use of multispectral sensing to improve 
detection accuracy under challenging conditions [16]. 
Behavior classification frameworks have been developed to 
predict pedestrian intent and interaction with vehicles [17-
19]. At the decision-making level, frameworks based on 
partially observable Markov decision processes (POMDPs) 
have been designed to reduce unnecessary braking while 
maintaining safety in occluded environments [20]. Related 
applications in other transport domains demonstrate the 
potential of AI-based optimization for collision avoidance 
[21]. Emerging approaches, such as transformer-based 
sequence models, graph neural networks (GNNs), and 
reinforcement learning (RL), are increasingly applied to 
trajectory prediction, complex interaction modeling, and 
adaptive decision-making. While still in early stages, these 
techniques show potential to enhance the robustness of AV 
safety systems and merit further exploration in 
biomechanical contexts. 

2.4 Simulation and traffic dynamics 
Simulation studies provide an essential complement to 

empirical data in assessing collision risks and safety 
measures. DSRC-based cooperative braking systems have 
been evaluated in simulation environments, demonstrating 
measurable improvements in rear-end collision avoidance 
[19]. Connected and automated vehicles (CAVs) have been 
shown to reduce traffic oscillations in microscopic 
simulations, indirectly contributing to improved safety [22, 
23]. Near-miss incidents have been integrated into Monte 
Carlo simulations, reducing error rates in crash frequency 
prediction compared to traditional methods [24]. Reviews of 
blackspot identification methods and transportation safety 
analytics also underline the need for rigorous evaluation 
frameworks [25, 26]. Driving simulators have been widely 
used to study human factors, though limitations remain in 
terms of fidelity and transferability to real-world conditions 
[27]. These simulation-based studies highlight both the 



Lingxiao Sun /Future Technology                                                                                    November 2025| Volume 04 | Issue 04 | Pages 311-317 

313 

 

potential and constraints of virtual experimentation in AV 
safety evaluation. 

2.5 Human factors and biomechanics 
In addition to external traffic risks, occupant responses 

during sudden maneuvers or collisions remain a critical 
research focus. Naturalistic experiments have recorded the 
kinematics of unrestrained passengers in autonomous 
shuttles during emergency braking, showing that postural 
variation significantly influences segmental motion and 
overall injury risk [28]. Cognitive and behavioral aspects are 
equally important. Studies have shown that executive 
functions strongly affect safe driving behaviors [29], while 
risk perception differs across user groups and influences 
exposure to danger [30]. Pedestrian behavior classification, 
intent recognition, and situational awareness further connect 
human factors with predictive safety models [23, 24, 30]. 
These findings underline that biomechanical and behavioral 
research must be integrated into AV safety systems to ensure 
that system-level improvements translate into actual 
reductions in occupant injury. 

3. Methodology 

This review follows an integrative methodology 
designed to connect big-data analysis, machine learning 
models, and simulation-based biomechanics into a unified 
framework for assessing collision safety in autonomous 
vehicles. The approach combines three main steps: (i) 
acquisition and processing of large-scale driving and crash-
related datasets, (ii) modeling and simulation of traffic 
dynamics and occupant biomechanics, and (iii) integration of 
results into a cross-disciplinary framework. 

3.1 Data acquisition and processing 
Multi-source datasets from naturalistic driving studies, 

crash databases, and vehicle sensors provide the basis for 
collision risk assessment. Data preprocessing includes 
cleaning, filtering, and spatio-temporal alignment. Extracted 
features, such as vehicle speed, acceleration, and relative 
position, support both descriptive statistics and predictive 
models. Figure 1 shows the data flow from collection to 
analysis, highlighting the multi-layered nature of safety-
related data. 

 
Figure 1 Movement of a subject during the emergency braking test 
[21] 

3.2 Machine learning models 
Machine learning enables proactive identification of 

high-risk scenarios and adaptive safety interventions. 
Supervised models are used to classify crash severity or 
predict accident likelihood, while spatio-temporal learning 
frameworks capture dynamic risk in traffic flows. For 
vulnerable road users, pedestrian intent recognition and 
multispectral detection improve early-warning performance. 
At the decision-making level, probabilistic frameworks 
support motion planning under uncertainty. Figure 2 
illustrates a generic prediction – control loop where 

perception feeds into risk models that inform planning. 

 
Figure 2. Individual tree of the Random Forest ensemble [15] 

3.3 Simulation of traffic and occupant safety 
Simulation extends safety evaluation beyond what is 

feasible with physical experiments. Traffic-level simulations 
test cooperative braking or connected vehicle strategies, 
while Monte Carlo models estimate crash frequencies under 
varied conditions. Driving simulators are employed to 
investigate human factors such as driver workload and 
attention. At the occupant level, finite element and multi-body 
models describe how bodies respond to impact, while 
naturalistic experiments provide validation data. Figure 3 
depicts the layered structure of these simulation approaches. 

3.4 Biomechanics and Human Factors 
Occupant biomechanics translates external crash forces 

into internal physiological responses. Kinematic 
measurements from shuttle experiments have shown that 
posture affects motion trajectories during sudden maneuvers. 
Human cognitive factors and risk perception also influence 



Lingxiao Sun /Future Technology                                                                                    November 2025| Volume 04 | Issue 04 | Pages 311-317 

314 

 

safety outcomes. Figure 4 presents representative 
biomechanical motion data, emphasizing the variability of 
occupant responses. 

 

Figure 3. Integration process of simulation technology in collision 
prediction 

 

 
Figure 4. A schematic representation of the seat model 

 

3.5 Integrated framework 
By linking risk identification, machine learning, 

simulation, and biomechanics, the methodology ensures that 
safety is assessed across multiple scales— from traffic-level 
conflict metrics to tissue-level injury mechanisms. Figure 5 
illustrates this integrative framework. 

 
Figure  5.  A  two-dimensional  multi-body  biomechanical  model  of  
the  human  body  with  seated posture 

4. Results and discussion 

4.1 Data-driven risk identification 
Large datasets have revealed consistent spatial and 

temporal patterns of crashes. Hotspots emerge in dense 
urban environments, and statistical models improve the 
estimation of crash severity. Machine learning further 
enhances predictive performance, confirming that data-
driven approaches can provide early insights for safety 
planning. 

4.2 Machine learning applications 
Machine learning improves traffic safety in several ways. 

Deep models capture complex temporal dependencies in 
traffic flow data, enhancing real-time risk prediction. 
Pedestrian detection systems achieve higher accuracy when 
enriched with multimodal sensing, and intent recognition 
helps anticipate conflicts. Decision-making models reduce 
unnecessary interventions, striking a balance between safety 
and efficiency. Figure 6 demonstrates an example of 
improved classification performance in pedestrian risk 
prediction. 

 
Figure 6. Joint representation 



Lingxiao Sun /Future Technology                                                                                    November 2025| Volume 04 | Issue 04 | Pages 311-317 

315 

 

4.3 Simulation outcomes 
Simulation studies confirm the benefits of connected and 

automated driving. Cooperative braking reduces response 
delays, microscopic traffic simulations show smoother flows 
with fewer critical events, and Monte Carlo models provide 
more reliable frequency estimates when combined with near-
miss data. However, limitations remain in simulator fidelity, 
as experimental setups do not always capture real-world 
biomechanical responses. 

4.4 Biomechanical insights 
Occupant experiments reveal that unrestrained 

passengers experience large variability in kinematic 
responses. Posture, body segment coordination, and cognitive 
factors such as attention influence injury risk. These results 
underscore the importance of integrating biomechanical 
evidence into system-level evaluations. Figure 7 and Figure 8 
present examples of occupant kinematic trajectories under 
emergency braking. To synthesize the results across different 
methodological approaches, Table 1 provides a comparative 
summary of the main advantages, limitations, and 
applications of big data analytics, machine learning, 
simulation technologies, and biomechanics in the context of 
autonomous vehicle safety. 

Table 1. Comparative summary of different approaches for collision 

safety 

Approach Advantages Limitations 
Applications 
in AV Safety 

Big Data 
Analytics 

Captures 
large-scale 
crash 
patterns; 
identifies 
hotspots and 
trends 

Limited by 
data quality, 
missing 
exposure 
measures 

Crash 
severity 
prediction; 
hotspot 
identification 

Machine 
Learning 

Improves 
detection, 
classification, 
and 
prediction; 
adaptable to 
real-time 

Often lacks 
uncertainty 
quantification; 
dataset bias 

Pedestrian 
intent 
prediction; 
decision-
making 

Simulation 
(FEA/MBD, 
traffic 
models) 

Enables 
virtual 
testing; cost-
effective; 
multi-scale 
analysis 

Limited 
fidelity; results 
may not 
transfer to 
real-world 
conditions 

Cooperative 
braking 
evaluation; 
traffic 
oscillation 

Biomechanics 

Links system-
level risk to 
human injury 
outcomes; 
posture-
specific 
insights 

Requires 
complex 
experiments; 
high 
variability 
among 
occupants 

Injury 
mechanism 
analysis; 
occupant 
protection 

 

The reviewed studies collectively show that integrating 
big data, machine learning, and simulation-based 
biomechanics provides a more complete picture of collision 
safety. Big-data methods identify when and where risks are 
likely to occur. Machine learning extends this by predicting 
future scenarios and supporting decision-making. Simulation 
bridges the gap between abstract risk indicators and 

measurable occupant outcomes, while biomechanics ensures 
that safety is defined not only in terms of crash avoidance but 
also in terms of human injury mitigation. 

 

 

 

 

 

 

 

 

 

 

(a) 

 

 

(b) 

 

 

(c) 
 

Figure 7. Collision process and movement form of lower limbs (a) 
phase 1; (b) phase 2; (c) phase 3 

 

 

 



Lingxiao Sun /Future Technology                                                                                    November 2025| Volume 04 | Issue 04 | Pages 311-317 

316 

 

 
Figure 8. Design scheme of bionic knee joint 

Nevertheless, several challenges remain. First, data 
limitations restrict generalizability, with issues such as 
inconsistent event definitions and demographic bias. Second, 
machine learning models often lack uncertainty 
quantification, making it difficult to judge reliability. Third, 
simulation fidelity and transferability remain concerns, as 
results may not fully reflect real-world conditions. Finally, 
there is limited cross-layer integration — advances in 
perception and communication are seldom connected 
directly to occupant injury outcomes. Future research should 
aim to create standardized, open datasets with harmonized 
definitions, incorporate robust uncertainty estimation into 
prediction models, and strengthen validation of simulations 
against biomechanical experiments. A closer alignment with 
international safety standards will also ensure that findings 
are transferable to practice. 

5. Conclusion 

This review has synthesized recent advances in big-data 
analytics, machine learning, simulation technologies, and 
biomechanics to evaluate and optimize collision safety in 
autonomous vehicles. The analysis demonstrates that data-
driven methods are effective for identifying crash hotspots 
and predicting severity, while machine learning significantly 
enhances detection, intent recognition, and decision-making 
in complex urban environments. Simulation studies validate 
the benefits of cooperative driving strategies but continue to 
face challenges in model fidelity and transferability. 
Biomechanical investigations further reveal the variability of 
occupant responses, emphasizing the influence of posture, 
cognition, and human factors on injury outcomes. The novelty 
of this work lies in offering an integrative perspective that 
explicitly links large-scale risk analysis, predictive machine 
learning, simulation environments, and biomechanical 
validation. By bridging these domains, the review moves 
beyond traditional crash testing and demonstrates the 
potential for adaptive, personalized, and biomechanically 
informed safety systems. Looking forward, several research 
directions are critical. First, the development of multi-scale 
human body models and digital twin systems will enable real-
time coupling between external crash dynamics and internal 

injury mechanisms. Second, cloud-based simulation and 
edge-AI frameworks should be explored to achieve scalable, 
low-latency, and computationally efficient safety evaluation. 
Third, greater attention must be given to uncertainty 
quantification, dataset bias, and privacy protection, ensuring 
that predictive models remain reliable and ethically robust. 
Finally, closer alignment with international safety assessment 
protocols—such as Euro NCAP and NHTSA guidelines—will 

accelerate the translation of biomechanically informed 
findings into practical vehicle safety standards. By addressing 
these challenges, future research can support the 
development of autonomous vehicles that are not only 
capable of navigating safely but also capable of providing 
transparent, human-centered, and regulation-compliant 
crash protection. 

Ethical issue 
The author is aware of and complies with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The author adheres to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 
be available upon request from the author. 

Conflict of interest 
The author declares no potential conflict of interest. 

References 

[1] Pomerleau, D. A. (1988). ALVINN: An autonomous 
land vehicle in a neural network. Advances in Neural 
Information Processing Systems, 1. DOI: not found 

[2] Yang, T., Murguia, C., Nesi, D., & Yuen, C. (2024). 
Towards crash-free autonomous driving: Anomaly 
detection and control for resilience to stealthy sensor 
attacks. IEEE Internet of Things Journal, 12(1). DOI: 
10.1109/JIOT.2024.3459590 

[3] Lee, S., Lee, S., Seong, H., Hyun, J., & Kim, E. (2023). 
Fallen person detection for autonomous driving. 
Expert Systems with Applications, 213, 119242. DOI: 
10.1016/j.eswa.2022.119242.  

[4] Wang, Y., Jiang, Y., Wu, Y., & Yao, Z. (2023). Mitigating 
traffic oscillation through control of connected 
automated vehicles: A cellular automata simulation. 
Expert Systems with Applications, 235, 121275. DOI: 
10.1016/j.eswa.2023.121275.  

[5] Chekired, D. A., Togou, M. A., Khoukhi, L., & Ksentini, 
A. (2019). 5G-slicing-enabled scalable SDN core 
network: Toward an ultra-low latency of autonomous 
driving service. IEEE Journal on Selected Areas in 
Communications, 37(8), 1769–1782. DOI: 
10.1109/JSAC.2019.2927065 

[6] Massa, F., Bonamini, L., Settimi, A., Pallottino, L., & 
Caporale, D. (2020). LiDAR-based GNSS-denied 
localization for autonomous racing cars. Sensors, 
20(14), 3992. DOI: 10.3390/s20143992 

[7] Deilamsalehy, H., & Havens, T. C. (2016). Sensor fused 
three-dimensional localization using IMU, camera and 
LiDAR. IEEE SENSORS (Orlando). DOI: 
10.1109/ICSENS.2016.7808523 

[8] Hess, W., Kohler, D., Rapp, H., & Andor, D. (2016). 
Real-time loop closure in 2D LiDAR SLAM. ICRA 2016, 
1271–1278. DOI: 10.1109/ICRA.2016.7487258. 



Lingxiao Sun /Future Technology                                                                                    November 2025| Volume 04 | Issue 04 | Pages 311-317 

317 

 

[9] P. Agrawal, A. Iqbal, B. Russell, M. K. Hazrati, V. 
Kashyap and F. Akhbari, "PCE-SLAM: A real-time 
simultaneous localization and mapping using LiDAR 
data," 2017 IEEE Intelligent Vehicles Symposium (IV), 
Los Angeles, CA, USA, 2017, pp. 1752-1757, doi: 
10.1109/IVS.2017.7995960. 

[10] Shi, A., Tao, Z., Xinming, Z., & Jian, W. (2014). 
Unrecorded accidents detection on highways based 
on temporal data mining. Mathematical Problems in 
Engineering, 2014, 852495. DOI: 
10.1155/2014/852495.  

[11] Xie, K., Ozbay, K., Kurkcu, A., & Yang, H. (2017). 
Analysis of traffic crashes involving pedestrians using 
big data: Investigation of contributing factors and 
identification of hotspots. Risk Analysis, 37(8), 1459–
1476. DOI: 10.1111/risa.12751.  

[12] Zhuang, Y., Dong, C., Li, P., & Xu, B. (2024). Identifying 
spatio-temporal pattern of electric vehicles involved 
traffic accidents. Journal of Transportation Safety & 
Security, 16(12), 1448–1468. DOI: 
10.1080/19439962.2024.2342478 

[13] Abeyratne, D., & Halgamuge, M. N. (2023). Applying 
big data analytics on motor vehicle collision 
predictions in New York City. (Book chapter). DOI: 
10.1002/9781119544487.ch11 

[14] Zhou, Z. (2024). Applied mathematics and nonlinear 
sciences. Sciences, 9(1), 1-18. DOI: 
10.2478/amns.2023.2.00338 

[15] Losada, Á., Páez, F. J., Luque, F., & Piovano, L. (2022). 
Application of machine learning techniques for 
predicting potential vehicle-to-pedestrian collisions 
in virtual reality scenarios. Applied Sciences, 12(22), 
11364. DOI: 10.3390/app122211364.  

[16] Iftikhar, S., Zhang, Z., Asim, M., Muthanna, A., 
Koucheryavy, A., & Abd El-Latif, A. A. (2022). Deep 
learning-based pedestrian detection in autonomous 
vehicles: Substantial issues and challenges. 
Electronics, 11(21), 3551. DOI: 
10.3390/electronics11213551.  

[17] Zhang, Y., Zhang, D., & Jiang, H. (2023). A review of 
artificial intelligence-based optimization applications 
in traditional active maritime collision avoidance. 
Sustainability, 15(18), 13384. DOI: 
10.3390/su151813384 

[18] Papathanasopoulou, V., Spyropoulou, I., Perakis, H., 
Gikas, V., & Andrikopoulou, E. (2021). Classification of 
pedestrian behavior using real trajectory data. MT-
ITS 2021, 1–6. DOI: 10.1109/MT-
ITS49943.2021.9529266. 

[19] Chen, M., Zhan, X., Tu, J., & Liu, M. (2019). Vehicle-
localization-based and DSRC-based autonomous 
vehicle rear-end collision avoidance concerning 
measurement uncertainties. IEEJ Transactions on 
Electrical and Electronic Engineering, 14(9), 1348–
1358. DOI: 10.1002/tee.22936.  

[20] Chen, Z., Ngai, D. C. K., & Yung, N. H. C. (2008). 
Pedestrian behavior prediction based on motion 
patterns for vehicle-to-pedestrian collision 
avoidance. ITSC 2008, 316–321. DOI: 
10.1109/ITSC.2008.4732782.  

[21] Santos-Cuadros, S., Page del Pozo, Á., Álvarez-Caldas, 
C., & San Román García, J. L. (2024). Kinematic 
analysis of an unrestrained passenger in an 
autonomous vehicle during emergency braking. 

Frontiers in Bioengineering and Biotechnology, 12, 
1270181. DOI: 10.3389/fbioe.2024.1270181.  

[22] Xu, C., Gao, J., Zuo, F., & Ozbay, K. (2024). Estimating 
urban traffic safety and analyzing spatial patterns 
through the integration of city-wide near-miss data: A 
New York City case study. Applied Sciences, 14(14), 
6378. DOI: 10.3390/app14146378. 

[23] Zhang, C., He, J., Wang, H., Ye, Y., Yan, X., Wang, C., & 
Zhang, X. (2024). A systematic review of the 
application and prospect of road accident blackspots 
identification approaches. Transportation Letters. 
DOI: 10.1080/19427867.2024.2416304 

[24] Ugurel, E., Wu, X., Wang, R., Lee, B. H. Y., & Chen, C. 
(2024). Metropolitan Planning Organizations’ uses 
of and needs for big data. Findings. DOI: 
10.32866/001c.127143.  

[25] Gálvez-Pérez, D., Guirao, B., & Ortuño, A. (2024). 
Analysis of the elderly pedestrian traffic accidents in 
urban scenarios: The case of the Spanish 
municipalities. International Journal of Injury Control 
and Safety Promotion, 31(3), 376–395. DOI: 
10.1080/17457300.2024.2335482.  

[26] Völz, B., Mielenz, H., Agamennoni, G., & Siegwart, R. 
(2015). Feature relevance estimation for learning 
pedestrian behavior at crosswalks. ITSC 2015, 854–
860. DOI: 10.1109/ITSC.2015.7323382.  

[27] Schratter, M., Bouton, M., Kochenderfer, M. J., & 
Watzenig, D. (2019). Pedestrian collision avoidance 
system for scenarios with occlusions. IEEE Intelligent 
Vehicles Symposium (IV), 1054–1060. DOI: 
10.1109/IVS.2019.8813822.  

[28] Badhon, F. A., Chowdhury, S. S., Haque, T., Rahman, S., 
Raihan, M. A., Hossain, M., & Al Mamun, M. A. (2023). 
Risk perception of vehicle-to-vehicle vendors and 
general pedestrians: A comparative study. 
Transportation Research Record. DOI: 
10.1177/03611981231182927 

[29] De Winter, J., van Leeuwen, P. M., & Happee, R. 
(2012). Advantages and disadvantages of driving 
simulators: A discussion. Measuring Behavior 2012 
(conference). DOI: not found 

[30] León-Domínguez, U., Solís-Marcos, I., Barrio-Álvarez, 
E., Barroso, Y., Martín, J. M., & León-Carrión, J. (2017). 
Safe driving and executive functions in healthy 
middle-aged drivers. Applied Neuropsychology: 
Adult, 24(5), 395–403. DOI: 
10.1080/23279095.2015.1137296. 

 

  

This article is an open-access article distributed under the 

terms and conditions of the Creative Commons Attribution 

(CC BY) license 

(https://creativecommons.org/licenses/by/4.0/). 

https://creativecommons.org/licenses/by/4.0/

