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Xie Z, Ma Y, Zhang Z, Chen S. Real-time driving risk prediction using a self-attention-based bidirectional long short-term memory network based on multi-source data. ACCIDENT; ANALYSIS AND PREVENTION 2024; 204:107647. [PMID: 38796999 DOI: 10.1016/j.aap.2024.107647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 05/18/2024] [Accepted: 05/20/2024] [Indexed: 05/29/2024]
Abstract
Early warning of driving risks can effectively prevent collisions. However, numerous studies that predicted driving risks have suffered from the use of single data sources, insufficiently advanced models, and lack of time window analysis. To address these issues, this paper proposes a self-attention-based bidirectional long short-term memory (Att-Bi-LSTM) network model to predict driving risk based on multi-source data. First, driving simulation tests are conducted. Driver demographic, operation, visual, and physiological data as well as kinematic data are collected. Then, the driving risks are classified into no risk, low risk, medium risk, and high risk. Next, the Att-Bi-LSTM model is constructed, and convolutional neural network (CNN), CNN-LSTM, CatBoost, LightGBM, and XGBoost are employed for comparison. To generate the inputs and outputs of the models, observation, interval, and prediction time windows are introduced. The results show that the Att-Bi-LSTM model using early-fusion method significantly outperforms the five comparison models, with a macro-average F1-score of 0.914. The results of ablation studies indicate that the Bi-LSTM layers and self-attention layer have achieved the expected effect, which is crucial for improving the model's performance. As the interval or prediction time window is extended, the accuracy of the prediction results gradually decreases. However, as the observation time window is extended, the results first improve and then become stable. Compared to using only relative kinematic data, using all data (i.e., multi-source data) is shown to improve the F1-score by 0.061. This study provides an effective method for driving risk prediction and supports the improvement of advanced driver assistance systems.
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Affiliation(s)
- Zhuopeng Xie
- Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China; School of Civil Engineering, Faculty of Engineering, University of Sydney, Darlington NSW 2008, Australia
| | - Yongfeng Ma
- Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China.
| | - Ziyu Zhang
- Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China
| | - Shuyan Chen
- Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China
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Cui C, An B, Li L, Qu X, Manda H, Ran B. A freeway vehicle early warning method based on risk map: Enhancing traffic safety through global perspective characterization of driving risk. ACCIDENT; ANALYSIS AND PREVENTION 2024; 203:107611. [PMID: 38733809 DOI: 10.1016/j.aap.2024.107611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/09/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024]
Abstract
In the era of rapid advancements in intelligent transportation, utilizing vehicle operating data to evaluate the risk of freeway vehicles and study on vehicle early warning methods not only lays a theoretical foundation for improving the active safety of vehicles, but also provides the technical support for reducing accident rate. This paper proposes a freeway vehicle early warning method based on risk map to enhance vehicle safety. Firstly, Modified Time-to-Collision (MTTC), a two-dimensional indicator that describes the risk of inter-vehicle travel, is used as an indicator of road traffic risk. This paper designs a transformation function to probabilistically transform MTTC into Risk Indicators (RI). The single-vehicle risk map is generated based on the mapping relationship between the risk values and the corresponding roadway segments. These single-vehicle risk maps of all vehicles on the road are superimposed to construct the risk map, which is used to describe the risk distribution in the freeway. Then, a vehicle early warning framework is built based on the risk map. The risk values in the risk map are compared with predefined early warning thresholds to alert the vehicle when it enters a high-risk state. Finally, VISSIM is used to carry out simulation experiments. The experiment simulates a freeway accident stopping situation. This scenario includes sub-scenarios such as unplanned stopping and lane-changing, continuous lane-changing, and adjacent lane overtaking. We analyze the risk map and vehicle warning results in different sub-scenarios, evaluate the risk changes of the vehicles before and after receiving the warning, and compare the warning results of the method in this paper with other alternative methods. The method is applied to 17 vehicles in the simulation to adjust their motion states. The results show that the total warning time is reduced by 29.6% and 73.3% of vehicles change lanes away from the accident vehicle. The overall results validate the effectiveness of the vehicle early warning method based on risk map proposed in this paper.
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Affiliation(s)
- Chuang Cui
- School of Transportation, Southeast University, Nanjing, Jiangsu Province 211189, China; Institute on Internet of Mobility, Southeast University and University of Wisconsin-Madison, Southeast University, Nanjing, Jiangsu Province 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, Jiangsu 211189, China
| | - Bocheng An
- School of Transportation, Southeast University, Nanjing, Jiangsu Province 211189, China; Institute on Internet of Mobility, Southeast University and University of Wisconsin-Madison, Southeast University, Nanjing, Jiangsu Province 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, Jiangsu 211189, China
| | - Linheng Li
- School of Transportation, Southeast University, Nanjing, Jiangsu Province 211189, China; Institute on Internet of Mobility, Southeast University and University of Wisconsin-Madison, Southeast University, Nanjing, Jiangsu Province 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, Jiangsu 211189, China.
| | - Xu Qu
- School of Transportation, Southeast University, Nanjing, Jiangsu Province 211189, China; Institute on Internet of Mobility, Southeast University and University of Wisconsin-Madison, Southeast University, Nanjing, Jiangsu Province 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, Jiangsu 211189, China
| | - Huhe Manda
- Ordos New Energy Development and Utilization Co., Ltd, Ordos, Inner Mongolia Autonomous Region, 017000, China
| | - Bin Ran
- School of Transportation, Southeast University, Nanjing, Jiangsu Province 211189, China; Institute on Internet of Mobility, Southeast University and University of Wisconsin-Madison, Southeast University, Nanjing, Jiangsu Province 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, Jiangsu 211189, China
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Sheykhfard A, Haghighi F, Das S, Fountas G. Evasive actions to prevent pedestrian collisions in varying space/time contexts in diverse urban and non-urban areas. ACCIDENT; ANALYSIS AND PREVENTION 2023; 192:107270. [PMID: 37659276 DOI: 10.1016/j.aap.2023.107270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/31/2023] [Accepted: 08/23/2023] [Indexed: 09/04/2023]
Abstract
This study aims to identify driver-safe evasive actions associated with pedestrian crash risk in diverse urban and non-urban areas. The research focuses on the integration of quantitative methods and granular naturalistic data to examine the impacts of different driving contexts on transportation system performance, safety, and reliability. The data is derived from real-life driving encounters between pedestrians and drivers in various settings, including urban areas (UAs), suburban areas (SUAs), marked crossing areas (MCAs), and unmarked crossing areas (UMCAs). By determining critical thresholds of spatial/temporal proximity-based safety surrogate techniques, vehicle-pedestrian conflicts are clustered through a K-means algorithm into different risk levels based on drivers' evasive actions in different areas. The results of the data analysis indicate that changing lanes is the key evasive action employed by drivers to avoid pedestrian crashes in SUAs and UMCAs, while in UAs and MCAs, drivers rely on soft evasive actions, such as deceleration. Moreover, critical thresholds for several Safety Surrogate Measures (SSMs) reveal similar conflict patterns between SUAs and UMCAs, as well as between UAs and MCAs. Furthermore, this study develops and delivers a pseudo-code algorithm that utilizes the critical thresholds of SSMs to provide tangible guidance on the appropriate evasive actions for drivers in different space/time contexts, aiming to prevent collisions with pedestrians. The developed research methodology as well as the outputs of this study could be potentially useful for the development of a driver support and assistance system in the future.
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Affiliation(s)
- Abbas Sheykhfard
- Department of Civil Engineering, Babol Noshirvani University of Technology, Mazandaran 4714871167, Iran.
| | - Farshidreza Haghighi
- Department of Civil Engineering, Babol Noshirvani University of Technology, Mazandaran 4714871167, Iran.
| | - Subasish Das
- Texas State University, 601 University Drive, San Marcos, TX 77866, United States.
| | - Grigorios Fountas
- Department of Transportation and Hydraulic Engineering, School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
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Zheng Y, Wen X, Cui P, Cao H, Chai H, Hu R, Yu R. Counterfactual safety benefits quantification method for en-route driving behavior interventions. ACCIDENT; ANALYSIS AND PREVENTION 2023; 189:107118. [PMID: 37235966 DOI: 10.1016/j.aap.2023.107118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/14/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023]
Abstract
Driving behavior intervention is a dominant traffic safety countermeasure being implemented that has substantially reduced crash occurrence. However, during implementation, the intervention strategy faces the curse of dimensionality as there are multiple candidate intervention locations with various intervention measures and options. Quantifying the interventions' safety benefits and further implementing the most effective ones could avoid too frequent interventions which may lead to counterproductive safety impacts. Traditional intervention effects quantification approaches rely on observational data, thus failing to control confounding variables and leading to biased results. In this study, a counterfactual safety benefits quantification method for en-route driving behavior interventions was proposed. Empirical data from online ride-hailing services were employed to quantify the safety benefits of en-route safety broadcasting to speed maintenance behavior. Specifically, to effectively control the impacts of confounding variables on the quantification results of interventions, the "if without intervention" case of the intervention case is inferred based on the structural causality model according to the Theory of Planned Behavior (TPB). Then, a safety benefits quantification method based on Extreme Value Theory (EVT) was developed to connect changes of speed maintenance behavior with crash occurrence probabilities. Furthermore, a closed-loop evaluation and optimization framework for the various behavior interventions was established and applied to a subset of Didi's online ride-hailing service drivers (more than 1.35 million). Analyses results indicated safety broadcasting could effectively reduce driving speed by approximately 6.30 km/h and contribute to an approximate 40% reduction in speeding-related crashes. Besides, empirical application results showed that the whole framework contributed to a remarkable reduction in the fatality rate per 100 million km, from an average of 0.368 to 0.225. Finally, directions for future research in terms of data, counterfactual inference methodology, and research subjects have been discussed.
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Affiliation(s)
- Yin Zheng
- The Key Laboratory of Road and Traffic Engineering, Ministry of Education, 4800 Cao'an Road, 201804 Shanghai, China; College of Transportation Engineering, Tongji University, 4800 Cao'an Road, 201804 Shanghai, China; Didi Chuxing, Zuanshi Mansion, Zhongguancun Software Park Compound 19, Dongbeiwang Road, 100000 Beijing, China
| | - Xiang Wen
- Didi Chuxing, Zuanshi Mansion, Zhongguancun Software Park Compound 19, Dongbeiwang Road, 100000 Beijing, China
| | - Pengfei Cui
- Didi Chuxing, Zuanshi Mansion, Zhongguancun Software Park Compound 19, Dongbeiwang Road, 100000 Beijing, China
| | - Huanqiang Cao
- Didi Chuxing, Zuanshi Mansion, Zhongguancun Software Park Compound 19, Dongbeiwang Road, 100000 Beijing, China
| | - Hua Chai
- Didi Chuxing, Zuanshi Mansion, Zhongguancun Software Park Compound 19, Dongbeiwang Road, 100000 Beijing, China
| | - Runbo Hu
- Didi Chuxing, Zuanshi Mansion, Zhongguancun Software Park Compound 19, Dongbeiwang Road, 100000 Beijing, China
| | - Rongjie Yu
- The Key Laboratory of Road and Traffic Engineering, Ministry of Education, 4800 Cao'an Road, 201804 Shanghai, China; College of Transportation Engineering, Tongji University, 4800 Cao'an Road, 201804 Shanghai, China.
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Hassan A, Lee C, Cramer K, Lafreniere K. Analysis of driver characteristics, self-reported psychology measures and driving performance measures associated with aggressive driving. ACCIDENT; ANALYSIS AND PREVENTION 2023; 188:107097. [PMID: 37163853 DOI: 10.1016/j.aap.2023.107097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 03/31/2023] [Accepted: 04/29/2023] [Indexed: 05/12/2023]
Abstract
Whereas aggressive driving mainly causes speed-related crashes, aggressive driving may be reduced to improve road safety by identifying aggressive driving behaviour, aggressive drivers' characteristics, and their underlying motivational and psychological processes. Previous studies show that both driving performance and self-reported measures of aggressive driving are effective means to identify aggressive drivers. However, these studies assessed aggressive driving patterns across only a limited number of events, did not relate driver characteristics to aggressive driving in each event, and used chiefly vehicle kinematics variables (e.g., mean speed), but not vehicle dynamics variables (e.g., brake pedal force) which better capture driver reaction and decision-making. To address these limitations, this study assessed driver characteristics, self-reported psychological measures, and driving performance measures associated with aggressive driving among 55 drivers' behaviours in 9driving events using a driving simulator and survey responses. The results of structural equation models showed that unique aggressive driving patterns and driver characteristics related to aggressive driving vary among different driving events. As such, we recommend road safety policies to reduce aggressive driving based on the findings in this study.
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Affiliation(s)
- Ahmad Hassan
- Department of Civil and Environmental Engineering, University of Windsor, ON N9B 3P4, Canada.
| | - Chris Lee
- Department of Civil and Environmental Engineering, University of Windsor, ON N9B 3P4, Canada.
| | - Kenneth Cramer
- Department of Psychology, University of Windsor, ON N9B 3P4, Canada.
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Huang Y, Wang Y, Yan X, Li X, Duan K, Xue Q. Using a V2V- and V2I-based collision warning system to improve vehicle interaction at unsignalized intersections. JOURNAL OF SAFETY RESEARCH 2022; 83:282-293. [PMID: 36481019 DOI: 10.1016/j.jsr.2022.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 05/09/2022] [Accepted: 09/02/2022] [Indexed: 06/17/2023]
Abstract
INTRODUCTION Unsignalized intersections are critical components of the road network where traffic collisions occur frequently. METHOD This study aims to design a Vehicle-to-Vehicle (V2V)- and Vehicle-to-Infrastructure (V2I)-based unsignalized intersection collision warning system (UICWS) to improve driver performance and enhance driver safety at unsignalized intersections. A multi-user driving simulator experiment was conducted with 48 participants divided into 24 pairs. The dynamic interaction of each participant pair as they approached the intersection from straight-crossing directions was examined under different warning conditions (i.e., with vs without UICWS) and intersection field of view (IFOV) conditions (i.e., standard vs improved IFOV). RESULTS AND CONCLUSIONS The experimental results showed that the UICWS could effectively help drivers make appropriate operation decisions and reduce the number of right-angle collisions and near-collisions at unsignalized intersections. In the condition without UICWS, improved IFOV could prompt drivers to make crossing decisions in advance and adjust speed smoothly. Moreover, drivers' crossing maneuvers changed with the relative distance between the subject and conflict vehicles and the intersection. The risks of collisions and near-collisions increased significantly when the two vehicles were at a similar distance to the intersection before they initiated any actions. PRACTICAL APPLICATIONS The findings show that the proposed UICWS can effectively reduce collisions or near-collisions at unsignalized intersections in a connected vehicle environment. On this basis, some active intervention strategies, such as specific speed guidance depending on the dynamics of the conflict vehicle, can be developed to ensure vehicles passing through unsignalised intersections safely.
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Affiliation(s)
- Yan Huang
- MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
| | - Yun Wang
- MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
| | - Xuedong Yan
- MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
| | - Xiaomeng Li
- Queensland University of Technology (QUT), Centre for Accident Research and Road Safety-Queensland (CARRS-Q), Kelvin Grove, QLD 4059, Australia
| | - Ke Duan
- MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
| | - Qingwan Xue
- Beijing Key Laboratory of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China
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Das T, Shoaib Samandar M, Rouphail N. Longitudinal traffic conflict analysis of autonomous and traditional vehicle platoons in field tests via surrogate safety measures. ACCIDENT; ANALYSIS AND PREVENTION 2022; 177:106822. [PMID: 36103759 DOI: 10.1016/j.aap.2022.106822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 08/04/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
Autonomous vehicles (AVs) have been introduced into the traffic stream alongside traditional vehicles (TVs) with the expectation of improved transportation safety, efficiency, and reliability. The majority of AV safety research has been done through simulation. The results of such research on the safety performances of AVs are heavily influenced by the methodological framework, algorithms, and assumptions about AV driving characteristics in a simulated environment. There is a need for AV safety research based on real-world settings before any wide-scale deployment of this technology. This paper investigates the impact of the presence of SAE level 2 AVs in the traffic stream in reducing longitudinal traffic conflicts using Surrogate Safety Measures on a real-world open-source database of mixed traffic trajectories. The analysis is conducted for both AV-exclusive and mixed AV-TV platoons. Furthermore, we explore whether the presence of AVs decreases longitudinal traffic conflicts in two-vehicle platoons comprising AV and TV mixed leaders and followers. We find that an exclusive AV platoon behaves similarly to an exclusive TV platoon and produces similar longitudinal conflicts. However, mixed platoons with both AVs and TVs result in a higher number of longitudinal conflicts. Maintaining near-identical leader-follower conditions, we find that the number of conflicts in mixed platoons when an AV follows a TV is higher than when a TV follows an AV. The increase in conflict numbers in a TV-AV mixed platoon can be attributed to AV's longer response time lag. In summary, analyses conducted in this paper indicate that exclusive platoons and pairs of vehicles exhibit fewer longitudinal conflicts than mixed platoons and pairs.
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Affiliation(s)
- Tanmay Das
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, United States.
| | - M Shoaib Samandar
- Institute of Transportation Research and Education (ITRE), Research IV, 909 Capability Dr, Raleigh, NC 27606, United States
| | - Nagui Rouphail
- Distinguished University Professor Emeritus, Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, United States
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Application of Machine Learning in Ethical Design of Autonomous Driving Crash Algorithms. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2938011. [PMID: 36248938 PMCID: PMC9553442 DOI: 10.1155/2022/2938011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/07/2022] [Accepted: 09/12/2022] [Indexed: 01/09/2023]
Abstract
The age of algorithms is here, and it is really changing people's lives. More and more ethical problems related to algorithms have attracted people's attention, but the related ethical research is still far behind the research of algorithms. As more intelligent algorithms emerge in an endless stream, there will also be a lot of algorithmic ethical issues. On the other hand, with the continuous improvement of the development level of the automobile industry, people have a stronger demand for the safety and stability of modern transportation, and more and more autonomous driving technology has been promoted and applied in the market. At present, most of the studies on the longitudinal collision avoidance system of vehicles use collision warning or emergency braking to avoid collision. However, when the vehicle is in a special situation such as high speed and slippery road, emergency steering is more effective. In order to further improve the vehicle safety and ethical algorithm design points, this article revolves around vehicle lateral active collision avoidance control method research, the collision avoidance decision-making, and path planning and collision avoidance transverse vehicle longitudinal motion control is analyzed, and based on automated driving simulation experiment, the tests carried out to verify the designed control strategy. The experimental results show that the proposed method not only has a good effect of preventing automatic driving collision but also can meet the requirements of algorithm ethics. This research can effectively guide the research of algorithmic ethics in the field of autonomous driving and effectively reduce the occurrence of traffic accidents.
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Leveraging UAV Capabilities for Vehicle Tracking and Collision Risk Assessment at Road Intersections. SUSTAINABILITY 2022. [DOI: 10.3390/su14074034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Transportation agencies continue to pursue crash reduction. Initiatives include the design of safer facilities, promotion of safe behaviors, and assessments of collision risk as a precursor to the identification of proactive countermeasures. Collision risk assessment includes reliable prediction of vehicle trajectories. Unfortunately, in using traditional tracking equipment, such prediction can be impaired by occlusion. It has been suggested in recent literature that unmanned aerial vehicles (UAVs) can be deployed to address this issue successfully, given their wide visual field and movement flexibility. This paper presents a methodology that integrates UAVs to track the movement of road users and to assess potential collisions at intersections. The proposed methodology includes an existing deep-learning-based algorithm to identify road users, extract trajectories, and calculate collision risk. The methodology was applied using a case study, and the results show that the methodology can provide beneficial information for the purpose of measuring and analyzing the infrastructure performance. Based on vehicle movements it observes, the UAV can communicate its collision risk to each vehicle so that the vehicle can undertake proactive driving decisions. Finally, the proposed framework can serve as a valuable tool for urban road agencies to develop measures to reduce crash risks.
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A Simulator for Educating the Digital Technologies Skills in Industry. Part One. Dynamic Simulation of Technological Processes. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112210885] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Digital technology is being introduced into all areas of human activity. However, there are a number of challenges in implementing these technologies. These include the delayed return on investment, the lack of visibility for decision-makers and, most importantly, the lack of human capacity to develop and implement digital technologies. Therefore, creating a digital training simulator for the industry is an actual task. This paper focuses on the first step in creating a digital training simulator for the industry: developing a dynamic process model. The process chosen is flotation, as it is one of the most common mineral processing methods. The simulation was performed in AVEVA Dynamic Simulation software. The model is based on a determination of reaction rate constants, for which, experiments were conducted on a laboratory pneumomechanical flotation machine with a bottom drive. The resulting model was scaled up to industrial size and its dynamic properties were investigated. In addition, the basic scheme of a computer simulator was considered, and the testing of the communication channels of a dynamic model with systems, equipment and software for digitalizing was conducted. The developed model showed acceptable results for its intended purpose, namely, an exact match to the technological process in terms of time. This helps to account for inertia and a fast response on all tested communication channels, as well as being acceptable for the real-time simulation speed of the solver.
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