Safety Of Autonomous Vehicles Research Articles

Investigation of Object Detection and Identification at Different Lighting Conditions for Autonomous Vehicle Application

Ensuring the safety of autonomous vehicles requires effective detection and tracking of surrounding objects. This paper proposes the design and development of a driverless transportation system module focused on identifying obstacles around vehicles. By integrating computer vision with deep learning, the system presents a reliable and cost-effective solution for autonomous driving. Utilizing Raspberry Pi 4B and a USB webcam, a compact hardware setup is created for seamless implementation in autonomous vehicles. The algorithm presented in this study enables the detection, classification, and tracking of both moving and stationary objects, including cars, buses, trucks, people, and motorcycles. TensorFlow Lite, a deep-learning network, is employed for efficient object detection and classification. Leveraging Python as the primary programming language, known for its high-level object-oriented features and integrated semantics, the algorithm is tailored for web and application development. Experimental results demonstrate the system’s capability to concurrently detect and identify multiple local objects with an accuracy ranging from 50% to 80% in day and night conditions. These findings underscore the potential of deep learning in advancing autonomous vehicle technology.

시나리오 분석 및 위험도 평가에 의한 자율주행자동차 검증방법

In this study, we propose an autonomous vehicle safety validation method to address the limitations of existing vehicle safety validation methods. Using scenario analysis and risk assessment of the autonomous vehicle safety validation scenario, the proposed method determines the significance of scenario validation, difficulty of scenario implementation, and critical nature of accidents that occur during validation testing. Then, it calculates the autonomous vehicle test level based on these results. Various safety validation tests, such as audits, simulation tests, proving ground tests, and real road tests, were performed according to the calculated level. Based on the validation results, the proposed method enables authorities to request improvement in the original equipment manufacturer address areas and is expected to contribute to providing safe autonomous vehicles to consumers.

Driving Decision Making of Autonomous Vehicle According to Queensland Overtaking Traffic Rules

Improving the safety of autonomous vehicles (AVs) by making driving decisions in accordance with traffic rules is a complex task. Traffic rules are often expressed in a way that allows for interpretation and exceptions, making it difficult for AVs to follow them. This paper proposes a novel methodology for driving decision making in AVs based on defeasible deontic logic (DDL). We use DDL to formalize traffic rules and facilitate automated reasoning, allowing for the effective handling of rule exceptions and the resolution of vague terms in rules. To supplement the information provided by traffic rules, we incorporate an ontology for AV driving behaviour and environment information. By applying automated reasoning to formalized traffic rules and ontology-based AV driving information, our methodology enables AVs to make driving decisions in accordance with traffic rules. We present a case study focussing on the overtaking traffic rule to illustrate the usefulness of our methodology. Our evaluation demonstrates the effectiveness of the proposed driving decision-making methodology, highlighting its potential to improve the safety of AVs on the road.

Uncertainties in Onboard Algorithms for Autonomous Vehicles: Challenges, Mitigation, and Perspectives

Autonomous driving is considered one of the revolutionary technologies shaping humanity’s future mobility and quality of life. However, safety remains a critical hurdle in the way of commercialization and widespread deployment of autonomous vehicles on public roads. Safety concerns require the autonomous driving system to handle uncertainties from multiple sources that are either preexisting, e.g., the stochastic behavior of traffic participants or scenario occlusion, or introduced as a result of processing, e.g., the application of neural networks. Thus, it is crucial to analyze the sources of uncertainties and quantify the risks associated with them, including the propagated risks that accumulate in the decision-making system. In this context, this paper provides an overview of uncertainty challenges and state-of-the-art techniques for mitigating these challenges. We argue that the uncertainties mainly originate from two aspects: 1) the external traffic environment, and 2) the internal autonomous driving system. Specifically, this paper first analyzes the safety challenges caused by the uncertainties and summarizes their sources. In addition, the corresponding techniques that mitigate and quantify the risk of uncertainties are presented. Finally, research perspectives are highlighted to facilitate future studies for guaranteeing the safety of autonomous vehicles.

Drivable Road Region Segmentation in Real Time with High Precision using Deep Learning

Abstract: This paper presents a novel approach that addresses the challenging task of real-time drivable road region extraction in computer vision. Semantic segmentation, which involves accurately identifying and segmenting objects in real-time data, is a complex problem. However, deep learning has proven to be a powerful technique for achieving semantic segmentation by automatically identifying patterns without the need for explicit programming. To tackle this task, the paper proposes a fusion of the YOLO algorithm and UNET architecture, leveraging their respective strengths. The YOLO algorithm enables high-speed object detection, while the UNET architecture provides advantages in global location utilization, contextual understanding, and performance, even with limited training samples. Importantly, the proposed method is lightweight, making it suitable for deployment on embedded systems with limited computational power. To optimize memory usage and capture context at different scales, the system employs dilated convolutions for efficient feature extraction. The algorithm exhibits exceptional performance in accurately segmenting irregular objects and handles diverse input data types, including images and videos, in real-time. Overall, this paper contributes significantly to the advancement of computer vision technologies and offers a valuable solution for real-time drivable road region extraction. Its potential applications include addressing driving challenges and enhancing safety in autonomous vehicles and intelligent transportation systems.

Safety and Theft Detection in Autonomous Vehicles

Abstract: The proposed work revolves around bolstering automotive cybersecurity and safety through a comprehensive strategy that encompasses various technological facets. The workflow commences with an advanced accident detection mechanism, facilitated by a set of IoT components such as bump switches, buzzers, ESP32 modules, switches, and device sensors. Upon detecting an accident, these components collaborate to promptly notify relevant entities, potentially minimizing the aftermath of collisions. Furthermore, the incident data is securely stored in the cloud, ensuring its availability for subsequent analysis and response coordination. The crux of this work lies in its application of machine learning algorithms—K-Means and Support Vector Machine (SVM) to tackle cybersecurity challenges. These algorithms undertake the vital task of classifying different types of cyber attacks. By learning patterns from labelled datasets, the algorithms become adept at identifying and categorizing various cyber threats. This classification empowers the system to swiftly recognize and respond to potential attacks, mitigating potential damages and risks to the vehicle's security. Another noteworthy feature of this work is its proactive stance against theft. Through a network of IoT components including switches and sensors, the system is primed to detect any unauthorized tampering or theft of vehicle parts. In such instances, the system triggers immediate notifications to relevant parties, thereby facilitating swift intervention and recovery measures. This real-time response capability greatly enhances the security of the vehicle and its components.

Cumulatively Anticipative Car-Following Model with Enhanced Safety for Autonomous Vehicles in Mixed Driver Environments

The contribution of autonomous vehicles to traffic is one of the key aspects of future ground transportation in smart cities. Autonomous vehicles are able to provide many benefits, but some benefits can only provide advantages if these vehicles comprise a large percent of on the road/driven vehicles, which may take decades. Until then, the robotic drivers in autonomous vehicles will share the same road system with human divers in a mixed-driver environment where the majority of road accidents for autonomous vehicles are associated with the operational inconsistency of human drivers. In this paper, a cumulatively anticipative car-following model (which considers cumulative influences from multiple preceding vehicles) is developed to potentially improve the safety of autonomous vehicles in mixed-driver environments that benefit from enhanced communication between the autonomous vehicles and other components in the transportation system. Through intensive simulations (200 simulations), this study comprehensively evaluates four models including the cumulative anticipative car-following model, the Wiedemann 99 model, adaptive cruise control, and the cooperative adaptive cruise control model. Across 10 scenarios and five speed limits (24.59–33.53 m/s), the cumulative anticipative car-following model consistently demonstrates superior conflict reduction, with average, maximum, and minimum conflict percentages ranging from 77.69% to 91.97% against the Wiedemann 99 model, 67.00% to 93.94% against the adaptive cruise control model, and 69.17% to 93.25% against the cooperative adaptive cruise control model. Notably, the cooperative adaptive cruise control model exhibits suboptimal performance, especially in mixed-driver settings. The cumulative anticipative car-following model also enhances vehicle mobility, reducing average stops by up to 93.54%, 91.74%, 92.04%, 88.60%, and 91.35% in comparison to the other three models at speeds of 24.59 m/s, 26.82 m/s, 29.06 m/s, 31.29 m/s, and 33.53 m/s. Overall, the cumulative anticipative car-following model holds significant potential for conflict reduction and traffic enhancement.

Research on Lane-Change Decision and Planning in Multilane Expressway Scenarios for Autonomous Vehicles

Taking into account the issues faced by self-driving vehicles in multilane expressway scenarios, a lane-change decision planning framework that considers two adjacent lanes is proposed. Based on this framework, the lateral stability of an autonomous vehicle under near-limit conditions during lane change is studied by the phase-plane method. Firstly, a state-machine-based driving logic is designed and a decision method is proposed to design the lane-change intention based on the surrounding traffic information and to consider the influence of the motion state of other vehicles in the adjacent lanes on the self-driving vehicle. In order to realize adaptive cruising under the full working conditions of the vehicle, a safety distance model is established for different driving speeds and switching strategies for fixed-speed cruising, following driving, and emergency braking are developed. Secondly, for the trajectory planning problem, a lane-change trajectory based on a quintuple polynomial optimization method is proposed. Then, the vehicle lateral stability boundary is investigated; the stability boundary and rollover boundary are incorporated into the designed path-tracking controller to improve the tracking accuracy while enhancing the rollover prevention capability. Finally, a simulation analysis is carried out through a joint simulation platform; the simulation results show that the proposed method can ensure the driving safety of autonomous vehicles in a multilane scenario.

Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions

Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophisticated technologies, including sensors, cameras, GPS, radar, light imaging detection and ranging (LiDAR), and advanced computing systems. These components work in concert to accurately perceive the vehicle’s environment, ensuring the capacity to make optimal decisions in real-time. At the heart of AV functionality lies the ability to facilitate intercommunication between vehicles and with critical road infrastructure—a characteristic that, while central to their efficacy, also renders them susceptible to cyber threats. The potential infiltration of these communication channels poses a severe threat, enabling the possibility of personal information theft or the introduction of malicious software that could compromise vehicle safety. This paper offers a comprehensive exploration of the current state of AV technology, particularly examining the intersection of autonomous vehicles and emotional intelligence. We delve into an extensive analysis of recent research on safety lapses and security vulnerabilities in autonomous vehicles, placing specific emphasis on the different types of cyber attacks to which they are susceptible. We further explore the various security solutions that have been proposed and implemented to address these threats. The discussion not only provides an overview of the existing challenges but also presents a pathway toward future research directions. This includes potential advancements in the AV field, the continued refinement of safety measures, and the development of more robust, resilient security mechanisms. Ultimately, this paper seeks to contribute to a deeper understanding of the safety and security landscape of autonomous vehicles, fostering discourse on the intricate balance between technological advancement and security in this rapidly evolving field.

Investigating collision patterns to support autonomous driving safety

ABSTRACT There is a debate on the importance of autonomous vehicles (AVs) and the methods for ensuring AV safety. This paper analyses collision reports to determine the association between risk factors and the level of damage to an AV due to collisions. Association rule mining was used to develop methodologies that can advance result interpretability, which is crucial in the transportation field. Twenty-one rules were discovered to reveal the factors that co-occur with AV damage. This study demonstrates that collision data, when analysed using appropriate machine learning algorithms, can generate useful insights that complement current policies to enhance AV safety.

Identifying typical pre-crash scenarios based on in-depth crash data with deep embedded clustering for autonomous vehicle safety testing

Choosing appropriate scenarios is critical for autonomous vehicles (AVs) safety testing. Real-world crash scenarios can be used as critical scenarios to test the safety performance of AVs. As one of the dominant types of traffic crashes, the car to powered-two-wheelers (PTWs) crash results in a higher possibility of fatality than ordinary car-to-car crashes. Generally, typical testing scenarios are chosen according to the subjective understanding of the safety experts with limited static features of crashes (e.g., geometric features, weather). This study introduced a novel method to identify typical car-to-PTWs crash scenarios based on real-world crashes with dynamic pre-crash features investigated from the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database. First, we present crash data collection and construction methods of the CIMSS-TA database to construct testing scenarios. Second, the stacked autoencoder methods are used to learn and obtain embedded features from the high-dimensional data. Third, the extracted features are clustered using k-means clustering algorithm, and then the clustering results are interpreted. Six typical car-to-PTWs scenarios are obtained with the proposed processes. This study introduces a typical high-risk scenario construction method based on deep embedded clustering. Unlike existing researches, the proposed method eliminates the negative impacts of manually selecting clustering variables and provides a more detailed scenario description. As a result, the typical scenarios obtained from AV testing are more robust.

DNN-Based Estimation for Misalignment State of Automotive Radar Sensor.

The reliability and safety of advanced driver assistance systems and autonomous vehicles are highly dependent on the accuracy of automotive sensors such as radar, lidar, and camera. However, these sensors can be misaligned compared to the initial installation state due to external shocks, and it can cause deterioration of their performance. In the case of the radar sensor, when the mounting angle is distorted and the sensor tilt toward the ground or sky, the sensing performance deteriorates significantly. Therefore, to guarantee stable detection performance of the sensors and driver safety, a method for determining the misalignment of these sensors is required. In this paper, we propose a method for estimating the vertical tilt angle of the radar sensor using a deep neural network (DNN) classifier. Using the proposed method, the mounting state of the radar can be easily estimated without physically removing the bumper. First, to identify the characteristics of the received signal according to the radar misalignment states, radar data are obtained at various tilt angles and distances. Then, we extract range profiles from the received signals and design a DNN-based estimator using the profiles as input. The proposed angle estimator determines the tilt angle of the radar sensor regardless of the measured distance. The average estimation accuracy of the proposed DNN-based classifier is over 99.08%. Therefore, through the proposed method of indirectly determining the radar misalignment, maintenance of the vehicle radar sensor can be easily performed.

Safety risk assessment for autonomous vehicle road testing

Objective Road testing can accelerate the development and validation of autonomous vehicles (AVs). AV road testing can come with high safety risks, particularly in a complex road traffic environment, due to the immaturity of AV technology. A priori safety risk assessments of the road traffic environment before AV road testing are of great importance, allow the quantifying of risk levels in different road scenarios, and provide guidelines for AV road testing in low to high-risk environments. Methods This study proposes a framework, namely Safety Risk Assessment for AV road testing (SRAAV), based on the probability and severity of five categories of potential AV accidents. Four groups of influencing factors are considered comprehensively in assessing AV safety risk, and their impacts are quantified using impact coefficients derived from a Bayesian network and empirical AV road testing data. The safety risk is assessed on a road section level, based on which an overall risk level is defined for a corridor and a region. Afterwards, the quantified safety risk is classified into four levels according to expert experience and knowledge, through a questionnaire survey. Results Applications of the proposed SRAAV framework are conducted for urban roads in Shanghai, and expressways in Shanghai and Gothenburg. The assessment results are validated using disengagement data from AV road testing. The results show that the SRAAV framework and its models could estimate the safety risk levels of road traffic environments for AV road testing in a sound way and have the flexibility for further extensions to be made. Conclusions The framework and assessment results can provide technical support for determining where and when to grant permission for public roads to be used for AV road testing, and how to choose public roads from a low to a high risk level, guaranteeing the safety of AV public road testing.

An exploration of the preferences and mode choice behavior between autonomous demand-responsive transit and traditional buses

With the advancement in autonomous driving techniques, autonomous demand-responsive transit (ADRT) is a newly emerging sustainable transport mode for the future, which will provide more flexible services to public users. ADRT offers benefits such as flexible stops and routes and comfortable seats, but it also involves risks due to the vehicles being driverless. This paper particularly investigates users’ preferences and attitudes towards ADRT, and mode choice behavior between ADRT buses and traditional buses. A survey with Likert scale statements and stated preference (SP) choice scenarios is designed and conducted to explore users’ attitudes towards the safety risks of autonomous vehicles (AVs), social concerns, service flexibility concerns when using AVs, interest in new things, and shuttle mode choices. An integrated choice and latent variable (ICLV) model is adopted to explore users’ psychological factors through latent variables and to integrate them into mode choice behavioral modeling. Estimated results indicate that users’ attitudes towards AV safety risks, their social concerns, and their flexibility concerns with ADRT strongly influence their mode choices and are strongly related to sociodemographic and travel-related factors such as age, gender, income, education, number of family members. In general, a young age, a high education level, a higher income, private car ownership, and better knowledge of AVs are positively related to attitudes towards ADRT. Females, users from large families, and users with driving licenses or long commuting times are less willing to adopt ADRT. The study's outcomes highlight significant heterogeneities among users and can be highly valuable for policymakers, such as government authorities, in providing social support and designing policies targeting specific population groups. This will be beneficial in attracting more users to this emerging mobility service and contributing to sustainable urban development.

Influence of non-driving related tasks on driving performance after takeover transition in conditionally automated driving

In conditionally automated driving, drivers are required to respond to takeover requests (TORs) and resume manual driving of the vehicle in situations where the conditionally automated driving systems are ineffective. Prior to TORs, the driver may be engaged in Non-Driving Related Tasks (NDRTs), and their manual driving performance after the takeover transition (post-automation) may require time to return to normal.This study investigated the influences of NDRTs on manual driving performance during the post-automation period. Seventeen volunteers participated in this driving simulator study. There were three NDRTs: writing business emails (working condition), watching videos (entertaining condition), and taking a break with eyes closed (resting condition). The duration of engagement in each NDRT before resuming manual driving was either 5 min (short interval) or 30 min (long interval). When TORs were made, drivers were given 10 s (TOR lead time) to switch from an NDRT to manual driving and then continue driving on a straight highway for 5 min.The results demonstrated that driving performance was impaired during the post-automation period. A significant detrimental effect on driving performance was observed for all three NDRT conditions and both task engagement durations. This effect was particularly evident for lane control, where drivers on average spent 4–8 s per minute outside of their lane for each of the five minutes following the TOR. These results indicate that driver engagement in other tasks, even for brief periods, can increase accident risk during the minutes following a TOR. Analysis of individual driving performance revealed a subset of 5 drivers who were strongly impaired, spending 10–25 s per minute outside of their lane throughout the post-automation period. These unsafe drivers could be accurately identified from their driving behaviour during the pre-automation (control) period. Surprisingly, participants were unaware that their degraded driving performance following the takeover. These findings extend the understanding of the disruptive effect of cognitive set-switching while driving and have important implications for the design and safety of autonomous vehicles.

RTA-IR: A runtime assurance framework for behavior planning based on imitation learning and responsibility-sensitive safety model

Current research on artificial intelligence (AI) algorithms in safety–critical areas remains extremely challenging due to their inability to be fully verified at design time. In this paper, we propose an RTA-IR architecture, which bypasses the formal verification of the AI algorithm by incorporating runtime assurance (RTA) and provides safety assurances for the AI controllers of complex autonomous vehicles (such as those obtained using neural networks) without excessive performance sacrifice. RTA-IR consists of a high-performance and unproven advanced controller and two verifiable safety controllers and a decision module designed based on the Responsibility Sensitive Safety Model (RSS). The advanced controller is designed based on attention generating adversarial imitation learning(GAIL), which can imitate the efficient policies of experts from a set of expert demonstrations. RSS provides verifiable safety criteria and switching logic for the decision module, and RTA-IR provides safety for autonomous vehicles when the advanced controller produces unsafe control, as well as restoring control of the vehicle by the advanced controller under conditions that confirm safety. We tested and evaluated RTA-IR separately for two levels of traffic density in one driving task. Experiments have shown that RTA-IR exhibits superior performance in terms of both safety and efficiency compared to the baseline method.

Evaluating perceived safety of autonomous vehicle: The influence of privacy and cybersecurity to cognitive and emotional safety

The autonomous vehicle (AV) is predicted to reduce the number of accidents and fatalities on the roads caused by human-related error and lessen traffic congestion caused by stop-and-go behavior. The susceptibility of autonomous vehicles (AVs) to potential hacking and data exploitation has generated significant concerns regarding cybersecurity and privacy risks within the domain of perceived safety. This study aimed to empirically test a comprehensive model of perceived safety in AVs, incorporating cognitive safety, emotional safety, and privacy cybersecurity. The responses of 466 participants were analyzed using a structural equation modeling (SEM) approach. The findings indicated that a significant majority of the respondents expressed their intention to utilize high-level autonomous vehicles (AVs) in the future. Specifically, 31.1% of the participants expressed an intention to use a level 2 AV, while 8.8% indicated their preference for a level 5 AV. In terms of perceived safety, privacy cybersecurity emerged as the most influential predictor, followed by emotional safety and cognitive safety. The analysis of causal relationships between the variables further revealed that privacy cybersecurity had the greatest impact on both emotional safety and cognitive safety, highlighting its critical role in shaping the overall perception of safety in AVs. Finally, this study can provide insight into how the drivers perceives AVs safety, which can be useful for government organizations, transportation agencies, and AV developers in shaping AV safety.

Group trust dynamics during a risky driving experience in a Tesla Model X.

The growing concern about the risk and safety of autonomous vehicles (AVs) has made it vital to understand driver trust and behavior when operating AVs. While research has uncovered human factors and design issues based on individual driver performance, there remains a lack of insight into how trust in automation evolves in groups of people who face risk and uncertainty while traveling in AVs. To this end, we conducted a naturalistic experiment with groups of participants who were encouraged to engage in conversation while riding a Tesla Model X on campus roads. Our methodology was uniquely suited to uncover these issues through naturalistic interaction by groups in the face of a risky driving context. Conversations were analyzed, revealing several themes pertaining to trust in automation: (1) collective risk perception, (2) experimenting with automation, (3) group sense-making, (4) human-automation interaction issues, and (5) benefits of automation. Our findings highlight the untested and experimental nature of AVs and confirm serious concerns about the safety and readiness of this technology for on-road use. The process of determining appropriate trust and reliance in AVs will therefore be essential for drivers and passengers to ensure the safe use of this experimental and continuously changing technology. Revealing insights into social group-vehicle interaction, our results speak to the potential dangers and ethical challenges with AVs as well as provide theoretical insights on group trust processes with advanced technology.

Neural‐network‐based event‐triggered adaptive security path following control of autonomous ground vehicles subject to abnormal actuator signal

AbstractThe malicious physical attacks from both sensor and actuator sides make real threats to the security and safety of autonomous ground vehicles (AGVs). This paper focuses on the problem of neural‐network‐based event‐triggered adaptive security control (ET‐ASC) scheme for path following of AGVs subject to arbitrary abnormal actuator signal. First, we assume that an arbitrary abnormal signal is caused by arbitrary malicious attacks or disturbances from actuators. Then, radial basis function neural network (RBF‐NN) is used to reconstruct such abnormal actuator signal. Second, modelling issues on security path following control of AGVs with Sigmoid‐like ETC scheme are shown when the AGV is suffering from abnormal actuator signal. In what follows, an ET‐ASC scheme is developed to mitigate the adverse effects of abnormal actuator signal with the reconstructed abnormal signal based on a novel Sigmoid‐like event‐triggered communication scheme. By using the proposed RBF‐NN‐based ET‐ASC scheme, control performance can be guaranteed under arbitrary malicious actuator signal rather than such attacks following a specific probability distribution. Final, some simulation experiments are provided to verify the effectiveness of proposed ET‐ASC scheme.

Discovering latent topics and trends in autonomous vehicle-related research: A structural topic modelling approach

Autonomous Vehicle (AV) technology is a disruptive transportation technology that promises to revolutionize how people travel. Due to their potential mobility benefits and associated impacts on public health and the environment, they have received massive attention from transportation experts. To date, there have been numerous academic research conducted regarding AVs, and many attempts have been made to summarize these research documents by manually reviewing them. Nevertheless, due to the vast nature of the existing literature, it is challenging to synthesize the themes in AV-related research and to explore their trends to inform future research direction. This study aims to utilize an advanced natural language processing technique to provide a comprehensive overview of the recent developments in AV-related research. The abstracts of 3292 articles published in transportation-based journals from January 2016 to October 2022 were collected and used for this study. A Structural Topic Model (STM) was employed to explore the dominant research themes hidden in the extant literature on AV research, examine their evolution over time, and determine the topics highly associated with developing and developed economies. Overall, the study highlighted that the least common themes in the literature are in the areas of the development of complex vehicle designs, safety, environmental benefits, and the impact of AVs on transportation infrastructure. Nevertheless, themes concerning AV safety and the environment are emerging as hot topics since late 2021. Topics related to investigating user perceptions, policies/approaches to increase user demand, and AV control/stability are highly studied. Besides, they predominantly originated from developed countries. The research themes, their associated trends, and the key policy suggestions drawn from the study are potentially useful for making well-informed research funding decisions, setting research priorities for countries, and guiding the future research endeavours of researchers.