Q2: Which methods and approaches are used to characterize safety in autonomous vehicles from a software point of view?
Based on the analysis of the selected articles, a comprehensive classification framework for the presented methodologies can be proposed as follows.
The initial category pertains to solutions that approach autonomous vehicles through the lens of software design and diverse system layers. This category introduces a suggested architecture encompassing all elements delineated within the independent driving domain. The subsequent type adopts a parametric perspective, leveraging distinctions in environmental and design parameters expounded upon in the preceding section. This approach is equipped to tackle challenges intrinsic to the system and software by leveraging mathematical models. Another subset delves deeper by addressing components pivotal to error management within self-driving systems. These contributions furnish solutions geared towards enhancing the fault tolerance of autonomous systems, consequently elevating the safety levels in self-driving vehicles. In parallel, a distinct group focuses on managing accidents across varied routes, proffering solutions centered around traffic management within the context of self-driving vehicles.
Safety in vehicles has different aspects. One of these involves traffic management and the routing of vehicles, which must be observed in all vehicles, including classic, self-driving, and connected, and controls the occurrence of accidents. On the other hand, for self-driving vehicles, it is necessary to consider all environmental and non-environmental parameters in the design and specify the design of their components. On the other hand, if a fault occurs in the vehicle’s control systems, there must be management or fault tolerance in place to avoid accidents. Therefore, we categorize different solutions along this direction.
5.1.1. Safety-Based Architectures and Frameworks
As indicated in the preceding section, the fundamental blueprint of autonomous vehicles diverges from traditional vehicles, encompassing an array of distinctive components. Sensors emerge as pivotal components as they supply the requisite data for the operation and decision-making of autonomous vehicles. Concurrently, data exchange and seamless network communication are imperative prerequisites for facilitating effective interconnectivity within this paradigm. Considering these dynamics, a holistic exploration of comprehensive solutions spanning all vehicle components becomes paramount.
Furthermore, many methodologies extend their scope to encompass environmental factors and other network constituents. This section serves as a platform for dissecting methods and architectures that encapsulate the full spectrum of architectural attributes inherent to the entire system.
Upon scrutinizing the pivotal aspects that underscore establishing a secure architecture for autonomous vehicles, a convergence of common concerns and limitations becomes apparent [
29]. Several critical problems emerge when addressing the establishment of a robust architecture for autonomous vehicles. These include the following:
Heterogeneous System Structure: The inherent heterogeneity within the system structure is a paramount consideration. Given the diversity in technologies and communication standards across vehicles, a comprehensive solution must be able to effectively manage this diversity. Clarity in addressing this issue is crucial when defining any proposed solution.
Network-Level Data Exchange: The data exchange at the network level introduces the challenge of network standardization, communication capacity, and reliability. Effective data exchange is pivotal, as failure in this realm can exacerbate the occurrence of accidents and hinder the overarching goal of intelligent vehicle design.
Comprehensive Component Coverage: In the architectural design of vehicles, it is essential to account for the roles played by all system components. A holistic perspective should be maintained to ensure that all facets of the system are adequately considered.
Impact of Vehicle Movement and Routing: The movement, positioning, and routing of vehicles significantly influence the potential for accidents either involving other vehicles or mobile/static environmental obstacles. These factors must be factored into the architectural design.
Control Solutions: Control solutions are imperative for individual and holistic system components. Effective control mechanisms ensure optimal operation and coordination within the autonomous vehicle ecosystem.
Urgency and Reactivity: The agility of guarantees and the ability to respond swiftly to unforeseen events and accidents are paramount. The architecture of a vehicle must facilitate rapid responses to ensure safety in dynamic situations.
Addressing these multifaceted concerns in designing and implementing an autonomous vehicle’s architecture is pivotal for fostering a safe and efficient self-driving ecosystem. Considering the issues above, the architectural articles within the field can be categorized in several ways. Some articles identify critical architectural design criteria while delving into the intricacies of hazards and necessities that are inherent to a secure architectural design. Comparable to the architecture of other complex systems, specific articles centered on vehicle architecture design adopt a systematic approach. They encompass requirement gathering, risk assessing to ensure safety, and the extracting of pivotal and generic attributes to prevent accidents. For instance, in [
30], the authors delved into the domain of safety management across various hierarchical levels. Their methodology involved leveraging augmented intelligence to construct safety models rooted in data characteristics. This approach guaranteed data quality and reliability, critical factors underpinning a robust safety architecture.
Another contribution, showcased in [
31], is risk management. The article employed advanced reinforcement learning techniques to handle risk management patterns associated with vehicle safety breaches. Decisions made within autonomous vehicles hinge on a risk model, whereby actions are determined based on minimizing risk. Notably, the complexity of algorithms and the allocation of resources at the network level are optimized to accommodate the limited processing power inherent to vehicles. This optimization contributes to an enhanced safety profile. Unforeseen environmental events fail to receive mention, and specific parameters derived from this context are excluded from the pattern extraction process.
Khan et al. [
32] contributed a conceptual model aimed at dissecting the risks intertwined with cybersecurity and, in the process, enhancing overall security and safety. This publication introduced various factors crucial for modeling the system’s evolution over time. This approach considered multiple elements, including human factors and regulatory guidelines for overseeing vehicle performance management. Given the undeniable significance of these parameters in safety management, security control, and cyber challenges at these strata, the model can markedly alleviate vehicle safety management. Nonetheless, it is pertinent to highlight a primary limitation evident within this work.
Within the array of approaches, a subgroup adopts a process-based methodology. This category endeavors to encapsulate the entirety of the autonomous vehicle system, encompassing facets ranging from vehicle motion to the intricacies of traffic management. At the level of individual routes, interactions spanning autonomous vehicles and their environmental counterparts, including automated and traditional vehicles, pedestrians, and workstations, are meticulously modeled, as evidenced in [
33]. This particular work introduced a multi-step framework tailored for traffic management. This framework is predicated on distilled requirements, thereby facilitating the execution of traffic modeling. By meticulously evaluating the parameters affiliated with each vehicle, the system is equipped to issue warnings in the presence of potential safety hazards. This framework also serves as a platform for simulating various vehicle communication scenarios, incorporating diverse communication protocols. The underlying implication is that real-world issues within the system can be effectively addressed by identifying and resolving commonplace methods.
On a different note, some researchers have concentrated on architectural designs rooted in component and environmental prerequisites. However, their coverage has not permeated all system layers nor delved into software design as extensively. This theme is encapsulated by [
34], which is centered on vehicle communication and networking. Notably, cryptographic algorithms have been developed as part of this work to safeguard individual privacy while concurrently thwarting attacks and preserving the integrity of information. The authors grapple with a delicate balance between security and privacy concerns. Notably, this paper is underpinned by the adoption of the 5G communication protocol. As a result, one of the inherent drawbacks of this research is its communication protocol limitations. Factors such as network capacity, message transmission and reception delays, and network architecture warrant consideration when selecting encryption methods. A comparative analysis encompassing encrypted and unencrypted approaches was executed, ultimately favoring the proposed plan due to its heightened security. This shift corresponded with an increase in the correlation coefficient of the transmitted messages, consequently bolstering their overall security efficacy.
In article [
35], the 5G standard is positioned as a communication system standard, albeit with limitations that curtail its real-world applicability. Nonetheless, it does exhibit advancements when juxtaposed with the preceding methodologies. Notably, this approach operates effectively in the context of diverse traffic types. Moreover, the criteria encompass multifaceted aspects such as vehicular mobility, geographic positioning, and the imperative of safeguarding data privacy. The delineation of architectural constraints and associated parameters encompass a spectrum of limitations, spanning communication boundaries, resource allocations, and the dissemination of informational messages. However, it is crucial to acknowledge that, despite these efforts, a residual shortcoming exists, potentially tempering the architectural prowess of real-world data applications. As alluded to earlier in this paper, certain articles are deliberately designed to encompass the realm of networking and communications.
Nonetheless, an equally pivotal facet is the demand for dependable data and seamless network mobility. Passerone et al. [
36] focused on data reliability and enhancing vehicular safety. Within the confines of this article, the focal point is vehicular communication, emphasizing standardizing and fortifying the exchange of data and signals. This endeavor is underpinned by a prudent consideration of diverse factors, encompassing the geographical and environmental contexts of the vehicles, the range of services rendered, and the nuances of information interchange. These contextual elements are the foundation for formulating pertinent parameters and evaluative criteria. Wan et al. [
37] introduced a comprehensive architecture structured around discrete components. At the heart of this architecture lies an acute focus on localization, prominently leveraging deep learning techniques to facilitate the precise identification of autonomous vehicles. A unique hallmark of this methodology is the simultaneous measurement of inter-vehicle distances, serving as a cornerstone for constructing the overarching system architecture. Beyond just localization, the architecture is conceived to incorporate supplementary parameters that encompass diverse dimensions. This entails the potential integration of communication capabilities, including conventional vehicles, and the holistic incorporation of automobile issues.
The central theme explored in article [
38] revolved around utilizing a vehicle-centric framework integrated with machine learning algorithms. This framework served as a conduit for deploying a predictive model designed to mitigate the incidence of malicious attacks and communication aberrations while enhancing overall safety and security. A notable facet of this study lies in its practical implementation, wherein two distinct modules, namely, acceleration reduction and feedback registration, are harnessed. These modules exhibit a commendably high level of predictive accuracy, facilitating the anticipation of vehicle acceleration patterns and movement trajectories by analyzing incoming data.
A noteworthy outcome of this research is the realization of a simulation-based approach characterized by its robust performance metrics in predicting anomalies and potential collisions. Notably, the authors employed a diverse simulator to assess the efficacy of their proposed approach. However, it is important to highlight that using various simulators could introduce complexity to future investigations within this domain. In the subsequent section, drawing upon the stipulated prerequisites and the comprehensive review of the articles above, we embark on a comparative analysis of the disseminated papers, focusing on their architectural attributes and highlighting their merits and limitations.
As depicted in
Table 8, the juxtaposition of these articles is meticulously organized, aligning with the context of secure vehicular architectures. Within this framework, it is discernible that performance metrics and temporal considerations play a pivotal role in assessing the scrutinized methodologies. The design underpinnings of several of these approaches conspicuously integrate the intricacies of communication dynamics, networking paradigms, and the accompanying protocols that govern them. Our comprehensive evaluation systematically elucidated these distinctive attributes, scrutinizing their positive facets and corresponding drawbacks. In
Figure 7, methods are classified based on the parameters covered.
As we saw in the analysis of the articles, about eighty percent of the articles have used solutions based on artificial intelligence in the design. The articles’ comprehensive review and meticulous research have unveiled a distinct imperative: pursuing an all-encompassing system-safety framework demands a more diversified exploration of safety methodologies. While the previously discussed processes bear intrinsic value, it is evident that there is a critical need for the incorporation of supplementary safety measures to realize a comprehensive and universally practical system-safety approach.
In light of the methods elucidated earlier, certain crucial aspects necessitate further investigation to ensure a robust system-safety paradigm. Specifically, the domains of traffic management and the inherent performance of the system emerge as paramount considerations. These elements play a pivotal role in proactively averting the occurrence of errors that have the potential to escalate into road-level accidents. However, the extant methodologies must address these dimensions more adequately, or their applicability across diverse conditions remains tenuous.
In response to these identified gaps, we are poised to delve deeper into these critical issues within the forthcoming sections. By embarking on a comprehensive exploration of traffic management strategies and enhancing system performance to mitigate error-induced hazards, our objective is to contribute significantly to augmenting the existing knowledge. This endeavor aspires to pave the way for a more inclusive, adaptable, and resilient system-safety paradigm that transcends limitations and encompasses many real-world conditions.
5.1.2. Fault-Management Approaches
As elucidated earlier, enhancing safety at both the node and system levels manifests as a strategic imperative to fortify the security of roads and vehicles. Notably, the difficulty of sensor data collection surfaces as a critical consideration. The successful acquisition and interpretation of sensor-generated data wield profound implications for the vehicle’s overall safety and interaction with its environment.
In the endeavor to comprehend the critical faults afflicting vehicles, it is judicious to categorize them into distinct classes. These may include faults from environmental conditions, road characteristics, and weather variables. It is incumbent upon vehicles to react with precision during these circumstances, making determinations that encompass a spectrum of driving parameters. These responses are pivotal in forestalling the materialization of accidents and ensuring the safety of the occupants and surrounding entities.
Indeed, an additional category of faults arises from the malfunction or breakdown of a vehicle’s internal sensors or components. Typically, such issues can be preemptively addressed through robust control and monitoring systems, which can detect anomalies and facilitate the replacement of faulty parts. This proactive approach maintains optimal vehicle performance and minimizes the potential for accidents stemming from internal malfunctions.
Another cluster of errors emerges from traffic accidents and obstacles that inevitably manifest on the road. These challenges can be effectively managed through various solutions, encompassing collision avoidance mechanisms and predictive fault-detection strategies. Imagine a scenario where an accident or unexpected obstruction materializes along a vehicle’s path. In this context, the vehicle can receive pertinent data and respond cautiously. Conversely, if a fault or issue transpires, the vehicle’s ability to predict and appropriately respond promptly is critical to preventing an escalation of the problem, as the unchecked progression of such conditions could ultimately lead to system failure.
In this study, the assessment of risks and their corresponding positions is effectively embedded within the control technique. The quantification of risk severity plays a pivotal role in categorizing risks, allowing for a nuanced understanding of their potential impacts. Moreover, this approach delves into risk management tasks’ time, velocity, and acceleration aspects, providing a multifaceted view of potential hazards.
However, it is noteworthy that, while this approach is robust in its coverage of various risk dimensions, it does not explicitly incorporate environmental factors. This could be a potential limitation as environmental conditions often significantly influence the risk landscape. Furthermore, the study alludes to the deployment of brake management systems, but the testing of these systems under diverse environmental and climatic scenarios still needs to be explored.
The insights gleaned from studies like this are instrumental in shaping the contours of fault-management parameters. By comprehensively addressing risk analysis and assessment, they pave the way for the formulation of precise strategies that contribute to the effective management of faults and, consequently, the enhancement of overall system safety. Leveraging insights from risk analysis within the system, a cluster of articles has discerned influential parameters within the environment and nodes. These parameters are the foundation for formulating fault-control solutions strategically tailored to address potential vulnerabilities. An illustrative example lies in [
39]. Their approach entailed endorsing a fault-tolerant control strategy that focused on path following within vehicles. This innovative solution is meticulously designed to manage and predict potential vehicle faults and account for external environmental factors, such as road curvature.
Biddle et al. [
40] focused on an in-depth examination of sensors and the various factors influencing their performance. The study extended its purview to encompass predicting potential faults in these sensors. The study meticulously detailed a comprehensive approach aimed at mitigating sensor faults. The underlying methodology comprised steps surrounding defects, isolating compromised sensors, and creating modules geared toward prediction and fault detection. In this context, a support vector machine (SVM) is employed to discern errors. Signal streams from the sensors are diligently monitored, enabling the detection of deviations and anomalies that signify potential errors. These deviations are subsequently scrutinized and classified based on the behavioral attributes of the signals. The frequency amplitude property is critical in differentiating standard and erroneous signals, serving as a central discriminator. This property’s characteristics play a pivotal role in identifying and delineating anomalies within the signal data.
Undoubtedly, sensor malfunctions or problems constitute one of the paramount factors that can lead to system faults and, in more extreme cases, result in crashes within autonomous vehicles. This complex issue has garnered significant attention across many articles, including the reference above [
41]. This paper focused on the electronic control units within sensors, presenting a nuanced strategy to mitigate the impact of sensor-related challenges. The essence of this methodology lies in its proactive nature, aiming to predict potential sensor-related issues and safeguard the continued operation of autonomous vehicles, even when a fraction of sensor capacity is compromised. This anticipation-driven approach is geared towards ensuring not only the functional continuity of the cars but also their safety. The methodology within this study unfolded in a structured manner. The initial level involved a meticulous inspection of the hardware and components constituting the sensors. During this stage, potential errors were systematically categorized based on their characteristics. Furthermore, given their sensor capacity, an assessment was conducted to ascertain how vehicles performed in accident scenarios.
One of the pivotal features of this approach involves the handling of critical sensor failures. A contingency strategy is adopted to circumvent disruptions in system performance. This method entails incorporating backup units when essential failures of sensors are detected. By integrating these redundant units, the system’s overall functionality remains intact, bolstering the vehicle’s ability to navigate effectively and ensuring higher safety. In essence, the approach outlined in this paper reflects a comprehensive endeavor to mitigate sensor-induced faults and enhance system safety within autonomous vehicles. The method contributes to a more resilient and robust autonomous vehicle ecosystem through the proactive prediction, the categorization of errors, and the strategic implementation of backup units. Undoubtedly, several studies have ventured beyond the surface to explore the intricate scenarios that could engender errors within autonomous vehicles.
Moreover, these studies have risen to the challenge of devising holistic system-level solutions that optimize data transmission and communication. An illustrative instance of this progressive approach can be observed in [
42]. The central focus of this research lies in addressing the multifaceted nature of data within autonomous vehicle systems, a pivotal aspect that underpins their functionality. At the crux of this study is the proposition of a comprehensive solution to manage the intricacies of multidimensional data. The inherent significance of this approach rests in its ability to accommodate the expansive and diverse data streams inherent to autonomous vehicle operations. Scalability and real-time data computation emerge as cornerstones, ensuring that the system can handle the dynamic influx of data streams while concurrently processing them in a time-sensitive manner.
Furthermore, the research extended its purview to encompass fault detection within the data domain. This emphasis on data integrity and accuracy is essential to preempting errors rooted in incorrect information. Detecting and rectifying data errors is an integral component of this strategy. Specifically, the proposed method involves clearing information from designated camera areas within the autonomous vehicle’s surroundings and rectifying distortions within each data frame. The system is poised to operate with a higher degree of reliability by eradicating these inconsistencies and inaccuracies.
In essence, this study represents a forward-looking endeavor to optimize data processing within autonomous vehicles. By proposing scalable solutions for multidimensional data management and incorporating robust fault-detection mechanisms, the researchers contributed to a more effective and dependable autonomous vehicle ecosystem. Parseh et al. [
43] brought attention to a novel and significant subject that has yet to undergo exhaustive exploration. The focus here centered on collision reconfiguration systems within autonomous vehicles (AVs), introducing fresh demands for comprehensive accident analysis. This paper entailed formulating innovative safety surrogate measures (SSMs) that account for the frequency of accidents and incorporate considerations of severity. Additionally, the article delved into the need for recording the trajectories of traffic instances that closely approach collisions. This trajectory data served as a basis to identify analogous scenarios and uncover concealed variations in these situations, contributing to a more nuanced understanding.
Park et al. [
44] strongly emphasized accident prevention, explicitly targeting collisions involving vehicles and barriers. In contrast to the previous article, which concentrated on collision reconfiguration systems, this study took a proactive stance by addressing potential collisions with other moving vehicles. Parameters such as acceleration, speed, distance, and static obstacles form the crux of the investigation. This approach aims to comprehensively understand each vehicle’s trajectory, facilitating the identification of potential collision points. Moreover, the study acknowledges the potential significance of environmental conditions and the information derived from vehicle-to-vehicle (V2V) communication. These factors can be incorporated into the model’s development parameters, enhancing its predictive capabilities and fortifying the safety mechanisms.
Zhu et al. [
45] focused on predicting and preventing errors within autonomous vehicles. This study is devoted to the critical aspect of risk assessment, focusing on control strategies. Notably, what sets this article apart is its utilization of supervised learning algorithms underpinned by a Bayesian model. This dynamic and realistic approach promises to predict the occurrence of risks and discern their severity levels in real-time. The relevance of this advancement becomes particularly apparent in the context of self-driving cars. By enhancing the prediction and assessment of risks, this methodology contributes to optimizing the vehicles’ performance in the event of potential collisions. The process involves aggregating, categorizing, and utilizing route and environmental data to classify various levels of risk. Importantly, this approach is not confined to theory but has been employed in various networked autonomous vehicle applications. The extraction of risks hinges on the integration of environmental and vehicle-specific data. Through this integration, the methodology determines the probability of risk occurrence at the lowest layer, the vehicles themselves, and their interrelationships.
The outcomes of this predictive approach are quantified in terms of impact parameters and risk levels. These outputs hold significance as they form the basis for accident prevention and safety management across different tiers of autonomous vehicles. The research employed simulations rooted in real-world data to substantiate the efficacy of this approach. As a potential avenue for further enhancement, integrating real-time data such as weather patterns and temporal parameters could enrich the methodology’s responsiveness and ability to address dynamic scenarios.
In summary, Biddle et al. [
40] offered an innovative approach to risk assessment, prediction, and prevention within the domain of autonomous vehicles. By employing advanced learning algorithms, Bayesian models, and real-time data, this research advances state-of-the-art autonomous vehicle safety with the potential for even more terrific refinement by incorporating additional real-time factors. In addition to tackling the crucial realm of vehicle collisions, researchers have delved into the cost-related aspects and navigated the challenges of vehicle communication. The study referenced as [
46] is particularly notable in this context. The complexity of previous techniques is mitigated by introducing an optimized acceleration and speed control method, yielding improved real-time performance. This optimization not only addresses cost-related concerns but also enhances the overall efficacy of the approach.
Moving on to article [
47], a different perspective is adopted—one that centers on obstacles. This study ventures into the domain of online verification and diagnostics systems tailored for vehicles. The core function of this system is to evaluate the presence of static barriers along a given route and predict their impact on the vehicle’s trajectory. This predictive model is a certification mechanism to ascertain whether the vehicle can safely proceed along its intended path. An intriguing facet of this method is its vehicle-agnostic nature. Irrespective of the vehicle type, the system leverages parameters such as time, speed, acceleration, and road conditions to determine whether the vehicle can safely proceed along its intended path. This holistic approach ensures that the predictions are adaptable to various vehicle scenarios.
Cui et al. [
48] uniquely navigated the intersection of safety and security concerns within autonomous vehicles. Enhancing network and data security contributes to reducing system errors while simultaneously fortifying the defense mechanisms against potential attacks. The outcome is an elevated level of protection that permeates both autonomous and traditional vehicles.
When surveyed collectively, the array of articles sheds light on the multifaceted nature of fault management within autonomous vehicles, spanning diverse levels and perspectives. The subsequent endeavor involves categorizing the identified parameters and embarking on a comparative analysis of the reviewed articles, contributing to a comprehensive understanding of the broader landscape of fault-management strategies and methodologies. The article’s fault-management landscape can be categorized into two overarching groups: node-level error management and system-level error management. Within these categories, a cluster of papers has honed in on environmental and vehicle attributes to devise comprehensive solutions. Addressing vehicular limitations such as mobility constraints, resource boundaries, and the necessity for swift responses has been pivotal to enhancing these approaches.
This section serves as a comprehensive analysis of fault-management methodologies. The efficacy of these strategies hinges on critical factors such as the success rate in detecting faults, service response time, and overall reliability. Many of these strategies are predicated on delineating parameters across different architectural layers. These parameters play a central role in optimizing the fault-management processes.
As articles are surveyed, they can be organized and evaluated within the framework of the categories above. Examining the advantages and disadvantages inherent in these methodologies informs the crafting of refined and effective fault-management strategies. A comparative analysis is presented in
Table 9, where the articles are categorized and evaluated based on the mentioned criteria to facilitate a comprehensive understanding of this landscape.
By categorizing and evaluating these articles through the lens of node-level and system-level error management while considering environmental and vehicle factors, the groundwork is laid for a deeper comprehension of fault-management strategies. This analysis is a roadmap to ascertain the most effective and adaptable approaches within autonomous vehicle systems.
5.1.3. Safety Parametric Models
Leveraging a mathematical model and a multi-objective function is a common approach to enhance the quality attributes of autonomous vehicle systems. Numerous articles on vehicle safety and other associated challenges, spanning cost management, barrier mitigation, and service provision, delve into these complexities and safety concerns. This section of the analysis examines the utilization of mathematical models within the context of autonomous vehicles and mixed traffic scenarios. By outlining these techniques, we lay the groundwork for the subsequent classification of variables and methodologies. It is necessary to consider various parameters such as technical limitations, environmental and traffic restrictions and requirements, and physical limitations in determining vehicle safety models [
49]. For this reason, many methods have extended into parametric modeling.
Indeed, mathematical models have emerged as a foundational tool in pursuing safety improvements. Jiang et al. [
50] presented an optimal multi-objective function that extends beyond the parameters directly linked to accidents. This function considers the temporal dimension, precisely traffic intervals, within the mathematical framework. The overarching objective of this model is to minimize casualties over an extended period. This nuanced approach optimized the balance between accident prevention and temporal considerations. As demonstrated by the example in [
50], the holistic approach captured in these models underscores the multi-dimensional nature of safety optimization. This mathematical lens allows for a more precise analysis, potentially paving the way for more effective safety interventions and strategies.
Ref. [
51] adopted a distinct focus on the risks inherent in the decision-making processes of autonomous vehicles. This study comprehensively evaluated risks, drawing on environmental criteria and risk classification to construct a robust risk model. This model considers a broad spectrum of risk categories encompassing life and road hazards and environmental and financial risks. A critical consideration emerged from the dynamic nature of these risk categories. As environmental conditions and legal regulations evolve, risk assessment criteria may change. Consequently, the priorities and weights attributed to different risk categories could shift. This fluidity necessitates regular revision and updates to a model’s measures to ensure its relevance and accuracy.
Papadoulis et al. [
52] provided a testament to the application of intelligent algorithms to enhance vehicle safety through a multi-dimensional approach. Integrating environmental and systemic factors, combined with vehicle speed as a pivotal feature, showcases the depth of consideration undertaken for safety optimization. Testing the model across varied traffic scenarios further bolsters its practical relevance. However, omitting certain factors like day-night conditions and weather warrants acknowledgment and potential avenues for further investigation.
In enhancing the safety of connected autonomous vehicles (CAV), Jo et al. [
53] introduced a pioneering approach through a probabilistic modeling framework to address cyber-attack challenges. Their model centers on two vital sub-systems: a perception system and a safety monitoring system. These sub-systems facilitated identification across various vulnerability modes. The model’s foundation lies in assessing the probability of cyber-attacks targeting the perception system and safety monitors, and the likelihood of encountering false alarms. However, crucial aspects like missed detections, the potential for successful attack recovery, and the probability of collisions were notably absent from the model’s consideration.
Shifting our focus to article [
54], the authors’ primary objective was collision prevention and safety maintenance within self-driving vehicles. This paper introduces a theoretical model for the minimum safety distance required to avert collisions. The model’s formulation draws on critical variables such as the vehicle’s lateral acceleration, turning speed, route management, and lane change dynamics. The model’s versatility yields success across various scenarios, effectively preempting accidents. There remains room for refinement to align it more closely with real-world complexities. This could improve its accuracy and realism, particularly concerning scenarios beyond controlled environments. Additionally, time-related parameters, such as customer reaction times, are introduced into the model, amplifying its applicability and realism.
In summary, Ref. [
55] adopted a probabilistic modeling approach to tackle cyber-attack challenges in the context of CAV safety. The model encompassed sub-systems, but certain elements require further integration, such as the possibility of missed detections and collision probabilities. Meanwhile, Ref. [
54] contributed a theoretical model for collision prevention and safety in self-driving vehicles, demonstrating its versatility in various scenarios. Nonetheless, refining the model’s realism and accounting for real-world complexities remains an ongoing challenge. Including time parameters like customer reaction times bolsters the model’s practicality.
Table 10 provides a comprehensive overview of various aspects gleaned from the reviewed papers. These methodologies predominantly focus on elevating the safety of vehicles, and they typically adopt a parametric model approach. However, it is essential to recognize that the complexity of their implementation warrants careful consideration, especially concerning their applicability across different layers and conditions.
In summary, fault management is a linchpin in system performance within the automation domain. Its application is particularly pronounced at specific levels of automation. While often adaptable across levels, the overarching methodologies demand consideration of implementation complexity. Evaluative measures, encompassing cost, responsiveness, and success rates, play an indispensable role in gauging these methods’ real-world feasibility and utility. The subsequent table elucidates the nuanced landscape of advantages and disadvantages, encapsulating the essence of these methodologies in pursuit of enhanced vehicle safety and performance.
5.1.4. Traffic Handling Approaches
Indeed, the models and architectures proposed in the reviewed papers demonstrate a commendable effort in accounting for a wide array of parameters pertinent to environmental conditions and the unique characteristics of autonomous vehicles. While specific methodologies have incorporated provisions to encompass conventional vehicles, the intricate landscape of traffic management serves as a vital complement to these approaches.
Traffic management operates as a linchpin in bolstering the efficacy of these methodologies. The complexities associated with changing lanes, traffic volume fluctuations, static and dynamic obstacles, and road conditions ranging from turns to varying traffic dynamics can significantly influence the safety of vehicles on the road. In essence, traffic management is pivotal in harmonizing the interactions between conventional and autonomous vehicles, ultimately enhancing the overall safety of the ecosystem.
As a result, the focus will now shift towards discussing methodologies explicitly tailored to traffic management while considering autonomous vehicles’ unique characteristics and requirements. This encompasses a holistic approach that emphasizes the vehicles and the broader context of traffic conditions and interactions. This discussion will further underscore the interconnected nature of the autonomous vehicles’ ecosystem and the larger traffic environment in which they operate. By effectively navigating the intricacies of traffic management, the potential to maximize safety outcomes is enhanced, creating a more harmonious and secure driving landscape for all road users. Both road and urban traffic exhibit a dynamic nature, requiring multifaceted approaches to analyze their complexities. Time parameters emerge as crucial facets in predicting traffic patterns. Urban areas, for instance, witness distinct busy hours that must be accounted for in any forecasting model. On the roadways, vehicle speed and inter-vehicle distance hinge on traffic volume. Unlike a fixed pattern, traffic lacks a static structure. Environmental conditions, temporal considerations, systemic dynamics, and infrastructural attributes shape it.
Weather conditions substantially influence traffic patterns, yet, this factor needs to be consistently integrated into many solutions. Vehicle speeds are variable and subject to a combination of local regulations, driver decisions, and the performance of self-driving vehicles. Further complicating matters, the uncertainty surrounding traffic situations amplifies the challenge of crafting accurate and responsive traffic management strategies. Traffic management within self-driving vehicles is a complex endeavor, necessitating a comprehensive approach that embraces the intricate interplay of various factors. The dynamic nature of traffic, the diverse influences shaping it, and its inherent uncertainty underscore the multifaceted landscape that traffic management methodologies must navigate. The ultimate goal remains the creation of systems that can effectively anticipate, respond to, and mitigate the challenges posed by dynamic traffic environments, thereby contributing to enhanced safety and efficiency within the autonomous vehicles’ ecosystem.
Indeed, rapid response and adaptive strategies in the face of accidents or collisions are paramount considerations in autonomous vehicles. Various articles within the reviewed literature have strived to elucidate the parameters that influence the safety of vehicles, paving the way for the development of robust and adequate safety measures.
Qin et al. [
13] delved into the intricate web of parameters that impact vehicle safety, particularly within the context of autonomous vehicles. This research focused on a broad spectrum of parameters, encompassing aspects of the vehicle’s environment, internal conditions, and obstacles. This methodology tackled the challenge of accident prevention through a multifaceted approach. This approach commences by introducing critical parameters like acceleration and speed, which are pivotal in dictating the vehicle’s behavior in response to dynamic circumstances. This step requires the intricate management of speed based on the permissible limits within a given environment, while accounting for unforeseen events. Beyond external influences, the model delves into the vehicle’s internal design elements. Factors like engine speed are intricately tied to acceleration characteristics, with their impact reverberating through the vehicle’s behavior. Ensuring the reliability of the data fed to the vehicle’s sensors, effectively addressing any noise in the data, remains paramount to the success of this safety-oriented approach.
Approaching obstacles warrants a nuanced strategy to ensure the safety of vehicles, and Zhu et al. [
56] offered a methodology rooted in reducing engine speed and, where feasible, a change in the vehicle’s trajectory. This simulation framework employs the Controller Area Network (CAN) protocol, which finds widespread application in industrial and automated contexts. However, using the CAN in this context introduces limitations that impact the method’s comprehensiveness and real-world applicability, particularly within mixed traffic scenarios involving conventional vehicles. Incorporating the Internet of Things (IoT) and the CAN protocol aligns with prevalent practices in industrial settings. However, this application’s limitations are evident when extrapolated to a real-world automotive environment characterized by mixed traffic. The constraints of the CAN protocol restrict its ability to capture the intricate nuances of a vehicle’s surroundings, limiting its efficacy in scenarios involving both autonomous and conventional vehicles.
Mixed traffic environments necessitate a more versatile and generalized protocol to accommodate the diverse communication needs of various vehicle types. While the article’s approach capitalizes on specialized sensors to detect obstacles nearby, its mode of operation—coming to a complete stop before rerouting—introduces potential safety risks for conventional drivers sharing the road. Conversely, this methodology boasts energy efficiency, as well as cost- and resource-reduction advantages, for autonomous vehicles. The autonomous vehicles in question benefit from optimized energy consumption and resource allocation by avoiding diversion risks and challenges.
In essence, Kosari et al. [
57] embodied the fusion of cutting-edge machine learning techniques with the intricacies of pedestrian and vehicle recognition. Utilizing the SqueezeNet algorithm, this methodology attains impressive accuracy levels while minimizing data transmission delays. This characteristic highlights the potential of machine learning in optimizing safety and efficiency within the autonomous vehicle landscape, thereby contributing to the broader objective of creating a secure and responsive driving environment.
The researchers have formulated an algorithm to manage the trajectory of autonomous vehicles, as outlined in the study by [
58]. This algorithm utilizes environmental data, including camera inputs and route control mechanisms, to evaluate potential hazards along a vehicle’s path. By doing so, it can anticipate and mitigate potential accidents involving autonomous vehicles. The algorithm’s predictions facilitate a decision-making process wherein the vehicle may either persist along its current trajectory or make adjustments to its course. Another cluster of studies delved into strategies for averting accidents through obstacle management. In [
59], the approach involved extracting essential parameters by initially encompassing all types of vehicles alongside static and dynamic environmental obstructions while examining their interrelationships. Potential risks are pinpointed through a comprehensive collision prediction model involving vehicles and obstacles, paving the way for subsequent remedial actions. This research introduced an innovative framework emphasizing the proactive anticipation and effective resolution of challenges. The identification of obstacles harnesses the power of camera imagery. However, it is worth noting that the efficacy of the proposed technique is contingent upon the intricate process of recognizing vehicles within the obtained images. Furthermore, integrating route planning and addressing the mobility aspect of vehicles hold the potential to further refine and augment this methodology.
The central theme of [
56] revolved around developing a robust strategy for delivering in-vehicle warning information with a foundation in crash-risk prediction. This strategy leveraged vehicle interaction data from a forward collision warning system and vehicle-to-vehicle wireless communication. The core objective of this paper was to furnish anticipatory warning information within the context of preventive traffic safety management (PTSM), thereby enhancing accident prevention efforts. Additionally, the authors delineated three distinct metrics designed to assess the warning information’s dependability and mitigate the potential for accidents.
Sun et al. [
60] introduced an innovative “intelligent connected vehicles” paradigm to orchestrate vehicular traffic. This approach entails continuous surveillance of vehicles to prevent collisions and accidents. The comprehensive dataset harnessed for vehicle control and navigation encompasses inputs from vehicle sensors and a network of interconnected devices. Consequently, this article stands out as one of the rare methodologies that accentuate the significance of data amalgamation from network sources and the surrounding environment. To augment the effectiveness of this approach, it becomes imperative to integrate cost and resource management to alleviate the intricacies associated with real-time deployment. Concurrently, several other studies have explored alternative strategies to navigate obstacles and avert accidents.
Wang et al. [
61] provided an intricate realm of lane changes within the context of traffic management. This model considers factors such as diversions, proximate vehicles, and the prevailing traffic volume in the surrounding environment. Within this framework, meticulous tracking of the vehicle and its ongoing trajectory takes place. When circumstances necessitate, the model initiates a lane change, dynamically assessing the control prediction model to identify the optimal path. Subsequently, responsive actions are taken to effectuate the required change in direction. This model considers the dynamic shifts in vehicle positions and traffic’s ever-changing flow and intensity. When deliberating upon the optimal route, it remains crucial to account for vehicle connectivity and its associated parameters. Several scholarly works have delved into the traffic management domain, emphasizing fundamental vehicle parameters.
Zhang et al. [
62] proposed a lane-changing strategy grounded in a comprehensive assessment of traffic-related factors and prevailing environmental conditions. The focal point of this research was its adaptability to the necessities of real-world scenarios for autonomous vehicles, where immediate responses are imperative. This strategy exhibited flexibility, adjusting based on predefined controls. Typically, rerouting is enacted in response to planned considerations stemming from environmental variables. Path control and planning coordination are executed by regulating inter-vehicle distances and a vehicle’s spatial positioning, further augmented by longitudinal distance measurements relative to obstacles.
Xiao et al. [
63] introduced a solution for obstacle management, catering to quality and time parameters while prioritizing safety. This research harnessed Hamiltonian analysis to devise a control system that facilitates cost management and orchestrates optimal route guidance. A distinct facet setting apart the scenarios explored in this article from other studies is formulating a mechanism to govern and suppress noise, further enhancing control efficacy.
An additional consideration within this discourse pertains to the computational costs incurred by the operations. Zhang et al. [
64] provided a comprehensive framework governing the motion of autonomous vehicles. This framework comprehensively addresses a spectrum of dynamics, encompassing lane changes, directional shifts, adeptly navigating obstacles and potential road hazards, and regulating vehicle velocity and acceleration. Central to this technique is a component tailored to evaluate the associated risks and safety hazards, informed by the unique attributes of the route and the vehicles involved.
The methodology necessitates further elaboration to specify intricate particulars to avoid collisions with stationary obstructions. In light of the distinctive movement patterns exhibited by vehicles, a suite of solutions is explored to govern vehicle placement within Wang et al. [
65], focused on optimizing mobility through temporal control of autonomous vehicles’ positions. The method factors environmental conditions using probability-based equations derived from collision and accident likelihood over different time intervals. This modeling approach is rooted in the overarching system architecture. Supplementing the ambient data, real-time feedback from the vehicle’s current state is assimilated within the perception layer, with updates orchestrated at predefined time intervals. The solution served to enhance the accuracy of decision-making within the framework.
In the report’s context [
66], the emphasis is placed on road roughness as a contributing factor to accidents. Parameters grounded in vehicle characteristics and their interaction with road irregularities are delineated. The proposed algorithm addresses the challenge of maintaining proper wheel contact on uneven surfaces, involving a mathematical equation and specified parameters to define vehicular motion on such roads. It is important to note that, while this technique presents a comprehensive framework for handling road roughness, it currently needs to incorporate real-time resource management or systemic status monitoring, requiring further refinement to achieve practical applicability.
On the other hand, Kummetha et al. [
67] modeled driver behavior, leveraging vehicle-specific parameters such as acceleration and speed. The temporal aspect is paramount here, as timely response times are pivotal for mitigating accident risks by established criteria. These studies offered valuable insights into enhancing safety and accident prevention in both cases. However, there are distinct areas for refinement and improvement regarding real-time resource management and systemic status tracking, as well as including temporal considerations.
He et al. [
54] introduced a comprehensive mathematical model to facilitate the efficient management of lane changes for autonomous vehicles. This model offers a solution that describes the trajectory and motion of a vehicle while undergoing a lane change, accomplished through the establishment of mathematical relationships. A notable aspect of this model is its adaptability to densely populated urban routes, reflecting its versatility across various traffic environments. The delineated scenarios within this framework are broadly classified into two overarching categories: general and emergency. These categories are the foundation for assessing and validating the model’s efficacy. This model demonstrates robust performance and effectiveness by undergoing scrutiny across a spectrum of scenarios, rendering it suitable for diverse situations and providing valuable insights for lane change management in autonomous vehicles. Zhang et al. [
68] presented a comprehensive analysis encompassing vehicle safety, and considered the energy and cost constraints associated with vehicles. A two-stage framework was meticulously designed, catering to security and energy efficiency. This framework addresses single-objective decision-making challenges that arise in such contexts.
Higher engine efficiency is typically associated with increased energy consumption. Regarding the mentioned article [
69], deep learning and neural networks are employed for validation, ensuring the effectiveness of broadly established safety techniques. This methodology notably centers on video data, leveraging the quality and reliability of this data to devise solutions pertinent to autonomous driving. The assessment of autonomous driving status hinges on input data gathered from traffic cameras. The study underscores the multifaceted nature of traffic management solutions, revealing various perspectives.
The subsequent segment involves a comparative analysis presented in
Table 11, which systematically evaluates and categorizes these solutions, providing a taxonomy for reference. The prevalence of articles within this category signifies its prominence. These articles adopt a comprehensive approach, considering both the environmental factors and the internal software components of autonomous vehicles. Consequently, reviewing diverse methods highlights a consistent consideration of resource management and real-time responsiveness. These aspects are recurrently mentioned in the context of advantages and disadvantages across numerous papers.