1. Introduction

Similar to other concepts in the broader field of artificial intelligence and computer science, the term heuristics is not originally a term that is exclusive to the computer science field. The term heuristics has its origins in the fields of psychology, philosophy, and mathematics, where it was initially developed as a method for problem-solving. The concept of heuristics was first introduced in the field of computer science around the mid-20th century. One of the reasons for the introduction of heuristics into the field of artificial intelligence was the growing awareness that brute-force problem-solving is constrained by its inherent processing limitations and the outcomes it produces over time [1]. The objective of heuristics in AI and computer science is to address complex and challenging design issues in a time- and resource-efficient manner. In contrast to the pursuit of an optimal solution, which may necessitate a significant investment of time and computational resources, heuristics seek to achieve the best result in a relatively short time frame [2]. This aligns with the practical constraints of real-world design and problem-solving. In view of the above, heuristics, which provide a rapid, adaptable, and uncertainty-adjusted approach, have emerged as an effective solution in a number of real-world applications of AI decision-making.

2. Heuristics and AI in autonomous vehicles

2.1. The origin of heuristics in AI decision making

As previously established, the fundamental objective of integrating heuristics and artificial intelligence is to address decision-making challenges in ways that align with real-world constraints. This objective is indicative of the growing recognition that exhaustive problem-solving approaches are inherently limited by their computational demands and the impracticality of achieving optimal outcomes in complex, dynamic environments [3]. In light of the aforementioned considerations, heuristics, which offer a prompt, adaptable, and uncertainty-adjusted approach, have emerged as an efficacious solution in a number of real-world applications of AI decision-making [4]. It is widely acknowledged that the Hungarian mathematician George Pólya was one of the earliest and most prominent advocates of the concept of heuristics. This is demonstrated by his foundational work, How to Solve It, published in 1945, in which he provides a comprehensive examination of heuristic techniques as a means of problem-solving. Moreover, Pólya regarded heuristics as a more pragmatic and beneficial approach to decision-making in the real world. Rather than pursuing an unattainable, idealized perfect answer, heuristics enable individuals to identify the most effective solution. The introduction of heuristics marked a pivotal moment in the evolution of artificial intelligence, laying the foundation for the development of AI-powered autonomous vehicles.

2.2. Adaptive decision making in varied environments

The term "adaptive decision" denotes the process by which autonomous systems are capable of modifying their decision-making processes and strategies in response to changes in the real-world environment. The growing prevalence of AI development in recent years has made the realization of adaptive decision-making a more attainable goal, due to the influence of AI [5]. The adaptive component represents a fundamental aspect of autonomous systems, analogous to the role of the driver's brain in a traditional vehicle. It is responsible for decision-making, adjustments, and the navigation of paths towards a desired destination. In practical, real-world scenarios, the capacity for adaptive decision-making will be of significant benefit to autonomous vehicles navigating complex urban environments. These environments present a number of challenges, including interactions at intersections, the presence of cyclists and other road users, and the prevalence of unpredictable factors such as wildlife. Similarly, in rural settings, where road signs are less prevalent and the surrounding landscape is less structured, adaptive decision-making will be essential for autonomous vehicles to navigate safely and effectively [6]. In general, the adaptive decision-making process must consider a number of factors, including meteorological conditions, unpredictability, and the surrounding environment, in order to make appropriate decisions. It is of greater importance that the adaptive decision system is capable of responding to any traffic patterns, such as sudden stops, changes in lane conditions, congestion, and so forth. Heuristics are of great consequence in the realm of adaptive decision-making, as they facilitate a more expeditious response to alterations in the surrounding environment. Such a system can assist in determining when to stop or change lanes, which is often advantageous in situations that require quick responses to emergencies or unpredictable changes that may pose a risk to vehicle safety. Moreover, machine learning and reinforcement learning facilitate the system's capacity to learn from past experiences, situations, and cases, thereby enhancing its ability to comprehend the real world and improve its decision-making capabilities, ultimately leading to safer and more optimal decisions in a shorter time frame. Currently, adaptive decisions working in conjunction with data collectors such as cameras, sensors, and radar are a crucial component of autonomous vehicles with regard to the safety and practical application of autonomous vehicles in the real world.

2.3. Enhancing safety through heuristic algorithms

Heuristic algorithms represent a framework designed for the application of autonomous vehicles. They are capable of rapidly solving complex issues and solving real-world problems in a robust and effective manner. Moreover, they prioritize safety in any situation based on the data collected by the vehicle's sensors. In the present era, the principal heuristic-based pathfinding algorithms are A* and D*, both of which are fundamental to enhancing the safety of autonomous vehicles [7]. A* is one of the most commonly used heuristic algorithms, wherein the evaluation function is represented as f(n)=g(n)+h(n). In this representation, g(n) denotes the known cost from the starting point to the node n, which is also referred to as the path cost. h(n), on the other hand, represents the heuristic function, with the objective of estimating the cost from node n to the destination. In essence, the A* algorithm serves as a function designed to identify the shortest distance path from the initial point to the final destination [8]. In the majority of cases, it is the optimal choice for determining the most suitable path based on its heuristic principle, rendering it highly beneficial in real-world scenarios where there are buildings, traffic, and unpredictable factors. The A* algorithm is capable of adapting its path in real time based on the data it collects. Conversely, D* represents an extension of the A* algorithm, specifically designed for dynamic environments. In comparison to A*, D* demonstrates superior performance in adapting to the ever-changing environment and unforeseen variables through its rapid responsiveness. D* is capable of recalculating the path in real time, thereby ensuring the safety of the vehicle from potential dangers posed by moving objects, including other cars and people. Similarly, D* is an algorithm that relies on heuristic techniques to facilitate the generation of useful and applicable decisions in real time, which may occasionally prove to be lifesaving. When both A* and D* heuristic algorithms are integrated, autonomous vehicles are capable of adapting and operating effectively in both familiar and evolving environments, thereby greatly expanding their potential range of environments in which they can function.

3. Ethical and safety challenges in autonomous vehicles

3.1. Ethical considerations in autonomous vehicles

As with other technological developments in human history, the advent of heuristics and artificial intelligence in autonomous vehicles has given rise to a number of significant ethical concerns. The accelerated pace of technological advancement in autonomous vehicles has led to a shift in public perception. While there is a general trust in the development of technology, there is a heightened concern for the well-being of individuals, particularly in the context of autonomous vehicles. Even a minor error can have significant ramifications, ranging from property damage to loss of life. Consequently, as autonomous vehicle technology advances, ethical considerations have emerged as a pivotal concern for various stakeholders, including users, governments, and manufacturers [9]. Same as human drivers in the time of emergency, though AI and algorithm-powered systems of autonomous vehicles are able to have more rational thinking than humans, one major drawback is that autonomous vehicles are only able to think within the confines of rationality, relying solely on data and algorithms. They lack the emotional thinking that humans possess, which makes it hard to understand or relate to the decisions made by the vehicle. At least for now, most of the public are not able to share the same emotional connection with the decisions made by autonomous vehicles, and this lack of understanding can become a major disadvantage when it comes to ethical challenges [10].

3.2. Lack of emotional and ethical thinking in autonomous vehicles

In the event of an emergency, autonomous vehicles are capable of performing the same functions as a human driver, thus offering a potential solution to the issue of human error in driving. It is important to acknowledge, however, that these vehicles lack the flexibility of the human mind in terms of emotional thinking, which is a crucial factor in decision-making. One illustrative example of this would be a situation in which a child suddenly runs into the street while an autonomous vehicle is approaching. In the majority of cases, human drivers consider emotional and ethical factors when making decisions, and thus will typically attempt to protect the child by avoiding or changing lane position [11]. In such a situation, the driver would endeavor to stop at all costs in order to save the child's life. However, it is possible that the decision-making system of autonomous vehicles may not take this into account and may not necessarily protect the child regardless of the cost. This is due to the fact that such systems are based on previous experiences, data, and algorithms, rather than on the emotional factors that would prompt a human driver to take action. Furthermore, autonomous vehicles are limited in their ability to make decisions based on emotional factors, as they rely on algorithms and data to inform their decision-making processes. The lack of emotional processing in autonomous vehicles represents a substantial obstacle to understanding and empathizing with the decisions they make. It seems likely that the majority of the public will not evince the same emotional response as the decision-making system of autonomous vehicles or be able to comprehend the rationale behind these decisions [12]. This could potentially give rise to significant ethical challenges in the future.

3.3. Public trust and acceptance in safety of autonomous vehicles

A review of historical evidence suggests that technologies that lack sufficient support and trust are unlikely to flourish. Some may encounter obstacles midway through their development, while others may fail to achieve their full promotion potential. Such technologies may even be subject to bans and significant opposition. In the case of autonomous vehicles, the development of heuristics and artificial intelligence, and their application in autonomous vehicles, the primary concern is the safety of the public, which impacts the trust and support that the public shows towards autonomous vehicles [13]. As autonomous vehicles are still in the early stages of development, the primary sources of information for the general public are historical records, personal experiences, and publicly shared incidents. Despite the relatively low number of accidents involving autonomous vehicles, the significance of these vehicles to the future of transportation means that such incidents are often given undue attention. This results in a perception of increased risk and uncertainty regarding the use of autonomous vehicles in public. A further crucial factor influencing public opinion with regard to the safety of autonomous vehicles is the transparency of the data and decision-making processes involved. This factor is of particular importance in the context of the current debate surrounding the regulation of autonomous vehicles [14]. As previously stated, the fundamental principles of autonomous vehicles are supported by heuristic algorithms and the data they collect in real time and retrospectively. It seems reasonable to posit that the public would prefer transparency on this matter, given the potential for safety issues to arise in the event of a promotional emergency. It is evident that the general public requires a more comprehensive understanding of this subject matter. A century ago, the advent of the first automobile evoked a comparable skepticism from the public, attributable to concerns surrounding the novelty of the concept, potential risks and the perception of the vehicle as a threat. However, once the public is informed of the facts surrounding the automobile, the demand for automobiles increases exponentially, accompanied by a surge in support and public trust. In the present era, automobiles have become a ubiquitous phenomenon, with a significant proportion of the population owning at least one. It is, therefore, incumbent upon the producers and companies of autonomous vehicles, as well as the governments, education systems and so forth, to assume responsibility for the education and transparency regarding autonomous vehicles, with a view to developing and utilizing their potential to the full.

4. Case studies and future of autonomous vehicles

4.1. Case study 1: Waymo’s autonomous vehicle technology based on AI and heuristic algorithms

Formerly a self-driving car project developed by Google, Waymo is now an American company that serves as a pioneer and leader in the field of autonomous vehicles. Over the course of several years, Waymo has developed its own autonomous driving system, designated as Waymo Driver. This system is one of the most rigorously tested autonomous vehicle technologies currently in existence. The system's fundamental reliance on a combination of heuristics and AI ensures the safety of users [15]. The primary function of AI in Waymo Driver is to recognize the surrounding environment, including road signs, pedestrians, and other vehicles, in a manner analogous to that of humans. The AI is the optimal component for enabling the system to comprehend its surroundings based on the data it collects from sensors. Furthermore, AI enables the Waymo autonomous vehicles to anticipate the movements of other vehicles and pedestrians, thereby enhancing situational awareness. The heuristic algorithm is incorporated into the Waymo autonomous vehicle for the purpose of path planning. Waymo's autonomous vehicle employs the A* algorithm, which enables the heuristic algorithm to calculate the optimal, safest, and most efficient path planning [16]. In addition to the aforementioned algorithm, Waymo also employs a heuristic evaluation technique that assesses the evolving environment in real time and modifies the path planning accordingly, thereby ensuring the safety and efficacy of the vehicle. The majority of Waymo autonomous vehicles operate within urban contexts, guided by data and the integration of AI and heuristic algorithms. This enables the vehicles to adapt to environmental changes with precision and reliability. The Waymo case has demonstrated the significant potential, reliability, and feasibility of autonomous vehicles in modern society.

4.2. Case study 2: Uber’s self-driving car accident in Tempe, Arizona

On 18 March 2018, in Tempe, Arizona, a pedestrian was fatally injured by an Uber self-driving vehicle. Elaine Herzberg was struck by the vehicle while traversing the roadway outside of a crosswalk in autonomous mode. This incident represents a significant turning point in the development of autonomous vehicles, providing a tangible illustration of the ethical challenges that these technologies may pose in real-world settings. This incident represents the first known fatality involving a fully autonomous vehicle and has prompted extensive debate about the safety and ethical implications of such technology [17]. The consequence of this accident has been the temporary suspension of Uber's autonomous vehicle testing, as well as an ongoing debate on the topic of the safety and morality of autonomous vehicles, due to the unique controversy surrounding them. The incident has brought to the fore a significant ethical and legal issue that remains a topic of ongoing debate: namely, who should bear responsibility for damages incurred in autonomous driving? This question pertains to the respective roles of vehicle owners, manufacturers, and engineers, particularly in the context of algorithmic decision-making. From the standpoint of the general public, each of the aforementioned parties appears to be a reasonable candidate for assuming responsibility. However, this very fact renders their respective roles equally untenable. Some may argue that the vehicle owner should assume responsibility, as the human operator is responsible for overseeing real-time situations and making decisions in emergency situations. Alternatively, some may suggest that the car producer should be held liable, given that it is their product and that they are responsible for ensuring that it is safe. Similarly, some may propose that the engineers of the algorithm should be held accountable, as they are responsible for designing the vehicle's decision-making capabilities and, therefore, for any damages caused by faulty decisions [18]. The aforementioned debates and controversies can be attributed to the lack of clarity surrounding the legal frameworks and regulations pertaining to autonomous vehicles and AI-related technologies. It is therefore imperative that the government, manufacturers, and legal entities collaborate to establish the requisite boundaries and framework prior to the widespread introduction of autonomous vehicles.

4.3. Future of heuristics and artificial intelligence in autonomous vehicles

In considering the future of heuristics and artificial intelligence in autonomous vehicles, it is crucial to acknowledge that this technology is still in its infancy and that its potential remains largely untapped. It is therefore reasonable to predict that there will be a greater number of adaptive heuristics, capable of reacting more rapidly and performing better in situations of emergency and unpredictability [19]. It is also worth noting that it is highly probable that a superior combination of AI and heuristic models will emerge, which will maintain the essential role of display in design and incorporate a machine learning model that ensures previous experiences and data are better integrated into the decision-making process of autonomous vehicles over time. In addition to the technological aspects of autonomous vehicles, it is crucial to ensure that regulations and legislation are evolving in tandem with advancements in AI and heuristics. This will foster public confidence and awareness, which are essential for the widespread adoption of autonomous vehicles. The implementation of effective and balanced laws and regulations, taking into account the perspectives of all stakeholders, is vital for the successful integration of autonomous vehicles on a global scale [20]. It is beyond dispute that autonomous vehicles have the potential to transform our society. Nevertheless, the manner in which and the point in time at which they are utilized can exert a considerable influence on the future of autonomous vehicles.

5. Conclusion

The concept of autonomous vehicles and the application of heuristics and artificial intelligence in autonomous vehicles represents a transformative and arguably the highest level of technology in transportation history. This technology has the potential to reshape the entire concept of transportation, making it a process that does not necessarily require human input. Heuristics and artificial intelligence both play a significant role in the development and support of autonomous vehicles. They are both involved in the replacement of the decision-making and execution aspects of transportation, which were previously the domain of humans. The rapidity of decision-making enabled by heuristics and AI's capacity to process intricate and challenging tasks, modify their approach based on past experience, and learn from these interactions are pivotal factors in the practical deployment of autonomous vehicles. Although the development of autonomous vehicles is progressing rapidly, it is challenging to anticipate the rate of growth in the near future. However, it is crucial to address and resolve the significant safety and ethical concerns to ensure that the advancement of autonomous vehicles is not hindered by external factors beyond its technological capabilities. It is also of great importance to the general public that the ethical and safety issues are addressed and resolved in a timely manner. Furthermore, it is imperative that all members of society assume responsibility and collaborate to guarantee that autonomous vehicles serve the public to the best of their abilities. This entails ensuring their safe operation and regulating their use in a manner that avoids controversy while fully leveraging their potential to benefit the public.