You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety 
Chapter Title:  Technique (CT.3) Hits Papers Addressed Problem (CT.4) References 
1. Abstract of Cited Paper (BIB001):  The objectives of this paper are to discuss how vehicle automation technology can be used to benefit car drivers and also to propose a concept of an autonomous highway vehicle which improves highway driving safety. 
2. Abstract of Cited Paper (BIB002):  Automation in transportation (rail, air, road, etc.) is becoming increasingly complex and interconnected. Ensuring that these sophisticated non-deterministic software systems can be trusted and remain resilient is a community concern. As technology evolves, systems are moving increasingly towards autonomy where the “machine” is intelligent: perceiving, deciding, learning, etc. often without human engagement. Our current mechanisms and policies for oversight and certification of these systems to ensure they operate robustly in safety-critical situations are not keeping pace with technology advancements. How is an autonomous system different than an advanced automatic system? How is trust different for an autonomous system? What are different perspectives on trust? Is it appropriate to apply the techniques used to establish trust in a today’s transportation systems to establishing trust in an autonomous system? This paper will examine these questions and propose a framework for discussing autonomy assurance and trust in transportation applications. We will explore further with two examples: 1) the notion of a self-driving taxi-cab; and 2) the evolution of a two-pilot flight deck, to single-pilot operations. 
3. Abstract of Cited Paper (BIB003):  Highly automated driving on highways requires a complex artificial intelligence that makes optimal decisions based on ongoing measurements. Notably no attempt has been performed to evaluate the impacts of such a sophisticated system on traffic. Another important point is the impact of continuous increase in the number of highly automated vehicles on future traffic safety and traffic flow. This work introduces a novel framework to evaluate these impacts in a developed microscopic traffic simulation environment. This framework is used on the one hand to ensure the functionality of the driving strategy in the early development stage. On the other hand, the impacts of increasing automation rates, up to hundred percent, on traffic safety and traffic flow is evaluated. 
4. Abstract of Cited Paper (BIB004):  Traffic engineers, vehicle manufacturers, technology groups, and government agencies are anticipating and preparing for the emergence of fully automated vehicles into the American transportation system. This new technology has the potential to revolutionize many aspects of transportation, particularly safety. However, fully automated vehicles may not create the truly crash-free environment predicted. One particular problem is crash assignment, especially between automated vehicles and nonautomated vehicles. Although some researchers have indicated that automated vehicles will need to be programmed with some sort of ethical system in order to make decisions on how to crash, few, if any, studies have been conducted on how particular ethical theories will actually make crash decisions and how these ethical paradigms will affect automated vehicle programming. The integration of three ethical theories—utilitarianism, respect for persons, and virtue ethics—with vehicle automation is examined, and a simple progr... 
5. Abstract of Cited Paper (BIB005):  Today research is going on within different essential functions need to bring automatic vehicles to the roads. However, there will be manual driven vehicles for many years before it is fully automated vehicles on roads. In complex situations, automated vehicles will need human assistance for long time. So, for road safety driver monitoring is even more important in the context of autonomous vehicle to keep the driver alert and awake. But, limited effort has been done in total integration between automatic vehicle and human driver. Therefore, human drivers need to be monitored and able to take over control within short notice. This papers provides an overview on autonomous vehicles and un-obstructive driver monitoring approaches that can be implemented in future autonomous vehicles to monitor driver e.g., to diagnose and predict stress, fatigue etc. in semi-automated vehicles. 
6. Abstract of Cited Paper (BIB006):  RobIL program is a collaborative effort of several leading Israeli academic institutions and industries in the field of robotics. The current objective of the program is to develop an Autonomous Off-Road Unmanned Ground Vehicle (UGV). The intention is to deal through this project with some of the most urgent challenges in the field of autonomous vehicles. One of these challenges, is the lack of efficient Safety Performance Verification technique, as the existing tools for hardware and software reliability and safety engineering do not provide a comprehensive solution regarding algorithms that are based on Artificial Intelligent (AI) and Machine Learning. In order to deal with this gap a novel methodology that is based on statistical testing in simulated environment, is presented. In this work the RobIL UGV project with special emphasis on the performance and safety verification methodology is presented. 
7. Abstract of Cited Paper (BIB007):  Abstract: Growing research progress in Internet of Things (IoT), automated/connected cars, Artificial Intelligence and person's data acquisition (Quantified Self) will help to reduce behavioral uncertainties in transport and unequivocally influence future transport landscapes. This vision paper argues that by capitalizing advances in data collection and methodologies from emerging research disciplines, we could make the driver amenable to a knowable and monitorable entity, which will improve road safety. We present an interdisciplinary framework, inspired by the Safe system, to extract knowledge from the large amount of available data during driving. The limitation of our approach is discussed. 
8. Abstract of Cited Paper (BIB008):  The future will see autonomous machines acting in the same environment as humans, in areas as diverse as driving, assistive technology, and health care. Think of self-driving cars, companion robots, and medical diagnosis support systems. We also believe that humans and machines will often need to work together and agree on common decisions. Thus hybrid collective decision making systems will be in great need. ::: ::: In this scenario, both machines and collective decision making systems should follow some form of moral values and ethical principles (appropriate to where they will act but always aligned to humans'), as well as safety constraints. In fact, humans would accept and trust more machines that behave as ethically as other humans in the same environment. Also, these principles would make it easier for machines to determine their actions and explain their behavior in terms understandable by humans. Moreover, often machines and humans will need to make decisions together, either through consensus or by reaching a compromise. This would be facilitated by shared moral values and ethical principles. 
9. Abstract of Cited Paper (BIB009):  Mobility is a basic necessity of contemporary society and it is a key factor in global economic development. The basic requirements for the transport of people and goods are: safety and duration of travel, but also a number of additional criteria are very important: energy saving, pollution, passenger comfort. Due to advances in hardware and software, automation has penetrated massively in transport systems both on infrastructure and on vehicles, but man is still the key element in vehicle driving. However, the classic concept of 'human-in-the-loop' in terms of 'hands on' in driving the cars is competing aside from the self-driving startups working towards so-called 'Level 4 autonomy', which is defined as "a self-driving system that does not requires human intervention in most scenarios". In this paper, a conceptual synthesis of the autonomous vehicle issue is made in connection with the artificial intelligence paradigm. It presents a classification of the tasks that take place during the driving of the vehicle and its modeling from the perspective of traditional control engineering and artificial intelligence. The issue of autonomous vehicle management is addressed on three levels: navigation, movement in traffic, respectively effective maneuver and vehicle dynamics control. Each level is then described in terms of specific tasks, such as: route selection, planning and reconfiguration, recognition of traffic signs and reaction to signaling and traffic events, as well as control of effective speed, distance and direction. The approach will lead to a better understanding of the way technology is moving when talking about autonomous cars, smart/intelligent cars or intelligent transport systems. Keywords: self-driving vehicle, artificial intelligence, deep learning, intelligent transport systems.

Literature Review:
General AI/ML 14 24% AV Validation; Challenge with validating machine-learning based systems to the ultra-dependable levels required for autonomous vehicle; Coexistence Human Machine Controller; Driving Car Tasks Classification; Lack of efficient Safety Performance Verification technique when AI/ML is used; Crash assignment, especially between automated vehicles and non-automated vehicles; Reducing the uncertainty of a driver behavior prediction model; Integration between automatic vehicle and human driver; How the vehicle autonomy technology can be used to benefit car drivers and t o improve highway driving safety by a concept of an autonomous highway vehicle; AV decisions in complex dilemmas as a social agent; Hybrid (humans and machines) collective decision making systems (work together and agree on common decisions); Autonomy assurance and trust (CERTIFICATION PROA CESS) in Automated Transportation Systems; Evaluating the impacts of the number of highly automated vehicles on future traffic safety and traffic flow; Enhancing Image Understanding. [64],  ,  , BIB010 , BIB007 , BIB005 , BIB008 , BIB006 , BIB002 ,  , BIB009 , BIB003 , BIB004 , BIB001 Artificial Neural Networks (ANN)