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:  Regression 
1. Abstract of Cited Paper (BIB001):  Increasing demands on safety and energy efficiency will require higher levels of automation in transportation systems. This involves dealing with safety-critical distributed coordination. In this paper we demonstrate how a Satisfiability Modulo Theories (SMT) solver can be used to prove correctness of a vehicular coordination problem. We formalise a recent distributed coordination protocol and validate our approach using an intersection collision avoidance (ICA) case study. The system model captures continuous time and space, and an unbounded number of vehicles and messages. The safety of the case study is automatically verified using the Z3 theorem prover. 
2. Abstract of Cited Paper (BIB002):  Autonomous Vehicle (AV) or self-driving vehicle technology promises to provide many economical and societal benefits and impacts. Safety is on the top of these benefits. Trajectory or path planning is one of the essential and critical tasks in operating the autonomous vehicle. In this paper we are tackling the problem of trajectory planning for fully-autonomous vehicles. Our use cases are designed for autonomous vehicles in a cloud based connected vehicle environment. This paper presents a method for selecting safe-optimal trajectory in autonomous vehicles. Selecting the safe trajectory in our work mainly based on using Big Data mining and analysis of real-life accidents data and real-time connected vehicles' data. The decision of selecting this trajectory is done automatically without any human intervention. The human touches in this scenario could be only at defining and prioritizing the driving preferences and concerns at the beginning of the planned trip. Safety always overrides the ranked user preferences listed in this work. The output of this work is a safe trajectory that represented by the position, ETA, distance, and the estimated fuel consumption for the entire trip. 
3. Abstract of Cited Paper (BIB003):  Detecting changing traffic conditions is of primal importance for the safety of autonomous cars navigating in urban environments. Among the traffic situations that require more attention and careful planning, road junctions are the most significant. This work presents an empirical study of the application of well known machine learning techniques to create a robust method for road junction detection. Features are extracted from 3D pointclouds corresponding to single frames of data collected by a laser rangefinder. Three well known classifiers-support vector machines, adaptive boosting and artificial neural networks-are used to classify them into “junctions” or “roads”. The best performing classifier is used in the next stage, where structured classifiers-hidden Markov models and conditional random fields-are used to incorporate contextual information, in an attempt to improve the performance of the method. We tested and compared these approaches on datasets from two different 3D laser scanners, and in two different countries, Germany and Brazil.

Literature Review:
Based Models combined to other techniques A Big Data based method and algorithm has been presented to find the safest-optimal trajectory for fully autonomous vehicles. The method proposed relies strongly on the results obtained from Big Data prediction system which predicts accidents, ETA, and clearance time. The algorithms for checking and calculating the optimal trajectory are very lightweight and straightforward. The simulations using the available data are promising. BIB003 How to prove the correctness of an algorithm for Vehicle Coordination Paper presented a formalisation of the distributed coordination problem encountered by intelligent vehicles while contending for the same physical resource. It formalised a coordination protocol and an intersection collision avoidance case study in the SMT-lib language and proved system safety using the Z3 theorem prover. The two BIB002 to prove correctness and safety of a vehicular coordination problem main conclusions are: (1) The responsibility approach to distributed coordination is a suitable abstraction for formal reasoning on system safety. The core of this approach is that every entity is responsible for making sure that it does not enter an unsafe state with respect to any other entity. This can be contrasted with the other approaches where consensus is required between all nodes, decisions are made by a central manager, or where each pair of nodes negotiates independently, all of which seem problematic from a scalability point of view; (2) The automatic verification of collaborative vehicular applications with the help of SMT solvers is at least plausible. Some cases were found where the model could not be verified and increasing the detail and scale of the model would certainly enlarge this problem. However, there are certainly domain-specific approximations that can be made to alleviate some of these problems. MMS-CH algorithm for calculating the safest path in dynamic environment was presented. It used the construction of convex hulls over the input data to eliminate data points irrelevant for the solution and to use the boundary of the hulls to search for the optimal separation margin between sets of obstacles. The tests showed the algorithm performs well in dynamic scenarios where the input data might be altered by insertion or deletion of data points. The preprocessing phase of the MMS-CH algorithm can recognize whether the change in the data set does or does not require any recalculation of the previous solution and thus prevents unnecessary computations. BIB001 Drivers' manoeuver classification Motion tracking (i.e skeletal tracking) data gathered from the driver whilst driving to learn to classify the manoeuvre being performed Preliminary results show that skeletal tracking data can be used in a driving scenario to classify maneuvers. Thermal infrared classifiers and visible light classifiers were compared and evaluated. Both approaches perform very well. However, the accuracy of the visible light classifiers cannot be reached by thermal infra-red classifiers. But because of the good performance of the thermal infrared classifier under all weather conditions, the performance of the thermal infra-red classifier is acceptable. [ The performance of ANNs, SVMs and AdaBoost were compared for the second step of the method, and of HMMs and CRFs for the last. AdaBoost was considered the best classifier, as it managed to learn the training set without overfitting, generalizing well to the test set. On a frame-by-frame analysis, subsequent use of CRF and HMM do not seem to improve from the results obtained by AdaBoost itself. However, both methods removed a lot of the classification noise, generating an output that allows to more clearly detect the start and end of a road junction. BIB004 Vehicle Cyber Attack Detection System Artificial Neural Networks Paper aimed to address the problem of attack detection and identification when the majority of multiple sensors was attacked in an automotive CPS. LSTM and GRU detected and identified attacks by considering sequential information with real data. It was demonstrated that the accuracy of LSTM is the highest among data-based methods (i.e., Neural Network, SVM, simple RNN, GRU and LSTM). The accuracy of LSTM followed the accuracy of GRU. Especially, LSTM and GRU had superior ability to detect coinstantaneous attacks. LSTM and GRU showed high performance in identification of Class 2, 3, 4, 5 and 6. Although calculation time of GRU is faster than that of LSTM, it is no matter to detect the attacks of the sensors on a general computer to use a CPU.