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:  5% 
1. Abstract of Cited Paper (BIB001):  Recently several artificial intelligence labs have suggested the use of fully equipped vehicles with the capability of sensing the surrounding environment to enhance roadway safety. As a result, it is anticipated in the future that many vehicles will be autonomous and thus there is a need to optimize the movement of these vehicles. This paper presents a new tool for optimizing the movements of autonomous vehicles through intersections: iCACC. The main concept of the proposed tool is to control vehicle trajectories using Cooperative Adaptive Cruise Control (CACC) systems to avoid collisions and minimize intersection delay. Simulations were executed to compare conventional signal control with iCACC considering two measures of effectiveness - delay and fuel consumption. Savings in delay and fuel consumption in the range of 91 and 82 percent relative to conventional signal control were demonstrated, respectively. 
2. Abstract of Cited Paper (BIB002):  This study aims at developing a method that predicts the CPU usage patterns of software tasks running on a self-driving car. To ensure safety of such dynamic systems, the worst-case-based CPU utilization analysis has been used; however, the nature of dynamically changing driving contexts requires more flexible approach for an efficient computing resource management. To better understand the dynamic CPU usage patterns, this paper presents an effort of designing a feature vector to represent the information of driving environments and of predicting, using regression methods, the selected tasks' CPU usage patterns given specific driving contexts. Experiments with real-world vehicle data show a promising result and validate the usefulness of the proposed method. 
3. Abstract of Cited Paper (BIB003):  Automatic detection of traffic lights has great importance to road safety. This paper presents a novel approach that combines computer vision and machine learning techniques for accurate detection and classification of different types of traffic lights, including green and red lights both in circular and arrow forms. Initially, color extraction and blob detection are employed to locate the candidates. Subsequently, a pretrained PCA network is used as a multiclass classifier to obtain frame-by-frame results. Furthermore, an online multiobject tracking technique is applied to overcome occasional misses and a forecasting method is used to filter out false positives. Several additional optimization techniques are employed to improve the detector performance and handle the traffic light transitions. When evaluated using the test video sequences, the proposed system can successfully detect the traffic lights on the scene with high accuracy and stable results. Considering hardware acceleration, the proposed technique is ready to be integrated into advanced driver assistance systems or self-driving vehicles. We build our own data set of traffic lights from recorded driving videos, including circular lights and arrow lights in different directions. Our experimental data set is available at http://computing.wpi.edu/Dataset.html. 
4. Abstract of Cited Paper (BIB004):  The sole idea of the autonomous driving is to reduce the number of accidents which will be caused by human errors. There will be a phase where autonomous vehicles will have to take part with the human driven vehicles. In this phase autonomous vehicle and vehicle drivers must be careful in driving maneuvers, since it is impossible to just have autonomous vehicles on the road while the transition to fully autonomous driving takes place. To make the system more reliable and robust, authors feel that it must be substantially improved and to achieve this 3 methodologies have been proposed. The use cases are derived from the camera placed in the suitable positions of every vehicle. This creates several use cases with different driving behaviors, which truly reflects the conditions which the autonomous car needs to undergo in series. Authors propose several architectures to address the safety related issues. Since, the number of use cases are so random that it becomes practically impossible to manage data, so we propose to have a secured cloud storage. The cloud provides a secure and reliable data management system to the machine learning algorithm computation. The data management till now is effectively done via trained unique use cases. This make a big data which is encapsulation of different unique use cases under different driving conditions. So we conclude that the usage of ML and cloud based data management is the way forward for handling autonomous vehicle reliably. This may not be the complete solution, but nevertheless we are one step closer towards 2025. This concept can be implemented only in co-operation of the OEMs and Public authorities. 
5. Abstract of Cited Paper (BIB005):  In the field of self-driving technology, the stability and comfort of the intelligent vehicle are the focus of attention. The paper applies cognitive psychology theory to the research of driving behavior and analyzes the behavior mechanism about the driver’s operation. Through applying the theory of hierarchical analysis, we take the safety and comfort of intelligent vehicle as the breakthrough point. And then we took the data of human drivers’ perception behavior as the training set and did regression analysis using the method of regression analysis of machine learning according to the charts of the vehicle speed and the visual field, the vehicle speed and the gaze point as well as the vehicle speed and the dynamic vision. At last we established linear and nonlinear regression models (including the logarithmic model) for the training set. The change in thinking is the first novelty of this paper. Last but not least important, we verified the accuracy of the model through the comparison of different regression analysis. Eventually, it turned out that using logarithmic relationship to express the relationship between the vehicle speed and the visual field, the vehicle speed and the gaze point as well as the vehicle speed and the dynamic vision is better than other models. In the aspect of application, we adopted the technology of multi-sensor fusion and transformed the acquired data from radar, navigation and image to log-polar coordinates, which makes us greatly simplify information when dealing with massive data problems from different sensors. This approach can not only reduce the complexity of the server’s processing but also drives the development of intelligent vehicle in information computing. We also make this model applied in the intelligent driver’s cognitive interactive debugging program, which can better explain and understand the intelligent driving behavior and improved the safety of intelligent vehicle to some extent.

Literature Review:
Road Detection; Road environmental recognition and various objects detection in real driving conditions; How to "automate" manual annotation for images to train visual perception for AVs;  ,  ,  Hough Transformation 3 5% Road Detection; Road environmental recognition and various object detection in real driving conditions; How to "automate" manual annotation for images to train visual perception for AVs; BIB005 ,  ,  Optimiza-tion Heuristics 3 5% Control intersection crossing (all way stop) and op-timization; Autonomous vehicles intersections crossing optimization; Human and Machine Driver Co-existence; BIB002 ,  , BIB005 RegressionBased Models Selective Attention Mechanism; Safe-optimal trajectory selection for autonomous vehicles; Predicts the CPU usage patterns of software tasks running on a self-driving car; BIB006 , BIB001 , BIB003 Principal Componen-ts Analysis (PCA) 2 3% Traffic Light Detection; Road environmental recognition and various object detection in real driving conditions; BIB004 ,  Canny Edge De-tection Algorithm