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:  17% 
1. Abstract of Cited Paper (BIB001):  Nowadays, many attentions have been devoted to autonomous vehicles because the automation of driving technology has a large number of benefits, such as the minimization of risks, the improvement of mobility and ease of drivers. Among many technologies for autonomous driving, road environmental recognition is one of the key issues. In this paper, we present the test results of various object detection algorithms using single monocular camera for autonomous vehicle in real driving conditions. The vision recognition system tested in this paper has three main recognition parts: pedestrian detection, traffic sign and traffic light recognition. We use Histogram of Gradients (HOG) features and detect the pedestrians by Support Vector Machine (SVM). Also features of traffic signs are extracted by Principal Components Analysis (PCA) and canny edge detection is used for traffic lights. These two signals are classified by Neural Network (NN). Algorithms that we tested are implemented in General-Purpose computing on Graphics Processing Units (GPGPU). We show the effectiveness of these methods in real-time applications for autonomous driving. 
2. Abstract of Cited Paper (BIB002):  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. 
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. 
4. Abstract of Cited Paper (BIB004):  Ever increasing autonomy of machines and the need to interact with them creates challenges to ensure safe operation. Recent technical and commercial interest in increasing autonomy of vehicles has led to the integration of more sensors and actuators inside the vehicle, making them more like robots. For interaction with semi-autonomous cars, the use of these sensors could help to create new safety mechanisms. This work explores the concept of using motion tracking (i.e. skeletal tracking) data gathered from the driver whilst driving to learn to classify the manoeuvre being performed. A kernel-based classifier is trained with empirically selected features based on data gathered from a Kinect V2 sensor in a controlled environment. This method shows that skeletal tracking data can be used in a driving scenario to classify manoeuvres and sets a background for further work.

Literature Review:
Road Detection; Robot maneuvers too close to an obstaR cle; Road environmental recognition and various object detection in real driving conditions; Drivers maneuver classification; Traffic Light Detection;  , BIB001 , BIB002 ,  , BIB005 , BIB003 , Prediction of adc vanced driver assistance systems (ADAS) remaining useful life (RUL) for the prognosis of ADAS safety critical components Pedestrian Detection; How to "automate" manual annotation for images to train visual perception for AVs Road junction detection;  ,  ,  , BIB004 Bayesian Artificial Intelligence