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: Pedestrian Protection Systems: Issues, Survey, and Challenges 
Chapter Title:  1) Stereo Preprocessing: This is the attention focusing stage 
1. Abstract of Cited Paper (BIB001):  The automotive industry and sensor suppliers have responded to the European Commission's request to reduce the road fatalities by 50% until 2010 and they are developing advanced systems for road safety. Short range radar (SRR) sensors in the 24 GHz domain allow coverage of the surroundings of the vehicle, complementing the existing 77 GHz radars used for adaptive cruise control (ACC) systems since 1999. This paper deals with a special variant of pre-crash systems, namely: "pre-crash for vulnerable road users protection". The target is the protection and collision mitigation of pedestrians and bicyclists versus a vehicle. To achieve trigger information for automatic protection systems (like automatic braking or other reversible systems) as well as warning information for drivers, a high performance sensor platform is necessary. This paper presents approaches from the EC-funded project SAVE-U (sensors and system architecture for vulnerable road users protection) of the fifth framework program of the European Commission. The sensor platform consists of short range radar sensors, cameras in the visible and infrared domain. The focus is located on a high- and low-level- data fusion architecture to fulfill the strong requirements. The intension of this paper is mainly to describe the 24 GHz short range radar sensor development for the detection capability of pedestrians in ranges up to 30 m and the sensor fusion technique. 
2. Abstract of Cited Paper (BIB002):  This paper presents first results of the project SAVE-U (Sensors and system Architecture for VulnerablE road Users protection). It has been funded by the European Commission (EC) in the period between 2002 and 2005 (5 th IST framework program). The goal of this project is to enhance the safety of pedestrians in hazardous traffic situations (see Figure 1) (3). To be able to do this, special active countermeasures (actuators) can be integrated into the vehicles. This paper deals with a sensors system which is able to deploy such actuators in critical situations in a suitable way for the driver. Radar sensors (SiemensVDO/ Germany), color cameras and infrared cameras (CEA/ France) mounted into demonstrator vehicles observe the scene in the frontal area. The information of the individual sensors is fused and applied to a deployment algorithm for a braking system. In case of a dangerous situation the vehicle brakes automatically to decrease the vehicle speed and to increase the safety of the pedestrian. Two car manufacturers (Volkswagen (see Figure 4) (3) and DaimlerChrysler (9), both Germany) equipped their cars with this sensor platform for recognizing pedestrians and developed algorithms to provide their individual protection strategy. Both vehicles are tested by MIRA/ UK. This paper presents the first results of tests with the SAVE-U system in the Volkswagen vehicle. 
3. Abstract of Cited Paper (BIB003):  Detecting people in images is key for several important application domains in computer vision. This paper presents an in-depth experimental study on pedestrian classification; multiple feature-classifier combinations are examined with respect to their ROC performance and efficiency. We investigate global versus local and adaptive versus nonadaptive features, as exemplified by PCA coefficients, Haar wavelets, and local receptive fields (LRFs). In terms of classifiers, we consider the popular support vector machines (SVMs), feedforward neural networks, and k-nearest neighbor classifier. Experiments are performed on a large data set consisting of 4,000 pedestrian and more than 25,000 nonpedestrian (labeled) images captured in outdoor urban environments. Statistically meaningful results are obtained by analyzing performance variances caused by varying training and test sets. Furthermore, we investigate how classification performance and training sample size are correlated. Sample size is adjusted by increasing the number of manually labeled training data or by employing automatic bootstrapping or cascade techniques. Our experiments show that the novel combination of SVMs with LRF features performs best. A boosted cascade of Haar wavelets can, however, reach quite competitive results, at a fraction of computational cost. The data set used in this paper is made public, establishing a benchmark for this important problem

Literature Review:
that is based on the study in BIB001 , which computes the depth map and divides it into N discrete layers. These are scanned using windows of appropriate sizes to obtain regions of interest where pedestrians are likely. 2) Chamfer matching: The distance transform of original image is formed, and pedestrian templates are matched in a hierarchical coarse-to-fine manner to efficiently lock into desired objects. 3) Texture classification: This stage uses a neural network with LRFs on image patches as inputs to verify the Chamfer system detections. This particular approach was chosen based on evaluations in BIB004 . 4) Stereo verification: This stage filters out false detections using dense cross correlation between left and right images in disparity search range. 5) Tracking: A simple α-β tracker is used on 2.5-D bounding boxes of the objects in order to improve reliability of the detection results. 6) Risk assessment and driver warning: Estimates the risk for each pedestrian based on the position and time to collision and gives a warning alarm when the risk exceeds a certain limit. Special attention is paid to the integration of modules to incorporate as much information as possible from the previous module into the next module. Performance of individual modules is characterized using receiver operating curves (ROCs) that plot the detection rate against false alarm rate. A sequential optimization technique has been developed for combining the individual ROCs to optimize the performance of the complete system. This avoids ad hoc tuning of parameters. The system has been extensively tested in difficult urban traffic conditions. It achieves false alarm rate of 3.6 per minute while maintaining a detection rate of 62%-78% with a 162-ms average processing time. However, for pedestrians in "risky" positions, i.e., those within the lateral offset of 1.5 m (instead of 4 m), where detection is most important, the system gave much better performance of 90%-100% detection with one false alarm per 3 min. It was also observed that stereo gives an order of magnitude improvement in performance compared to monocular vision. However, they contend that more research is still needed to bring these rates to acceptable levels for real-world use. The system described in BIB003 and BIB002 has been built by Volkswagen in the SAVE-U extension of the PROTECTOR project. It uses RADAR sensors, color cameras, and IR cameras to detect pedestrians in front of the car and to apply automatic braking to reduce the vehicle speed and, therefore, the severity of injury to the pedestrian. The RADAR network provides the range and the azimuth of the objects, enabling the generation of their trajectories. The cameras are used to identify the pedestrians. Sensor fusion is performed using a mix between RADARdriven fusion (sequential) and cooperative fusion (parallel). Detection is performed separately in individual sensors. The fusion algorithm combines the detections of five RADAR beams. The visible light and IR detections are also fused together. The targets detected by the RADAR are then sent to the imaging sensors, which then try to check in the corresponding image areas if a target is present. The targets obtained are combined at pixel level to generate the regions of interest. Image-based classification is then used to distinguish between pedestrians and other objects.