## 3 Physical and Mixed Safety
### Pedestrian Safety  
Pedestrians are the least-protected road users. They do not have any safety measures in the case of a possible accident. Because of this, any traffic incident involving them is very dangerous. Therefore, this is a very important topic for road safety research. According to data from the World Health Organization (2021), there have been about 20-50-million fatalities and injuries in road accidents worldwide. As many as 19% involve fatal accidents involving pedestrians [58]. In Poland, 28,660 people were injured in 2021. Pedestrians accounted for 16.9% of all those injured. Based on the data [59], only 24.8% of all accidents involving pedestrians were their fault. This means that systems that detect people and their potential behavior can help prevent as many as 72.7% of accidents. If we look at the statistics for the location of accidents, it turns out that most, 71.9%, involve intersections and pedestrian crossings. This confirms the validity of the research and ANNs' continued improvements in detecting people. However, in this case, we have stricter requirements. They mainly rely on accuracy and real-time performance, as this directly impacts the field of autonomous vehicles and smart cities. The constant pursuit of the perfect detection ofpedestrians and other objects has resulted in the development of different approaches to this topic [60]. The Convolutional Neural Network (CNN) approach will be discussed first. CNNs use image recognition and classification to detect objects, recognize faces, etc. The proposed improvement involves [61] automatically optimizing the feature representation to the detection task and regularizing the neural network. The accuracy of the Support Vector Machine (SVM) classifier using the features learned by the CNN is equivalent to the accuracy of the CNN, confirming the importance of automatically optimized features. However, the computational demand of the CNN classifier is more than an order of magnitude lower than that of the SVM, irrespective of the type of features used. The next group discussed is the R-CNN, Fast R-CNN, and Faster R-CNN. The Region-based Convolutional Neural Network is based on the proposal of regions that are used to locate objects within the input image. [PERSON] et al. [62,63] proposed extracting 2000 region proposals from images and processing them. The problem with such a solution is the long processing time of 2000 region proposals. It is impossible to work in real-time because processing a single image takes a few to tens of seconds. In addition, the selective search algorithm is a fixed algorithm. Therefore, we cannot improve the quality of the candidate region proposals. Based on this knowledge, the proposal to improve [64] the R-CNN was to modify this algorithm. The new approach of the region proposal method was based on the algorithm in [65]. Such a modification makes it possible to significantly improve the operation of the entire method. The next step for the R-CNN method was for the author to build a faster object-detection algorithm, and the new method was named Fast R-CNN. Here, we do not determine 2000 region proposals. Instead, the operation feeds the input image to the CNN to generate a convolutional feature map. Based on such a map, proposal regions are determined and are wrapped into squares. After transforming them to a fixed size, we feed them to the fully connected layer. We can successfully use this model to detect pedestrians [66]. One modification that improves the method's pedestrian-detection capability is using the EdgeBoxes algorithm to generate effective region proposals from the image [67]. The quality of these regions significantly affects detection performance. Also added is a batch normalization layer between the convolutional layer and the activation function layer. Due to the wide variety of features in pedestrian detection in natural scenes, it was noted that a divide-and-conquer philosophy could mitigate them. Based on this, the Scale-Aware Fast R-CNN (SAF R-CNN) method was proposed [68].