Abstract
Intelligent transportation systems have been very well received by car companies, people, and governments around the world. The main challenge in the world of smart and self-driving cars is to identify obstacles, especially pedestrians, and take action to prevent collisions with them. Many studies in this field have been done by various researchers, but there are still many errors in the accurate detection of pedestrians in self-made cars made by different car companies, so in the research in this study, we focused on the use of deep learning techniques to identify pedestrians for the development of intelligent transportation systems and self-driving cars and pedestrian identification in smart cities, and then some of the most common deep learning techniques used by various researchers were reviewed. Finally, in this research, the challenges in each field are discovered, which can be very useful for students who are looking for an idea to do their dissertations and research in the field of smart transportation and smart cities.
Highlights
In recent years, intelligent transportation systems have been developed to help reduce the volume of traffic in metropolitan areas, reduce the rate of accidents and injuries and deaths caused by them, reduce fuel consumption, reduce environmental pollution, and so on. ese systems use different technologies for various applications
Fukui et al [26] used complex neural network-based (CNN) methods that are highly accurate in a variety of contexts to identify pedestrians. e researchers proposed a new method proposed in this research based on Convolution Neural Network (CNN) and used Random Dropout and Ensemble Inference Network (EIN) for training and classification, respectively
Tarchoun et al [34] proposed two methods for tracking pedestrians in images taken from moving vehicles: (i) In the first method, the block matching algorithm and block matching features are used to identify pedestrians (ii) e second method uses a faster R-CNN detector to detect pedestrians e proposed methods were implemented using the I2VMVPD database, and the results showed that the first method was able to detect pedestrians in images obtained from moving vehicles in less time but had a higher false positive rate compared to the second method. e second method had better accuracy and performance in pedestrian detection
Summary
Intelligent transportation systems have been developed to help reduce the volume of traffic in metropolitan areas, reduce the rate of accidents and injuries and deaths caused by them, reduce fuel consumption, reduce environmental pollution, and so on. ese systems use different technologies (including IoT, machine learning and data mining, neural networks, deep learning, and image processing) for various applications. Intelligent transportation technology ranges from basic management systems (such as car navigation, traffic signal control systems, variable driving signs, automatic license plate number recognition, or speed camera) to surveillance applications such as advanced CCTV security systems that provide information. From sources such as car park guide information systems, they collect weather information, etc. Intelligent transportation systems can be generalized to different modes of transportation, in which, using automated tools and related scheduling, various types of information receiving and processing operations, as well as traffic management and control, are performed. In the management of critical or accident-prone points, the number of accidents at each critical point is usually too high, so this point is given more importance than the severity of the accident
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