As the intelligent car-networking represents the new direction of the future vehicular development, automotive security plays an increasingly important role in the whole car industry chain. On condition that the accompanying problems of security are proofed, vehicles will provide more convenience while ensuring safety. Security models can be utilized as tools to rationalize the security of the automotive system and represent it in a structured manner. It is essential to improve the knowledge about security models by comparing them besides proposing new methods. This paper aims to give a comprehensive introduction to the topic of security models for the Intelligent Transport System (ITS). A survey of the current methodologies for security modeling is conducted and a classification scheme is subsequently proposed. Furthermore, the existing framework and methods to build automotive security models are broadly examined according to the features of automotive electronic system. A number of fundamental aspects are defined to compare the presented methods in order to comprehend the automotive security modeling in depth.
Based on camera and millimeter wave radar data fusion, an intelligent vehicle obstacle detection method suitable for hazy environment is proposed. Firstly, by taking the effectiveness of obstacle detection after image dehazing as the evaluation standard, a series of typical dehazing networks are compared and the best one was selected for image preprocessing. An obstacle detection model based on YOLOv5s depth network was established; Then, the camera data and radar data are fused in time and space, and the sensor data is associated based on the global nearest neighbor data association algorithm. Finally, the effectiveness of the proposed method is verified by open source data sets and real vehicle experiments.
Traffic intelligence has become an important part of the development of various countries and the automobile industry. Roadside perception is an important part of the intelligent transportation system, which mainly realizes the effective perception of road environment information by using sensors installed on the roadside. Vehicles are the main road targets in most traffic scenes, so tracking a large number of vehicles is an important subject in the field of roadside perception. Considering the characteristics of vehicle-like rigid targets from the roadside view, a vehicle tracking algorithm based on deep learning was proposed. Firstly, we optimized a DLA-34 network and designed a block-N module, then the channel attention and spatial attention modules were added in the front of the network to improve the overall feature extraction ability and computing efficiency of the network. Next, the joint loss function was designed to improve the intra-class and inter-class discrimination ability of the tracking algorithm, which can better discriminate objects of similar appearance and the color of vehicles, alleviate the IDs problem and improve algorithm robustness and the real-time performance of the tracking algorithm. Finally, the experimental results showed that the method had a good tracking effect for the vehicle tracking task from the roadside perspective and could meet the practical application demands of complex traffic scenes.
The ‘new four modernizations’ of automobiles with the trend of electrification, intelligence, network connection and sharing has brought new challenges to the electrical/electronic architecture of vehicles. The topology of the vehicle has changed from distributed ECU development to the centralized oriented domain controllers and central computers; the function of the whole vehicle has been changed from decentralized software development to the development of a unified software architecture for aggregation. This paper studies the Ethernet-based on-board domain controller and expounds the research method for the unified architecture platform. Based on the SOA model, it discusses the definition of services based on SOME/IP, the software architecture, the hardware topology, and communication layer parameters; and builds a domain controller hardware platform, and simulates the development content in the later stage to form a complete Development process of Ethernet-based on-board domain controller.
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