The autonomous driving industry has mushroomed over the past decade. Although autonomous driving has undoubtedly become one of the most promising technologies of this century, its development faces multiple challenges, of which security is the major concern. In this article, we present a thorough analysis of autonomous driving security. First, the attack surface of autonomous driving is presented. After an analysis of the operation of autonomous driving in terms of key components and technologies, the security of autonomous driving is elaborated in four dimensions: 1) sensors; 2) operating system; 3) control system; and 4) vehicle-to-everything (V2X) communication. Sensor security is examined from five components, which are mainly responsible for self-positioning and environmental perception. The analysis of operating system security, the second dimension, is concentrated on the robot operating system. Concerning the control system security, the controller area network is approached mainly from vulnerabilities and protection measures. The fourth dimension, V2X communication security, is probed from four categories of attacks: 1) authenticity/identification; 2) availability; 3) data integrity; and 4) confidentiality with corresponding solutions. Moreover, the drawbacks of existing methods adopted in the four dimensions are also provided. Finally, a conceptual multilayer defense framework is proposed to secure the information flow from external communication to the physical autonomous vehicle.

The functional connection patterns of the brain during the processing of human emotions are complex and changeable. Therefore, the use of brain network information at the global scale is not sufficient to reflect the coupling relationship between brain regions, and it is impossible to accurately analyze the differences in information interaction patterns of the brain under different emotions. The purpose of this article is to study the functional connectivity of the brain by using the phase synchronization of EEG (Electroencephalogram) channel information in different emotional states. Considering that the Phase Locking Value (PLV) can effectively reflect the phase synchronization relationship between the EEG channels, it is adopted to describe the functional connection relationship of the brain. After the brain network based on PLV is constructed, considering that the topological structure of the brain network is complex and volatile, we merge the two types of attributes: functional integration and functional separation to analyze the differences in brain connectivity for different emotions. Further, the modular structure of the brain network is constructed through community detection to extract its more comprehensive local characteristics for connectivity analysis. The results show that, compared with positive emotions, brain regions under negative emotions have higher phase synchronization, more complex brain connectivity patterns, and more obvious modular structures. In addition, key cortical brain regions associated with emotional stimulation have been identified as brain network hubs. The verification on the DEAP dataset demonstrates that the analysis framework of this study effectively improved the accuracy of emotion recognition.