## 5 Discussion and Future Works
### Secure the Perception and Operation of CAVs  
Fully autonomous vehicles operate without driver assistance or monitoring. Accurately perceiving the surroundings and understanding the context, such as the road conditions [113] and the weather status, would be the prerequisite for the safe driving of CAVs. The accurate perception lies in the robust sensing of its surrounding environments and the reliable fusion of information. For CAVs, the sensing ability is possibly impacted by malicious attacks via spoofing/deception attacks (e.g., faked GPS signals) to generate fraud or unreliable data. Additionally, attackers can delay the acquisition and transmission of the data via DoS attacks to disable the delay-sensitive applications, and thus, threaten the vehicle safety. Recently, due to the development of artificial intelligence (AI) technology, the perceiving capabilities of sensors can be enhanced to improve the detection rate and decrease the error rate [114; 115]. A typical example is that the detection accuracy of cameras can be improved using neural network tools and methods [116; 117; 118]. Therefore, the advancement in AI technology, to an extent, can make the sensors more robust to malicious attacks. On the other hand, coupling different sensors and fusing multi-sources data would be indispensable for a CAV to accurately perceive the surrounding environment [118]. For instance, stereo vision uses an overlapping region of two cameras to determine depth; Google Driverless Car fuses LiDAR with stereo-vision and Enhanced Maps (E-maps) for road scenery understanding. Besides multiple sensors, with the introduction of vehicle connectivity, vehicle communication provides another information source for data fusion. In [119], data from long-range radar and the VANET network are associated and fused to provide accurate data of tracked objectives in front of the vehicle. Traditionally, the Kalman filter and particle filter can be used for data fusing of multiple sources [120; 121]. Presently, some machine learning methods are also applied in data fusion processes, such as SVM classification, unsupervised clustering, etc. However, these methods are usually time/resource-consuming. Meanwhile, received data from multiple sources are within different coordinate systems and use unsynchronized clocks. Spatial and temporal alignment of incoming data is needed before data is further fused [38; 118]. Therefore, a \"lightweight\" and efficient fusion algorithm is still needed. In addition, multi-modality fusion needs to deal with some issues in practice when they are implemented. For example, information from the GPS was not available when the vehicle was under tunnels or bridges; information from one sensor source is possibly blocked by malicious attackers. Therefore, reliable fusion is needed to handle these situations. Furthermore, attack-aware and error-tolerant end-to-end learning can be developed for fully autonomous operations. Currently, autonomous vehicles mainly utilize module-based learning to achieve some functions, such as object detection and pattern recognition. Recent efforts have been dedicated to applying end-to-end learning in autonomous driving. Typically, the operation mode of autonomous driving goes through three steps: perception, understanding, and planning [122; 123; 124]; one level up, end-to-end learning can be used to innovatively update the operation mode, which shortens the process with perception and action [125; 126; 113]. It indicates that the new driving mode takes the raw data as input, such as the image, and outputs control commands directly. In [127], [PERSON] trained a convolutional neural network to map raw pixels from a single front-facing camera directly to steering commands. Undoubtedly, the application of end-to-end learning in autonomous vehicles shows great advantages in enhancing vehicular operations and reducing the perception-control period. However, neural networks are proven to be easily fooled or perturbed by interferences [128].