## 1 Introduction  
One of the most challenging aspects of automotive applications is the configuration of mmWave antenna arrays. Because of its high mobility, the vehicle will be intermittently blocked by other objects during driving, which results in blind spots that are covered by beams. To maintain a high-speed transmission link, the solution is to use frequent beam alignment. Two requirements for the transmission link in in-vehicle applications are low overhead and low latency, and the solutions that are currently employed in IEEE802.11 ad are insufficient to meet these requirements [1, 2]. However, the only solution that supports massive data sharing is the millimeter wave, and not only that, but the millimeter wave also has a nonnegligible value for 5G in-vehicle cellular communication and infotainment services [3, 4]. Therefore, to realize the rapid configuration of mmWave links, it is necessary to design a low-overhead method with high efficiency and high robustness at the same time. The utilization of out-of-band side information in mmWave vehicular networks is an alternative approach to simplifying beamforming [5]. There are many sensors, such as GPS, visible light radar, lidar, and other sensors around the vehicle, and information is exchanged between various sensors [6] so that the vehicle has the ability of situational awareness. It can be seen from this that side information exists in various parts of the intelligent transportation system. In [7], the data of sensors and other systems are not only trained by a millimeter-wave beam, but they are also assisted by out-band information. In [8], it was shown that the sub6 GHz band can be used for beam selection and channel estimation because of the channel consistency between the mmWave and sub6 GHz bands. A beam-alignment scheme is proposed, which is achieved by obtaining effective intelligence from radar signals when configuring and designing the vehicle beam. A method based on inverse fingerprinting is introduced to select the best pair of beams. Just give the location of the receiver, and the beam arrangement for that location is provided by the infrastructure on the basis of the frequency of occurrences in the dataset for that location [8]. The mmWave beam selection with time-dependent vehicle-movement trajectories is addressed by a structure for yielding 5G MIMO datasets of exploding ray tracing, and deep learning is employed for assisted selection. The millimeter-wave antenna array has high gain and uses more antennas, which leads to high pilot overhead and computational complexity. In order to solve these challenges, scholars propose the use of the method based on deep learning to realize the channel estimation [9; 10; 11], and deep learning has great potential to reduce overhead [12]. In Ref. [13], the authors propose a novel machine-learning-based method for concurrent transmission in mmWave vehicle-to-vehicle communications. Such an approach can enable effective analog beam selection and the maintenance of low computational complexity. By combining reconfigurable-intelligent-surface (RIS) technology with downlink multiuser communication, the authors of [14] developed a hybrid beamforming scheme to facilitate the sum-rate performance improvement for RIS-based systems. In order to reduce the overhead, the authors of [15] propose a beam-selection scheme that is based on deep learning that randomly selects some beam measurements, and then uses the machine-learning model to estimate the quality of the entire beam. The performance of this method is obviously constrained by the beam search area, and it cannot make full use of the spatial correlation of the beam quality. In this paper, we propose a novel machine-learning framework that leverages intelligent situational awareness to assist in mmWave beam-power prediction. In a vehicular environment, moving vehicles are the main moving reflectors in cities. Vehicle Intelligent Situational Awareness, as an aid, corresponds to the acquired power of several beams.