in cellular and vehicular networks, among others. Despite the prospects, mmWave communications are prone to high losses due to blockage by various obstacles, such as buildings and other objects in the environment. For vehicle-to-everything (V2X) scenarios, such as vehicle-to-vehicle (V2V) [1; 2] and vehicle-to-infrastructure (V2I) [3; 4; 5] scenarios, the obstacles include pedestrians, sign posts, and other vehicles in the environment. These obstacles can cause significant signal attenuation, in addition to the inherently-high mmWave path losses, which can make it challenging to maintain reliable communication links in mmWave networks. To mitigate these challenges and achieve sufficient received signal strength, various techniques such as directional beamforming and beam management techniques or procedures (i.e., beam alignment, training, tracking, pairing, steering, selection, prediction, etc.) are employed [3]. These techniques can help to extend the coverage area and ensure reliable communication with optimal beam alignment [6]. Beam management is a key functionality in communication networks in ensuring sufficient received signal strength and facilitating reliable communication. It is an integral feature of 5G cellular and vehicular networks and standards. It allows the transmitter (TX) and receiver (RX) to align beams and direct energy towards directions of interest, which reduces interference and maximizes spectral and energy efficiencies [7]. Beam management is more critical at the high frequency mmWave frequencies and in high-mobility scenarios and use cases. Using the downlink, for example, beam alignment and training is a key step in initial access, which is a process by which the RX establishes a physical connection with the TX before any downlink data can be sent [3]. Over the years, beam management has been explored using classical approaches. For example, some of the traditional beam management schemes in the 3 rd Generation Partnership Project (3 GPP) standards utilize the reference signal received power (RSRP) measurements to determine the optimal beam pairings. These traditional schemes consume wireless resources and incur high beam training overheads in finding the best beam pairings, thus necessitating alternative approaches such as position-aided, vision-aided, or, more generally, sensing-aided beam prediction approaches. Further, there is the increasing adoption of the \"artificial intelligence (AI) for wireless\" paradigm in communication systems, where several challenges, including beam management challenges, are now being addressed using AI or machine learning (ML) approaches [7]. In the vehicular network domain, intelligent transport systems such as autonomous driving have stringent performance requirements with respect to data rates, reliability, and latency, among others. In such systems, the vehicles use different types of sensors and generate large volumes of data that can be shared and used to enhance road safety, improve traffic management, and support infotainment and other allied services [5]. Over time, different algorithms and protocols have been developed to enable the exchange of safety messages or sharing of perception data and environmental maps through the vehicles, infrastructure, or network for intelligent and efficient coordination among vehicles, pedestrians, and other road users. Dedicated short range communication (DRSC) and 4G V2X have been the legacy systems used for vehicular networks. However, to support gigabits-per-second rates required for the intelligent transport and connected vehicular networks beyond the capabilities of DRSC and 4G V2X, the mmWave bands can provide a large spectrum to enable the exchange of raw sensor data that can facilitate improved vehicular communication operations [8]. However, to extend range and facilitate reliable communication with sufficient received signal power, mmWave systems must employ large antenna arrays with narrow beams and high directivity.