# A Review of Cognitive Control: Advancement, Definition, Framework, and Prospect
## 2 Cognitive Control Research Overview
### Cognitive Control Based on CDS
#### 2.3.3 CRC for Vehicular Radar and Communication Systems  
Connected and autonomous vehicles (CAVs) are an integral part of intelligent transportation systems, and they have profoundly changed people's transportation patterns and lives. With the development of mobile communication, artificial intelligence, and high-performance computing technologies, the automation level of CAV is increasing.  
Vehicular radar is indispensable for CAV and its advanced driver assistance system [117]. It can sense road conditions and track targets and plays a significant role in adaptive cruise control, lane-changing assistance, and automatic parking assistance [118]. In addition to vehicular radar, vehicle-to-vehicle (V2V) communication is also an essential function of CAV, which makes tasks such as positioning and navigating more collaborative [119]. However, the driving environment of CAVs is imperfect; radar sensing and V2V communication can be declined by process and measurement noise, complex road conditions, and cyberattacks.  
In [120], CRC was used for the adaptive selection of waveform parameters of the vehicular radar. When a CAV was only affected by process and measurement noise, the vehicular radar waveform parameters were adjusted by the executive part of the CRC. Once other uncertainties occur, such as structural uncertainty, the disturbed cognitive action is modified by the classifier following the shortest distance principle. In [121], the state information of other vehicles was used to formulate an extended state-space equation, based on which the accuracy of the entropic state calculation and optimization of vehicular radar waveform parameters were further improved.  
In [122], CRC was applied to anti-jamming V2V communications with hybrid attackers. The transmission power and channel were reselected dynamically in each PAC to enhance the transmission success rate of the V2V communication under jamming attacks. In [123], the attacker's eavesdropping on the V2V communication and the attack mode prediction made by the transmitting vehicle were further considered, and both results were incorporated into a utility function in the form of probability matrices to enhance the CRC system efficiency.  
In [124], a coordinated CRC approach was proposed. Both the vehicular radar and the V2V communication system were considered separate CRC models with their own risk-sensitive subsystems. By establishing a mutual assistance relationship, the coordinated CRC provided a new safety mechanism that allowed one system to learn from and react to the risks that the other system has encountered.  
In summary, CDS is a structured engineering system initially inspired by cognitive radar and cognitive radio, and its architecture is constructed in accordance with [PERSON]'s cognition paradigm, embodying four aspects of cognition: PAC, memory, attention, and intelligence. Cognitive control and CRC are extensions of CDS and have been applied to CBTC systems, smart grids, and vehicular radar and communication systems. These applications are based on the architectures depicted in Figures 16 and 17. The differences among these applications are the algorithms employed in the perceptual part, the executive part, and the risk-sensitive subsystem, and the cognitive actions are also different according to the corresponding scenarios. In general, the Bayesian filter algorithm is primarily used as an estimation algorithm, whereas various reinforcement learning algorithms are widely used in cognitive controllers. In fact, cognitive control and CRC have also been applied to other fields, such as the cognitive Internet of Things; however, the related research contents are not attributed to the automatic control domain, which is beyond the scope of this paper. Table 3 summarizes the references in this subsection according to their research content.