## 2 Related Works
In [15], the authors present a DCC algorithm that integrates a priority model and adjusts the beacon transmission rate. This algorithm effectively manages congestion's influence on vehicle safety by guaranteeing the reliable and timely reception of safety information. In [16], the authors present a novel approach to enhance the object filtering process of collective perception by considering DCC awareness. This approach dynamically adjusts the message size based on DCC constraints and, consequently, the message generation rate. The comparison with the existing ETSI design shows that this design improves the perceived quality and reduces the message generation rate. In [17], the authors introduce a traffic density-based congestion control algorithm (TDCCA) that incorporates vehicle IDs into their respective CAMs and utilizes TRC-based DCC to enhance model parameter efficiency. The algorithm considers a range of network conditions, from non-saturated to saturated, as well as sparsely dispersed and congested networks. The proposed approach demonstrates improved performance in terms of PRR and latency. While the majority of conventional DCC algorithms rely on CBR as the criterion for determining control parameters, a single-measurement approach is insufficient due to the myriad factors influencing channel load, which consequently leads to issues such as fairness. In [18], the authors introduce Rate-OPT and Power-OPT algorithms for dedicated short-range communication (DSRC), demonstrating that their coordinated and alternating application results in enhanced channel utilization and packet transmission rates. This integrated congestion control algorithm optimizes channel load usage by dynamically allocating transmission range and rate in response to vehicle density. In [19], the authors introduce a combined transmission power and rate control strategy, which deprioritizes the use of TPC as the primary response mechanism. In contrast to a purely TPC-based approach, this strategy lessens the reliance on precise transmission power adjustments and demonstrates that it can efficiently approximate the optimal control parameter configuration for load adaptation across individual channels. A joint power and rate algorithm in [20], which can set different priorities for different vehicles, introduces fairness into V2V communication, and conducts simulation verification through multiple scenarios. In [21], the authors propose a perception-based hybrid beacon algorithm that utilizes the driver's state as a reference condition and broadcasts it to nearby vehicles, then makes joint decisions to adjust the transmission range and power in order to enhance safety. In [22], the authors propose an approach that integrates real-time traffic flow sensing with channel congestion status, utilizing distributed network utility maximization to improve channel utilization. In [23], the authors introduce the POSACC algorithm, prioritizing location accuracy and communication reliability as paramount metrics. They effectively manage the beacon rate and transmission power, resulting in enhanced efficiency. The HPR-DCC integrates the merits of both TRC and TPC, facilitating improved congestion control and maintaining CBR within the convergence range. TPC allows for power-efficient communication by reducing transmit power when the channel conditions permit, while TRC optimizes the transmission rate to maximize throughput when the channel quality is favorable. This system offers an adaptable balance between energy consumption and network efficiency. However, the most optimal coordination and equilibrium between the TPC and TRC depend on the specific system requisites and the characteristics of the adaptive scenario in which they will operate. Consequently, the full potential of the HPR-DCC is realized through careful consideration and customization of the system according to the specific needs and conditions of the intended application.