## 2 Related Works
According to the 3 GPP standard [29], NR-V2X sidelink typically employs distributed resource allocation schemes based on the reservation mechanism, known as the SB-SPS algorithm [30], to obtain low latency communication performance. However, SB-SPS still faces consecutive resource collisions caused by resource allocation conflicts among multiple vehicles [31]. Recently, there have been many achievements in improving the SPS algorithm to alleviate the communication collisions [32; 33; 34]. Reference [32] improves the efficiency of resource utilization, enhances reliability, and reduces latency by designing a vehicle reuse distance during the SPS reselection process. [PERSON] [33] develops an adaptive sidelink open-loop power control (AS-OLPC) algorithm to dynamically adjust transmission power, thereby improving communication reliability in complex urban environments. [PERSON] [34] analyzes the effectiveness of introducing a re-evaluation mechanism at the MAC layer of NR-V2X to avoid collisions caused by packet re-transmissions, though the overall improvement is modest and requires more refined allocation strategies. However, in highly dynamic scenarios, there are uncertain complicated environment changes in the network that will intensify the packet collisions, so the above methods could not be adaptive to solve the communication performance degradation. To overcome this problem, recent studies have increasingly focused on applying reinforcement learning techniques to optimize V2X resource allocation [35; 36; 37]. Existing resource allocation methods often rely on static models, which are unable to adapt in real-time to the dynamic traffic environment. In contrast, reinforcement learning can autonomously adjust, significantly improving resource utilization and system performance. Specifically, Reference [35] uses Q-learning to optimize SPS algorithm parameters, including reservation probability (RP) and reselection counter (\(RC\)), enhance packet reception rate (PRR) and reduce update delay (UD) in high-dynamic vehicular networks. [PERSON] [36] proposes a deep reinforcement learning-based congestion control mechanism that optimizes channel busy rate (CBR) and age-of-information (AoI), showing significant improvements over traditional decentralized congestion control (DCC) algorithms. [PERSON] [37] treats each vehicle as an independent agent and employs a multi-agent deep reinforcement learning (MARL) resource allocation algorithm, enabling vehicles to learn to select resource blocks and transmission power for periodic packet broadcasting. It is worthwhile to note from existing studies that the optimal \(RC\) parameter trained by RL can intelligently reduce consecutive collision probabilities in highly dynamic sidelink communication. Similarly, considering that \(RC\) optimization is crucial to sensing CRLB, this paper will introduce an RL approach to \(RC\) optimization in the JCS sidelink resource allocation to improve sensing performance. Additionally, sidelink collision detection indicates that the dynamic change on channel states can be feasible through full-duplex technology. With advancements in self-interference cancellation (SIC) technology for in-band full-duplex communication [38], some studies have begun using full-duplex antennas to enhance resource allocation mechanisms. Reference [39] utilizes an in-band full-duplex transceiver and its collision detection capabilities to trigger SPS resource reselection, improving communication performance and analyzing the relationship between the collision detection threshold and vehicle density variations. Reference [40] also leverages collision detection capabilities brought about by full-duplex communication to adjust the probability of maintaining the same subchannel for transmission. In short, most of the current resource allocation algorithm improvements only focus on improving communication performance, and there are few references on the joint optimization of sidelink JCS systems.