## 1 Introduction
Especially, in high-density scenarios, the probability of selecting the same resource for different vehicles quickly rises, resulting in the deterioration of both communication and sensing performances. In fact, obtaining knowledge about the dynamic echo channel state of JCS is still a big challenge and difficult to resolve. Therefore, it is rational for this paper to study the consecutive collision problem related to echo together with transmission signals of the JCS sidelink. With the advancements in self-interference (SI) technology in recent years [9], simultaneous transmission and reception on the same frequency band with in-band full-duplex (FD) transceivers have become feasible, offering hope for the implementation of JCS systems in the sidelink. Additionally, the powerful sensing capabilities of full-duplex bring collision detection functionality, creating new opportunities for enhancing sidelink resource allocation schemes. Moreover, in response to the high dynamics of V2X traffic density and network load, inspired by references [10; 11], reinforcement learning and other intelligent algorithms can be employed to optimize resource allocation, ensuring the stability and accuracy of communication and sensing tasks. Inspired by the above, this paper focuses on high-positioning accuracy, low-latency and high-reliability in the 5G NR-V2X sidelink JCS system. By studying the comprehensive impact of interference due to consecutive collisions, we propose a reinforcement learning-based collision mitigation resource allocation scheme (CCM-SPS). Specifically, this scheme employs JCS full-duplex collision detection and reinforcement learning to optimize traditional SB-SPS parameters, mitigating performance degradation from consecutive collisions. Furthermore, the impact of varying vehicle density and packet sizes on JCS performance in dynamic vehicular networks is discussed. Finally, the effectiveness of the proposed scheme in enhancing overall performance is validated using a V2X sidelink system-level simulator. The main contributions of this work can be summarized as follows:  
* In order to address the conflicting requirement between sensing accuracy and communication reliability for sidelink resources, a novel collision mitigation resource allocation scheme is proposed. The algorithm integrates the full-duplex detection capability of JCS with the resource sensing reservation process of the traditional SB-SPS scheme. This allows vehicles to dynamically optimize reservation times based on sensing channel information, effectively reducing consecutive packet collisions and enhancing the overall utilization efficiency of resources in the sidelink JCS system. * The proposed CCM-SPS and traditional SB-SPS's performance is theoretically analyzed in scenarios of a variety of vehicle densities and packet sizes. Through reinforcement learning, comprehensive optimization of resource utilization for sensing and communication is achieved. * Comprehensive evaluations are performed using the Cramer-Rao Lower Bounds (CRLB), packet reception rate (PRR) and update delay (UD). The novel scheme shows comparative advantages in positioning accuracy, latency, and reliability performance indicators over a comparative scheme. The remainder of the article is organized as follows. In Section 2, a review of recent literature is conducted, and in Section 3, a theoretical analysis is conducted on the performance of the sidelink JCS system. In Section 4, the consecutive collision problem is analyzed using a traditional resource allocation scheme. Then, in Section 5, the specific implementation of the improved resource allocation scheme is introduced, including full-duplex collision detection and a Q-learning based collision mitigation scheme. The extensive results are presented in Section 6, which analyzes the performance indicators in various scenarios. Section 7 presents the concluding remarks.