VANET is an emerging area of wireless ad-hoc networks to contribute in the success of connected vehicles projects. The extremely changeable number of mobile nodes and high mobility are challenging issues. Furthermore, this particular network has several problems in term of defining suitable schemes and protocols like rate adaptation mechanisms. The overall performance of diverse applications in VANET such as traffic control and multimedia delivery is based on the achievement ratio these networks can offer and the network throughput. Rate adaptation is an essential technique to evade the performance network degradation and to maximize the throughput by using the estimation of the present channel characteristics and determining the optimal bitrate for subsequent transmissions. Despite there are several available rate control algorithms for 802.11 WLANs standards, there are few works devoted to the rate adaptation for the standard of vehicular networks. In this paper, we compare and evaluate the existing data rate adaptation schemes in numerous vehicular environments to recognize their behavior and analyze their performance in diverse scenarios. Six algorithms were chosen for comparison using the NS-3 simulator: AARF, AARF-CD, AMRR, CARA, Onoe, and Minstrel. The simulation outcomes demonstrate that Minstrel algorithm outperforms the remaining five mechanisms in dense and dynamic situations.

VANET是无线自组织网络的新兴领域，可为联网车辆项目的成功做出贡献。移动节点的数量极其多变和高度移动性是具有挑战性的问题。此外，该特定网络在定义诸如速率自适应机制之类的合适方案和协议方面存在若干问题。VANET中各种应用程序（例如流量控制和多媒体传递）的整体性能取决于这些网络可以提供的实现比率和网络吞吐量。速率适配是通过使用当前信道特性的估计并确定后续传输的最佳比特率来规避性能网络降级并使吞吐量最大化的一项重要技术。尽管有几种可用的802速率控制算法。在11种WLAN标准中，很少有专门针对车载网络的速率适配的工作。在本文中，我们比较并评估了许多车辆环境中的现有数据速率适配方案，以识别其行为并分析其在各种情况下的性能。使用NS-3仿真器选择了六个算法进行比较：AARF，AARF-CD，AMRR，CARA，Onoe和Minstrel。仿真结果表明，在密集和动态情况下，Minstrel算法的性能优于其余五种机制。使用NS-3仿真器选择了六个算法进行比较：AARF，AARF-CD，AMRR，CARA，Onoe和Minstrel。仿真结果表明，在密集和动态情况下，Minstrel算法的性能优于其余五种机制。使用NS-3仿真器选择了六个算法进行比较：AARF，AARF-CD，AMRR，CARA，Onoe和Minstrel。仿真结果表明，在密集和动态情况下，Minstrel算法的性能优于其余五种机制。