Research on Vehicular Ad hoc Networks (VANET) has been the main attractive Research effort these days, a vehicular ad hoc network (VANET) is an extension of a mobile ad hoc network (MANET), where vehicles can communicate with each other in a form of the intelligent transport system (ITS). The communication between vehicles can be interfered with by Denial of Service (DoS) attacks such as jamming attacks, launching on the control channel, and causing significant network performance degradation. These attacks have an impact on the network by lowering network performance; earlier, there has been a lot of study for improving network performance by employing routing protocols. When a jammer can interpret data link layer protocols, it becomes as energy efficient as legitimate nodes. In this Research, we have built a model that examines the performance of vehicular ad hoc networks (VANET) under jamming attacks to provide EVA(Enhancement Voting Algorithm) based on global Trust( Direct Trust and Indirect Trust)for detecting jamming attacks. Route request (RREQ), and route reply (RPLY) packets are utilized in the route discovery phase. In the route maintenance phase, Route Error (RERR) and HELLO packets are used. These packets are also measured while assessing trust because they contribute significantly to routing activities. Although misbehaving nodes can handle those packets, their likelihood of being used is lower than that of well-behaving nodes. Three trust levels are defined based on the computed global trust value to make the best routing decision, which will be implemented in NS3 simulation. We use Bonnmotion to create and analyze mobility scenarios, which serve as a tool for investigating mobile multi-hop network scenario characteristics. The scenarios were exported for the network simulators NS3 to create a mechanism approach. The network's performance is evaluated in terms of QoS parameters and throughput (PDR), signal intensity, RSSI, carrier sensing time, energy consumption, average delay, and throughput to identify network performance under jamming attacks. It is examined that after malicious nodes which send data above the assigned value have been labeled as malicious nodes and removed from the network, the network performance significant Improvement.