Nowadays, there are many countries started adopting the vehicle based wireless communications for different purposes such as traffic monitoring, security, etc. The concept of VANET (Vehicular Ad-Hoc Network) is a network in which moving vehicular as nodes in the network in order to form the mobile network. Therefore, VANETs are dynamic mobile ad hoc network (MANET) topology. Basically, there are two main types of VANET infrastructure such as distributed and centralized. The ad hoc and cellular technologies are grouped in centralized infrastructure called Vehicle to Infrastructure (V2I). The distributed architecture is completely based on ad hoc methodologies called as Vehicle to Vehicle(V2V) communication. The focus of our work is on V2V. For V2V VANETs, there is a need for accurate estimation of vehicle nodes location. The GPS based approach is having limitations of unavailability and hence is not reliable for nodes position estimation. There are many research solutions proposed recently for efficient estimation of vehicle nodes position but suffered from the limitations of efficiency. In this project, we proposed a novel approach for location estimation called ESCL-VNET (Extended Self-Correcting Localization scheme for V2V Networks) which is based on recent SCL-VNET method. We first designed a novel received signal strength indication (RSSI) based location estimation method. Self-correction is performed to minimize the location errors by learning network topology scenarios. As this method is not enough to address all environmental conditions, we then proposed the weighted localization using the signal to the interference-noise ratio (SINR) to achieve the reliability and more accuracy in location estimation. The experimental evaluation of the proposed approach is done using NS3 simulation tool. The results show that ESCL-VENT achieved the improved accuracy and performance as compared to existing SCL-VENT