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In emergency situations, cooperative positioning of vehicular nodes is essential for facilitating precise and stable information for achieving reliable data dissemination in Vehicular Ad hoc NETworks (VANETs). However, the existence of Non-Line-Of-Sight (NLOS) nodes degrades the accuracy in estimating ranging measurements introduced by the blockages from tall vehicles and buildings. In this paper, Raccoon Optimization Algorithm-based Accurate Positioning Scheme (ROA-APS) is proposed for improving the accuracy in the estimation of ranging measurements in order to determine the exact position of NLOS nodes. It is proposed for ensuring maximized reliability and reduced latency in the event of warning message exchange. It inherits the food rummaging style of real raccoons for speeding and strengthening the local and global search process involved in the estimation of NLOS node positions. It utilizes maximum probability of acquiring higher adaptability through active learning to attain better localization of NLOS nodes. It inherits the distance information for calculating the position accuracy associated with vehicle trajectory, distance information error and the number of vehicles. It also uses the method of weighted average to enforce more confidence to the distance information provided by neighboring nodes. The simulation experiments of the proposed ROA-APS using EstiNet simulators are conducted to determine its significance with respect to positioning accuracy, emergency message dissemination rate, positioning error, neighbor vehicles awareness rate and positioning time. The results confirm an increased mean emergency message dissemination rate, positioning accuracy and neighbor vehicles awareness rate by 16.21%, 14.38% and 15.16% when compared to the benchmarked schemes.