This paper proposes a novel collaborative physical layer authentication scheme designed for vehicular ad-hoc networks (VANETs) based on Dempster-Shafer (D-S) evidence theory. Our method enhances the authentication robustness against complex attacks and changing channel conditions. We do this by including a detailed channel model that considers multipath fading and dynamic Doppler shifts, along with a simulated intelligent attack strategy. Each cooperating vehicle extracts channel state information (CSI) features and generates basic probability assignments (BPAs) using a lightweight convolutional neural network (CNN). These BPAs, which indicate the legitimacy or illegitimacy of a transmitting vehicle, are then combined using a modified D-S combination rule with reliability weighting and conflict resolution. Our extensive simulations evaluated the scheme’s performance. The results show its better authentication performance (especially in Equal Error Rate and Area Under Curve) compared to traditional machine learning methods like K-Nearest Neighbors (KNN) and Naive Bayes, and also a standalone CNN. These simulations show how effective our enhanced D-S evidence scheme is at combining uncertain or even conflicting information from multiple cooperators. This leads to more reliable authentication decisions in complex vehicular environments.