In next-generation traffic, multiple intelligent vehicles equipped with autonomous driving systems (ADS) operate simultaneously. These vehicles share real-time information through vehicle-to-vehicle (V2V) communication during operation and make decisions based on this mutual perception. In this paper, we present a novel METAmorphic testing framework to evaluate ADS performance in multi-vehicle scenarios, addressing the META information shared during V2V communication (META²V2V). META²V2V interacts with the shared information by systematically injecting four types of controlled common perturbations into V2V data streams and observing how these disruptions affect the vehicles’ mutual perception and subsequent interactions. META²V2V assesses ADS decisions from two aspects: soundness and robustness, by introducing soundness metamorphic testing relations (S-MRs) and robustness metamorphic relations (R-MRs), respectively. Specifically, S-MRs claim that the performance of ADS under perfect information should outperform that under perturbations, whereas R-MRs claim that the performance of ADS should not degrade too much under information perturbations. Furthermore, META²V2V employs multi-objective search-based testing conducted from two directions to efficiently generate test groups that violate S-MRs and R-MRs, respectively. Experimental results demonstrate that, compared to random testing, our approach can identify 733.3% and 133.0% more violations that reveal soundness and robustness issues.