In response to the significant challenges posed by multipath effects, signal attenuation, and frequent occurrences of gross errors and cycle slips in short-term occluded environments, which diminish the accuracy and robustness of vehicular satellite navigation systems, this paper proposes an integrated navigation method for vehicle GNSS/INS based on adaptive factor graph. Initially, a GNSS/INS factor graph model is established, utilizing INS as the primary navigation system, supplemented by GNSS factors for corrective adjustments based on GNSS measurement residuals. This design allows for adaptive adjustments to the weights of GNSS factors, effectively mitigating divergence errors caused by environmental changes and subsequently reducing the impact of excessive GNSS positioning errors on the combined navigation′s accuracy and robustness. The method was empirically validated using a racing car experiment, which demonstrated that the adaptive factor graph-based method significantly reduced the root mean square error and maximum error by 70.01% and 55.31%, respectively, compared to conventional factor graph-based methods. The results confirm that the proposed method enhances positioning accuracy and robustness.