With the rapid development of vehicular networks, location privacy leakage is a key security issue when users enjoy location- based services (LBSs) provided by vehicular networks. This study proposes a personalized location privacy protection scheme based on differential privacy to address the issue of privacy leakage of location services in vehicular networks, which can meet the personalized privacy needs of users on the premise of protecting their privacy. Firstly, a normalized decision matrix is defined to describe the efficiency and privacy effects of navigation recommendations. Then, the utility model is established by introducing the multi-attribute theory, and the user's privacy preference is integrated into the model to select the best driving route for the user. Finally, considering the user's privacy preference, the distance proportion is used as the measurement index to allocate the appropriate privacy budget for the user, and the false location generation range is determined to generate the most effective service request location. Based on the real data set, the proposed scheme is compared with the existing scheme through simulation experiments. The experimental results show that the personalized location privacy protection scheme proposed in this study can meet the service requirements of users and provide higher quality of service (QoS) while reasonably protecting the privacy of them.