Federatedlearning (FL) is an emerging distributed learning paradigm widely used in vehicular networks, where vehicles are enabled to train the deep model for the server while keeping private data locally. However, the annotation of private data by vehicular users is very difficult since the high costs and professional needs, and one solution is that roadside infrastructures could provide label mapping to the data according to the geographical coordinates. In this scenario where vehicles and roadside infrastructures hold the data and labels, respectively, traditional FL is not applicable since it needs each participant to have both data and labels. In this article, we propose a federated split learning (FSL) paradigm that split the deep model into two submodels which are trained separately in the vehicles and the roadside infrastructures. The vehicles and the roadside infrastructures exchange the intermediate data (i.e., smashed data and cut layer gradients) in training local submodels and upload the local gradients to the global server for aggregation into the global model. Specifically, we first adopt three types of privacy attacks to demonstrate that attackers could recover the private data and labels according to the shared intermediate data and uploaded local gradients. We then propose a differential privacy (DP)-based defense mechanism to defend the privacy attacks by perturbing the intermediate data. Furthermore, we design a contract-based incentive mechanism that encourages vehicles and roadside infrastructures to enhance training performance by adjusting their privacy strategies. The simulation results illustrated that the proposed defense mechanism can remarkably emasculate the performance of attacks and the proposed incentive mechanism is efficient in the FSL paradigm for vehicular networks.