Accurately　predicting　driving　behavior　can　help　to　avoid　potential　improper　maneuvers　of　human　drivers,　thus　guaranteeing　safe　driving　for　intelligent　vehicles.　In　this　paper,　we　propose　a　novel　deep　belief　network　(DBN),　called　MSR-DBN,　by　integrating　a　multi-target　sigmoid　regression　(MSR)　layer　with　DBN　to　predict　the　front　wheel　angle　and　speed　of　the　ego　vehicle.　Precisely,　the　MSR-DBN　consists　of　two　sub-networks:　one　is　for　the　front　wheel　angle,　and　the　other　one　is　for　speed.　This　MSR-DBN　model　allows　ones　to　optimize　lateral　and　longitudinal　behavior　predictions　through　a　systematic　testing　method.　In　addition,　we　consider　the　historical　states　of　the　ego　vehicle　and　surrounding　vehicles　and　the　driver’s　operations　as　inputs　to　predict　driving　behaviors　in　a　real-world　environment.　Comparison　of　the　prediction　results　of　MSR-DBN　with　a　general　DBN　model,　back　propagation　(BP)　neural　network,　support　vector　regression　(SVR),　and　radical　basis　function　(RBF)　neural　network,　demonstrates　that　the　proposed　MSR-DBN　outperforms　the　others　in　terms　of　accuracy　and　robustness.