Steering　control　plays　an　important　role　in　vehicle　driving　decisions.　Convolutional　Neural　Network(CNN)　has　been　widely　studied　in　the　field　of　autonomous　vehicle　navigation　due　to　its　powerful　nonlinear　expression　and　spatial　feature　understanding　ability　in　image　scene.　In　this　paper,　we　present　an　end-to-end　steering　controller　based　on　CNN　to　predict　the　desired　steering　wheel　angle　from　a　continuous　video　image,　which　does　not　require　manual　design　rules　and　simplifies　a　series　of　intermediate　steps　in　traditional　autonomous　driving　decision,　such　as　target　detection,　object　recognition　and　path　tracking.　The　pre-trained　network　model　is　verified　in　the　TORCS　racing　simulator.　The　experimental　results　show　that　the　controller　has　good　generalization　ability　and　can　make　the　vehicle　follow　the　right　side　of　the　lane　on　the　unknown　test　track.　In　order　to　intuitively　understand　what　factors　in　the　image　have　an　impact　on　vehicle　decision-making,　the　remarkable　features　that　affect　the　steering　control　of　the　autonomous　vehicle　are　visualized.　©　2019　IEEE.