Vehicle　behavior　analysis　has　gradually　developed　by　utilizing　trajectories　and　motion　features　to　characterize　on-road　behavior.　However,　the　existing　methods　analyze　the　behavior　of　each　vehicle　individually,　ignoring　the　interaction　between　vehicles.　According　to　the　theory　of　interactive　cognition,　vehicle-to-vehicle　interaction　is　an　indispensable　feature　for　future　autonomous　driving,　just　as　interaction　is　universally　required　for　traditional　driving.　Therefore,　we　place　the　vehicle　behavior　analysis　in　the　context　of　the　vehicle　interaction　scene,　where　the　self-vehicle　should　observe　the　behavior　category　and　degree　of　the　other-vehicle　that　is　about　to　interact　with　itself,　in　order　to　predict　whether　the　other-vehicle　will　pass　through　the　intersection　first　or　later,　and　then　decide　to　pass　through　or　wait.　Inspired　by　the　interactive　cognition,　we　develop　a　general　framework　of　Structured　Vehicle　Behavior　Analysis　(StruVBA)　and　derive　a　new　model　of　Structured　Fully　Convolutional　Networks　(StruFCN).　Moreover,　both　Intersection　over　Union　(IoU)　and　False　Negative　Rate　(FNR)　are　adopted　to　measure　the　similarity　between　the　predicted　behavior　degree　and　the　ground　truth.　Experimental　results　illustrate　that　the　proposed　method　achieves　higher　prediction　accuracy　than　most　existing　methods,　while　predicting　vehicle　behavior　with　richer　visual　meaning.　In　addition,　it　also　provides　an　example　of　modeling　the　interaction　between　vehicles　and　a　verification　for　interaction　cognition　theory　as　well.　IEEE