With　the　rapid　development　of　artificial　intelligence,　the　study　of　intelligent　transportation　is　getting　more　and　more　attention　and　vision-based　vehicle　behaviour　analysis　has　become　an　active　research　field.　Most　existing　methods　label　vehicle　behaviours　with　discrete　labels　and　then　use　the　vehicle　trajectories　or　motion　characteristics　to　train　classifiers　which　identify　vehicle　behaviours.　However,　a　simple　discrete　label　cannot　contain　detailed　information　about　the　vehicle　behaviour.　So,　inspired　by　structured　learning,　the　authors　design　a　structured　label　which　is　used　to　characterise　the　instantaneous　behavioural　state　based　on　the　vehicle　image,　including　behaviour　trend　and　degree　simultaneously.　A　structured　convolutional　neural　networks　model　is　constructed　to　learn　and　predict　structured　representation　of　transient　vehicle　behaviour　and　preliminary　experimental　results　justify　the　feasibility　of　vehicle　behaviour　structural　analysis　model,　but　it　achieves　only　53.3%　prediction　accuracy.　To　reduce　the　risk　of　overfitting　to　small-scale　training　data,　the　authors　further　propose　an　overfitting-preventing　deep　neural　network,　which　exploits　transfer　learning　and　multi-task　learning　to　achieve　a　much　higher　prediction　accuracy　of　91.1%.