Intelligent　vehicles　use　advanced　driver　assistance　systems　(ADASs)　to　mitigate　driving　risks.　There　is　increasing　demand　for　an　ADAS　framework　that　can　increase　driving　safety　by　detecting　dangerous　driving　behavior　from　driver,　vehicle,　and　lane　attributes.　However,　because　dangerous　driving　behavior　in　real-world　driving　scenarios　can　be　caused　by　any　or　a　combination　of　driver,　vehicle,　and　lane　attributes,　the　detection　of　dangerous　driving　behavior　using　conventional　approaches　that　focus　on　only　one　type　of　attribute　may　not　be　sufficient　to　improve　driving　safety　in　realistic　situations.　To　facilitate　driving　safety　improvements,　the　concept　of　dangerous　driving　intensity　(DDI)　is　introduced　in　this　paper,　and　the　objective　of　dangerous　driving　behavior　detection　is　converted　into　DDI　estimation　based　on　the　three　attribute　types.　To　this　end,　we　propose　a　framework,　wherein　fuzzy　sets　are　optimized　using　particle　swarm　optimization　for　modeling　driver,　vehicle,　and　lane　attributes　and　then　used　to　accurately　estimate　the　DDI.　The　mean　opinion　scores　of　experienced　drivers　are　employed　to　label　DDI　for　a　fair　comparison　with　the　results　of　our　framework.　The　experimental　results　demonstrate　that　the　driver,　vehicle,　and　lane　attributes　defined　in　this　paper　provide　useful　cues　for　DDI　analysis;　furthermore,　the　results　obtained　using　the　framework　are　in　favorable　agreement　with　those　obtained　in　the　perception　study.　The　proposed　framework　can　greatly　increase　driving　safety　in　intelligent　vehicles,　where　most　of　the　driving　risk　is　within　the　control　of　the　driver.