The　driver＇s　takeover　time　is　crucial　to　ensure　a　safe　takeover　transition　in　conditional　automated　driving.　The　study　aimed　to　construct　a　prediction　model　of　driver＇s　takeover　time　based　on　individual　characteristics,　external　environment,　and　situation　awareness　variables.　A　total　of　18　takeover　events　were　designed　with　scenarios,　non-driving-related　tasks,　takeover　request　time,　and　traffic　flow　as　variables.　High-fidelity　driving　simulation　experiments　were　carried　out,　through　which　the　driver＇s　takeover　data　was　obtained.　Fifteen　basic　factors　and　three　dynamic　factors　were　extracted　from　individual　characteristics,　external　environment,　and　situation　awareness.　In　this　experiment,　these　18　factors　were　selected　as　input　variables,　and　XGBoost　and　Shapely　were　used　as　prediction　methods.　A　takeover　time　prediction　model　(BM　+　SA　model)　was　then　constructed.　Moreover,　we　analyzed　the　main　effect　of　input　variables　on　takeover　time,　and　the　interactive　contribution　made　by　the　variables.　And　in　this　experiment,　the　15　basic　factors　were　selected　as　input　variables,　and　the　basic　takeover　time　prediction　model　(BM　model)　was　constructed.　In　addition,　this　study　compared　the　performance　of　the　two　models　and　analyzed　the　contribution　of　input　variables　to　takeover　time.　The　results　showed　that　the　goodness　of　fit　of　the　BM　+　SA　model　(Adjusted_R2)　was　0.7746.　The　XGBoost　model　performs　better　than　other　models　(support　vector　machine,　random　forest,　CatBoost,　and　LightBoost　models).　The　relative　importance　degree　of　situation　awareness　variables,　individual　characteristic　variables,　and　external　environment　variables　to　takeover　time　gradually　reduced.　Takeover　time　increased　with　the　scan　and　gaze　durations　and　decreased　with　pupil　area　and　self-reported　situation　awareness　scores.　There　was　also　an　interaction　effect　between　the　variables　to　affect　takeover　time.　Overall,　the　performance　of　the　BM　+　SA　model　was　better　than　that　of　the　BM　model.　This　study　can　provide　support　for　predicting　driver＇s　takeover　time　and　analyzing　the　mechanism　of　influence　on　takeover　time.　This　study　can　provide　support　for　the　development　of　real-time　driver＇s　takeover　ability　prediction　systems　and　optimization　of　human–machine　interaction　design　in　automated　vehicles,　as　well　as　for　the　management　department　to　evaluate　and　improve　the　driver＇s　takeover　performance　in　a　targeted　manner.　©　2024　Elsevier　Ltd