The　distribution　density　of　the　driver＇s　start-reaction　time　was　estimated　using　non-parametric　kernel　density　to　accurately　analyze　the　driver＇s　start-reaction　time.　Gaussian　kernel　was　chosen　as　the　kernel　function　and　the　optimal　window　width　was　obtained　by　the　recursive　method.　The　estimation　results　of　kernel　density　were　compared　with　the　results　of　normal　distribution　and　lognormal　distribution　by　distribution　fitting　and　hypothesis　testing.　The　results　show　that　the　nonparametric　kernel　density　estimation　of　the　driver＇s　start-reaction　time　is　accurate　and　effective.　The　method　overcomes　the　problems　of　the　unknown　prior　distribution　type.　The　curve　of　kernel　density　estimation　is　more　intuitive　to　see　the　changes　of　driver＇s　start-reaction　time　in　each　time　period　and　the　overall　distribution　pattern.