To　solve　the　traffic　safety　problems　that　arise　during　＇mixed　traffic＇　between　autonomous　and　manual　vehicles,　this　paper　presents　a　harzard　scenario　simulation　and　safety　assessment　method　based　on　autonomous　driving　crash　reports.　197　crash　reports　of　autonomous　vehicles　are　selected　from　California　Department　of　Motor　Vehicles　and　used　for　autonomous　driving　hazard　scenario　extraction.　Combining　37　pre-collision　scenarios　published　by　the　National　Highway　Traffic　Safety　Administration,　typical　traffic　safety　scenarios　are　identified.　A　random　forest　model　is　used　to　identify　key　elements　of　typical　autonomous　vehicle　accident　scenarios.　The　CARLA　microscopic　traffic　simulation　platform　is　used　to　simulate　two　typical　＇mixed　traffic＇　safety　scenarios:　Lead　Vehicle　Stopped　and　Following　Vehicle　Making　a　Maneuver.　Three　indicators　-　relative　velocity,　relative　distance,　and　time-to-collision　-　are　selected　for　constructing　a　safety　evaluation　suitable　for　autonomous　vehicles.　The　research　results　show　that　in　the　Lead　Vehicle　Stopped　scenario,　when　the　following　vehicle　is　15　meters　away　from　the　front　vehicle,　the　risk　of　collision　is　extremely　high　when　the　initial　speed　of　the　following　vehicle　is　greater　than　13.1　m/s.　In　the　Following　Vehicle　Making　a　Maneuver　scenario,　when　the　initial　speed　of　the　following　vehicle　is　greater　than　12.5　m/s,　the　risk　of　collision　with　the　front　vehicle　is　also　extremely　high.　The　results　can　provide　support　for　the　safety　evaluation　of　autonomous　driving　and　the　improvement　of　autonomous　driving　system.　©　2024　SPIE.