Autonomous　driving　simulation　is　crucial　for　testing　autonomous　driving　functions　and　iterating　algorithms.　However,　most　current　studies　focus　solely　on　normal　traffic　behaviors,　such　as　car-following　or　lane-changing,　while　neglecting　abnormal　traffic　behaviors　that　are　very　important　for　autonomous　vehicle　training　and　testing,　such　as　random　deviations　from　the　road　centerline,　random　desired　speeds,　etc.　This　paper　introduces　an　innovative　method　that　integrates　car-following　and　lane-changing　behaviors　into　a　unified　framework　by　designing　a　path　value　evaluation　function　for　static　path　optimization　and　performing　optimal　path-following　obstacle　avoidance.　This　method　allows　for　random　deviations　from　the　road　centerline　and　random　desired　speeds.　By　utilizing　the　highD　dataset　for　model　calibration,　the　simulated　traffic　flow　closely　mirrors　real-world　conditions.　Simulation　experiments　indicate　that　this　model　can　simulate　complex　and　diverse　vehicle　driving　behaviors,　particularly　in　scenarios　involving　deviations　from　the　road　centerline　and　abnormal　speeds,　with　the　average　following　distance　reaching　95.2%　of　the　standard　value.　Copyright　©　2024　The　Authors.