With　the　advancement　of　the　Internet　of　Vehicles　(IoV),　delay-sensitive　vehicular　applications　have　flourished.　Among　them,　the　autonomous　driving　technology　is　a　focal　point.　For　autonomous　driving　vehicles,　efficiently　and　timely　processing　the　ever-increasing　data　is　critical.　In　real　traffic　scenes,　the　task-processing　efficiency　is　closely　related　to　the　traffic　flows.　However,　the　traffic　flow　modeling　is　always　ignored　or　considered　roughly　in　the　most　existing　studies.　For　this　issue,　a　traffic　model　based　on　a　stochastic　geometry　framework　is　proposed　to　simulate　a　real　traffic　environment　of　autonomous　driving　vehicles.　To　reduce　the　cost　of　processing　tasks,　a　distributed　computation　offloading　scheme　based　on　mobile　edge　computing　(MEC)　is　proposed　by　soliciting　nearby　vehicles　and　roadside　units　(RSUs)　with　rich　computing　resources.　For　the　average　cost　minimization　optimization　problem,　we　divide　the　NP-hard　problem　into　several　sub-problems　and　take　advantage　of　the　Lagrange　multiplier　with　KKT　constraints　to　solve　by　optimizing　task　splitting　ratios.　We　compare　the　proposed　traffic　model　with　some　common　ones　and　also　consider　the　pros　and　cons　of　different　computation　offloading　strategies.　Simulation　results　show　that　the　proposed　strategy　outperforms　other　benchmarks　and　the　proposed　modeling　method　is　rational.