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+samples+is+a+key+performance+indicator+of+a+trajectory+prediction+model.+However%252C+the+development+and+ranking+of+state-of-the-art+(SotA)+models+are+driven+by+their+In-Distribution+(ID)+performance+on+individual+competition+datasets.+We+present+an+OoD+testing+protocol+that+homogenizes+datasets+and+prediction+tasks+across+two+large-scale+motion+datasets.+We+introduce+a+novel+prediction+algorithm+based+on+polynomial+representations+for+agent+trajectory+and+road+geometry+on+both+the+input+and+output+sides+of+the+model.+With+a+much+smaller+model+size%252C+training+effort%252C+and+inference+time%252C+we+reach+near+SotA+performance+for+ID+testing+and+significantly+improve+robustness+in+OoD+testing.+Within+our+OoD+testing+protocol%252C+we+further+study+two+augmentation+strategies+of+SotA+models+and+their+effects+on+model+generalization.+Highlighting+the+contrast+between+ID+and+OoD+performance%252C+we+suggest+adding+OoD+testing+to+the+evaluation+criteria+of+trajectory+prediction+models.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.aa7bbc31740cd421986b535a94a1b2ac%3frik%3d8z0Nje4NDvOcfw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Autonomous+Deep+Learning+Out+of+Distribution&FORM=IRPRST&ck=54864A18C67034588626DD37FB85A62E&selectedIndex=8&itb=0)
+detection%252C+where+test+data+may+come+from+classes+that+are+unknown+during+training+(i.e.%252C+OOD+data).+Due+to+the+unavailability+and+diversity+of+OOD+data%252C+good+generalization+ability+is+crucial+for+effective+OOD+detection+algorithms%252C+and+corresponding+learning+theory+is+still+an+open+problem.+To+study+the+generalization+of+OOD+detection%252C+this+paper+investigates+the+probably+approximately+correct+(PAC)+learning+theory+of+OOD+detection+that+fits+the+commonly+used+evaluation+metrics+in+the+literature.+First%252C+we+find+a+necessary+condition+for+the+learnability+of+OOD+detection.+Then%252C+using+this+condition%252C+we+prove+several+impossibility+theorems+for+the+learnability+of+OOD+detection+under+some+scenarios.+Although+the+impossibility+theorems+are+frustrating%252C+we+find+that+some+conditions+of+these+impossibility+theorems+may+not+hold+in+some+practical+scenarios.+Based+on+this+observation%252C+we+next+give+several+necessary+and+sufficient+conditions+to+characterize+the+learnability+of+OOD+detection+in+some+practical+scenarios.+Lastly%252C+we+offer+theoretical+support+for+representative+OOD+detection+works+based+on+our+OOD+theory.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.d44a1b8ca4a49d289d27bbcfd8c2688e%3frik%3dEotYbYDkAJRwQg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Autonomous+Deep+Learning+Out+of+Distribution&FORM=IRPRST&ck=7556EEA57D147AA8D17C1F0180A25471&selectedIndex=25&itb=0)
