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Models for Automated Driving
+estimate+either+long-term+trajectory+waypoints+or+a+set+of+control+actions+as+a+reactive+solution+for+closed-loop+planning+based+on+their+rich+scene+comprehension.+However%252C+these+estimations+are+coarse+and+are+subjective+to+their+world+understanding+which+may+generate+sub-optimal+decisions+due+to+perception+errors.+In+this+paper%252C+we+introduce+LeGo-Drive%252C+which+aims+to+address+this+issue+by+estimating+a+goal+location+based+on+the+given+language+command+as+an+intermediate+representation+in+an+end-to-end+setting.+The+estimated+goal+might+fall+in+a+non-desirable+region%252C+like+on+top+of+a+car+for+a+parking-like+command%252C+leading+to+inadequate+planning.+Hence%252C+we+propose+to+train+the+architecture+in+an+end-to-end+manner%252C+resulting+in+iterative+refinement+of+both+the+goal+and+the+trajectory+collectively.+We+validate+the+effectiveness+of+our+method+through+comprehensive+experiments+conducted+in+diverse+simulated+environments.+We+report+significant+improvements+in+standard+autonomous+driving+metrics%252C+with+a+goal+reaching+Success+Rate+of+81%2525.+We+further+showcase+the+versatility+of+LeGo-Drive+across+different+driving+scenarios+and+linguistic+inputs%252C+underscoring+its+potential+for+practical+deployment+in+autonomous+vehicles+and+intelligent+transportation+systems.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.7d64d7fd0a737fae275788efaf4293e8%3frik%3do9rrUU2jZi1ffg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Visual+Language+Models+for+Automated+Driving&FORM=IRPRST&ck=22450AC55600BAB0819A768D94AB0920&selectedIndex=25&itb=0)
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