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+training+and+testing.+CRITICAL+stands+out+for+its+ability+to+generate+diverse+scenarios%252C+focusing+on+critical+driving+situations+that+target+specific+learning+and+performance+gaps+identified+in+the+Reinforcement+Learning+(RL)+agent.+The+framework+achieves+this+by+integrating+real-world+traffic+dynamics%252C+driving+behavior+analysis%252C+surrogate+safety+measures%252C+and+an+optional+Large+Language+Model+(LLM)+component.+It+is+proven+that+the+establishment+of+a+closed+feedback+loop+between+the+data+generation+pipeline+and+the+training+process+can+enhance+the+learning+rate+during+training%252C+elevate+overall+system+performance%252C+and+augment+safety+resilience.+Our+evaluations%252C+conducted+using+the+Proximal+Policy+Optimization+(PPO)+and+the+HighwayEnv+simulation+environment%252C+demonstrate+noticeable+performance+improvements+with+the+integration+of+critical+case+generation+and+LLM+analysis%252C+indicating+CRITICAL%27s+potential+to+improve+the+robustness+of+AV+systems+and+streamline+the+generation+of+critical+scenarios.+This+ultimately+serves+to+hasten+the+development+of+AV+agents%252C+expand+the+general+scope+of+RL+training%252C+and+ameliorate+validation+efforts+for+AV+safety.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.0a78ba4fb4755e8b0cfe63da132df84f%3frik%3dzHGT8ew%252fLbgDtA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Autonomous+Vehicle+Infrastructure+Course&FORM=IRPRST&ck=569BC8888C87B51827835012540283F6&selectedIndex=28&itb=0)
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