AiSCREAM: Absolute Target Localization with Language-Conditioned Cross-View Alignment for Autonomous Vehicles

Autonomous personal mobility benefits greatly from the abil-ity to predict executable spatial goals from language and front view imagealone. In this study, we focus on absolute target position prediction froma language instruction and front view image, which requires referring ex-pression disambiguation and absolute target localization without depthcues. To address this, we propose AiSCREAM, a language-conditionedtarget localization model based on cross-view vision-language reason-ing. AiSCREAM constructs cross-view geometric semantic alignment be-tween the front and an Aerial Semantic view, which is an overhead-viewimage synthesized from the front image via a text-conditioned image-to-image generation model. By preserving appearance-level scene charac-teristics, the alignment can leverage language-conditioned semantic cuesand enable reliable absolute target localization from a single image. Toevaluate AiSCREAM, we constructed DRAMATiST, a benchmark thatintroduces road and traffic diversity. The experimental results demon-strated that AiSCREAM achieved a mean absolute position error of4.03 m on DRAMATiST, which outperformed baseline methods and hu-man performance. The project page is available at https://ai-scream-project-page.vercel.app/ .