4 papers
STRIDE-QA: Visual Question Answering Dataset for Spatiotemporal Reasoning in Urban Driving Scenes
Keishi Ishihara, Kento Sasaki, Tsubasa Takahashi +2
Vision-Language Models (VLMs) have been applied to autonomous driving to support decision-making in complex real-world scenarios. However, their training on static, web-sourced ima…
Hierarchical Reasoning with Vision-Language Models for Incident Reports from Dashcam Videos
Shingo Yokoi, Kento Sasaki, Yu Yamaguchi
Recent advances in end-to-end (E2E) autonomous driving have been enabled by training on diverse large-scale driving datasets, yet autonomous driving models still struggle in out-of…
One-D-Piece: Image Tokenizer Meets Quality-Controllable Compression
Keita Miwa, Kento Sasaki, Hidehisa Arai +2
Current image tokenization methods require a large number of tokens to capture the information contained within images. Although the amount of information varies across images, mos…
ACT-Bench: Towards Action Controllable World Models for Autonomous Driving
Hidehisa Arai, Keishi Ishihara, Tsubasa Takahashi +1
World models have emerged as promising neural simulators for autonomous driving, with the potential to supplement scarce real-world data and enable closed-loop evaluations. However…