The study introduces ELM as a framework for agents to understand driving scenarios with large spatial and temporal spans. It emphasizes the importance of spatial localization and temporal cues in autonomous driving. ELM outperforms existing models in various tasks related to scene understanding, localization, memorization, and forecasting.
The study highlights the significance of embodied understanding for intelligent agents like self-driving vehicles. It discusses the limitations of traditional Vision-Language Models (VLMs) in perceiving complex driving scenarios and proposes ELM as a solution to overcome these limitations. The research presents a detailed methodology involving pre-training strategies, token selection modules, and evaluation metrics to validate the effectiveness of ELM.
By conducting experiments and ablation studies, the study demonstrates the superior performance of ELM compared to previous VLMs on tasks such as tracking, box detection, traffic sign inquiry, moment recap, and activity prediction. The results showcase ELM's ability to generalize across different tasks and handle zero-shot scenarios effectively.
Overall, the study provides valuable insights into enhancing agents' embodied understanding of driving scenarios through advanced language models like ELM.
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arxiv.org
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