EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
arXiv:2604.22851v2 Announce Type: replace-cross Abstract: While Vision-Language Models (VLMs) have advanced high-level reasoning in autonomous driving, their ability to ground this reasoning in the underlying physics of ego-motion remains poorly understood. We introduce EgoDyn-Bench [Project page: (https://tum-avs.github.io/EgoDyn-Bench-Website/), Code: (https://github.com/TUM-AVS/EgoDyn-Bench), Dataset: (https://huggingface.co/datasets/fnc1901/EgoDyn-Bench)], a diagnostic benchmark for evaluating the semantic ego-motion understanding of vision-centric foundation models. By mapping continuous
The proliferation of Vision-Language Models (VLMs) in autonomous driving necessitates specialized benchmarks to assess their physical grounding, a critical gap EgoDyn-Bench aims to address.
Accurate ego-motion understanding is fundamental for safe and reliable autonomous systems, and this benchmark directly evaluates a core capability of next-generation AI models in this domain.
The introduction of EgoDyn-Bench provides developers with a standardized tool to diagnose and improve the physical reasoning capabilities of vision-centric foundation models for autonomous driving.
- · Autonomous Driving Developers
- · AI Safety Researchers
- · Robotics Companies
- · VLM Developers
- · Companies with weak VLM ego-motion understanding
- · Unstandardized benchmarking approaches
Improved ego-motion understanding will lead to safer and more robust autonomous driving systems.
This benchmark could become a de facto standard, influencing research directions and product development cycles in autonomous vehicle AI.
Enhanced physical grounding in AI models may accelerate the broader adoption and regulatory approval of higher levels of autonomous driving.
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