arXiv Computer Vision By Hoonhee Cho, Jae-Young Kang, Giwon Lee, Hyemin Yang, Heejun Park, Kuk-Jin Yoon

VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation

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VIPS is a benchmark for vehicle‑to‑infrastructure cooperative autonomous driving that uses pseudo‑simulation to combine vehicle and infrastructure observations, enabling scalable yet realistic evaluation of robustness and error propagation without full simulation. The paper also introduces CoS‑V2X, a cooperative planning framework that employs sparse representations to model vehicle‑infrastructure interactions efficiently and robustly under heterogeneous observations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv AI
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CMU-Drive and V2V-VLA: Cooperative Multi-agent Unified Driving with Reasoning Benchmark and Vehicle-to-Vehicle Vision-Language-Action Models

arXiv:2608. 07621v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have recently achieved impressive performance for end-to-end autonomous driving, yet existing approaches are primarily designed for an individual single autonomous driving agent with limited support for cooperative perception, reasoning, and planning.

By Hsu-kuang Chiu, Stephen F. Smith
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