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

• URL: http://arxiv.org/abs/2609.02462v1
• Date: Wed, 02 Sep 2026 11:32:52 GMT
• Title: VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation
• Abstract summary: VIPS is a benchmark for cooperative autonomous driving in V2I settings based on pseudo-simulation.<n>CoS-V2X is a cooperative planning framework based on sparse representations.
• Score: 50.18051979020699
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: End-to-end autonomous driving in urban environments requires robust decision-making under partial observability and complex multi-agent interactions. Severe occlusions and dense traffic at intersections limit the perception capability of single-agent systems, motivating recent efforts on Vehicle-to-Infrastructure (V2I) cooperation for perception and planning. However, existing evaluation protocols face a fundamental trade-off: open-loop evaluation fails to capture error accumulation and recovery from deviations, while closed-loop evaluation is costly, difficult to scale, and often relies on simulated environments that may suffer from domain gaps. To bridge this gap, we propose VIPS, a benchmark for cooperative autonomous driving in V2I settings based on pseudo-simulation. VIPS extends pseudo-simulation by integrating vehicle and infrastructure observations. This enables scalable yet realistic evaluation of robustness and error propagation without full simulation. We further present CoS-V2X, a cooperative planning framework based on sparse representations. CoS-V2X models vehicle-infrastructure interactions using compact features for efficient communication and robust decision-making under heterogeneous observations. Code and dataset are available at https://vips2026.github.io.

Related papers

• Roadside-Cooperative Autonomous Driving: From Data Platform to Vision-Language End-to-End Reasoning [19.188066393763407]
  AURORA is an end-to-end cooperative driving framework.<n>AURORA mitigates spatial and semantic discrepancies across ego and roadside viewpoints.<n>LoRA-adapted VLM bridges semantic reasoning and generative trajectory planning.
  arXiv  Detail & Related papers  (2026-08-21T12:29:27Z)
• CADET: A Modular Platform for Evaluating Distributed Cooperative Autonomy in Connected Autonomous Vehicles [3.544599494530976]
  CADET is a modular platform for systematic and reproducible evaluation of distributed cooperative autonomy systems.<n>We show that distributed deployment choices shape safety, with V2V intent packets outperforming cloud-based perception and RSU-assisted perception sustaining safety until overloaded by concurrent requests.
  arXiv  Detail & Related papers  (2026-06-02T15:13:13Z)
• A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner [69.43049144653882]
  This paper introduces a unified architecture that decouples high-level decision-making from vehicle-specific control.<n>The proposed architecture is realized through GuadalPlanner, which defines standardized interfaces between planning, sensing, and vehicle execution.
  arXiv  Detail & Related papers  (2026-02-11T10:02:31Z)
• SparseCoop: Cooperative Perception with Kinematic-Grounded Queries [24.54324085409114]
  We propose SparseCoop, a fully sparse cooperative perception framework for 3D detection and tracking.<n> Experiments on2X-Seq and Griffin datasets show SparseCoop achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-12-07T13:22:06Z)
• Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model [63.336123527432136]
  We introduce Bench2Drive-R, a generative framework that enables reactive closed-loop evaluation.<n>Unlike existing video generative models for autonomous driving, the proposed designs are tailored for interactive simulation.<n>We compare the generation quality of Bench2Drive-R with existing generative models and achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-12-11T06:35:18Z)
• V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models [13.716889927164383]
  Vehicle-to-everything (V2X) cooperation has emerged as a promising paradigm to overcome the perception limitations of classical autonomous driving.<n>This paper introduces V2X-VLM, a novel end-to-end (E2E) cooperative autonomous driving framework based on vision-language models (VLMs)<n>V2X-VLM integrates multiperspective camera views from vehicles and infrastructure with text-based scene descriptions to enable a more comprehensive understanding of driving environments.
  arXiv  Detail & Related papers  (2024-08-17T16:42:13Z)
• NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking [65.24988062003096]
  We present NAVSIM, a framework for benchmarking vision-based driving policies. Our simulation is non-reactive, i.e., the evaluated policy and environment do not influence each other. NAVSIM enabled a new competition held at CVPR 2024, where 143 teams submitted 463 entries, resulting in several new insights.
  arXiv  Detail & Related papers  (2024-06-21T17:59:02Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• CoPEM: Cooperative Perception Error Models for Autonomous Driving [20.60246432605745]
  We focus on the (onboard) perception of Autonomous Vehicles (AV), which can manifest as misdetection errors on the occluded objects. We introduce the notion of Cooperative Perception Error Models (coPEMs) towards achieving an effective integration of V2X solutions within a virtual test environment.
  arXiv  Detail & Related papers  (2022-11-21T04:40:27Z)
• V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer [58.71845618090022]
  We build a holistic attention model, namely V2X-ViT, to fuse information across on-road agents. V2X-ViT consists of alternating layers of heterogeneous multi-agent self-attention and multi-scale window self-attention. To validate our approach, we create a large-scale V2X perception dataset.
  arXiv  Detail & Related papers  (2022-03-20T20:18:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.