# CooperRisk: A Driving Risk Quantification Pipeline with Multi-Agent Cooperative Perception and Prediction

arXiv: https://arxiv.org/abs/2506.15868 · HTML: https://mlsanity.com/paper/2506.15868/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 21 of its 36 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 21 of 21)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication](https://mlsanity.com/paper/2109.07644/) (2021)
- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [Scene Transformer: A unified architecture for predicting multiple agent trajectories](https://mlsanity.com/paper/2106.08417/) (2021)
- [MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying](https://mlsanity.com/paper/2306.17770/) (2023)
- [ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries](https://mlsanity.com/paper/2208.01582/) (2022)
- [HM-ViT: Hetero-modal Vehicle-to-Vehicle Cooperative perception with vision transformer](https://mlsanity.com/paper/2304.10628/) (2023)
- [V2X-Real: a Large-Scale Dataset for Vehicle-to-Everything Cooperative Perception](https://mlsanity.com/paper/2403.16034/) (2024)
- [ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning](https://mlsanity.com/paper/2206.13387/) (2022)
- [QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction](https://mlsanity.com/paper/2306.10508/) (2023)
- [V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction](https://mlsanity.com/paper/2412.01812/) (2024)
- [Learning Cooperative Trajectory Representations for Motion Forecasting](https://mlsanity.com/paper/2311.00371/) (2023)
- [Co-MTP: A Cooperative Trajectory Prediction Framework with Multi-Temporal Fusion for Autonomous Driving](https://mlsanity.com/paper/2502.16589/) (2025)
- [Learning Risk-aware Costmaps for Traversability in Challenging Environments](https://mlsanity.com/paper/2107.11722/) (2021)
- [Autonomous Vehicles Meet the Physical World: RSS, Variability, Uncertainty, and Proving Safety (Expanded Version)](https://mlsanity.com/paper/1911.01207/) (2019)
- [Uncertainty-Aware Prediction and Application in Planning for Autonomous Driving: Definitions, Methods, and Comparison](https://mlsanity.com/paper/2403.02297/) (2024)
- [A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model](https://mlsanity.com/paper/2408.12805/) (2024)

## Built on by (most cited first, 4 of 4)

- [QuantV2X: A Fully Quantized Multi-Agent System for Cooperative Perception](https://mlsanity.com/paper/2509.03704/) (2025)
- [TurboTrain: Towards Efficient and Balanced Multi-Task Learning for Multi-Agent Perception and Prediction](https://mlsanity.com/paper/2508.04682/) (2025)
- [Risk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planning](https://mlsanity.com/paper/2508.07686/) (2025)
- [MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems](https://mlsanity.com/paper/2605.10904/) (2026)

## Abstract (arXiv)

Risk quantification is a critical component of safe autonomous driving, however, constrained by the limited perception range and occlusion of single-vehicle systems in complex and dense scenarios. Vehicle-to-everything (V2X) paradigm has been a promising solution to sharing complementary perception information, nevertheless, how to ensure the risk interpretability while understanding multi-agent interaction with V2X remains an open question. In this paper, we introduce the first V2X-enabled risk quantification pipeline, CooperRisk, to fuse perception information from multiple agents and quantify the scenario driving risk in future multiple timestamps. The risk is represented as a scenario risk map to ensure interpretability based on risk severity and exposure, and the multi-agent interaction is captured by the learning-based cooperative prediction model. We carefully design a risk-oriented transformer-based prediction model with multi-modality and multi-agent considerations. It aims to ensure scene-consistent future behaviors of multiple agents and avoid conflicting predictions that could lead to overly conservative risk quantification and cause the ego vehicle to become overly hesitant to drive. Then, the temporal risk maps could serve to guide a model predictive control planner. We evaluate the CooperRisk pipeline in a real-world V2X dataset V2XPnP, and the experiments demonstrate its superior performance in risk quantification, showing a 44.35% decrease in conflict rate between the ego vehicle and background traffic participants.