CooperRisk: A Driving Risk Quantification Pipeline with Multi-Agent Cooperative Perception and Prediction (2506.15868v1)

Abstract: 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.

An Expert Review on "CooperRisk: A Driving Risk Quantification Pipeline with Multi-Agent Cooperative Perception and Prediction"

The paper "CooperRisk: A Driving Risk Quantification Pipeline with Multi-Agent Cooperative Perception and Prediction" presents a novel framework for quantifying driving risk in connected autonomous vehicle (CAV) environments. This framework leverages the capabilities of Vehicle-to-Everything (V2X) communication to incorporate data from multiple agents, enhancing both perception accuracy and interpretability of risk evaluations in complex traffic scenarios.

Key Contributions

1. Integrated V2X Pipeline: CooperRisk is proposed as the first comprehensive risk quantification pipeline that takes full advantage of V2X technology. This integration not only enhances the perception capabilities of individual vehicles but also provides a robust mechanism to capture and predict multi-agent interactions essential for understanding driving risks.
2. Scene-Consistent Prediction Model: A standout feature of CooperRisk is the development of a transformer-based prediction model. This model is designed to ensure that predicted trajectories are mutually consistent across multiple agents. By avoiding conflicting predictions, the pipeline mitigates unnecessary conservativeness in risk estimation that could result in overly cautious driving behavior.
3. Risk Map Generation for Planning: With the trajectory predictions, CooperRisk constructs scenario-specific risk maps that depict risk distributions in future timestamps. These maps provide a visual and quantifiable representation of risk severity and exposure, which downstream planning algorithms can use to make informed decisions.
4. Evaluation on Real-World Dataset: The authors validate their approach using the V2XPnP dataset, showcasing substantial improvements in risk quantification metrics. Notably, CooperRisk achieves a 44.35% reduction in conflict rates between ego vehicles and other traffic participants, which underscores its practical effectiveness.

Implications and Future Directions

The introduction of CooperRisk addresses several limitations faced by previous systems constrained by single-agent perception. By employing V2X-enabled cooperative perception, this framework provides an enhanced situational awareness that is crucial for autonomous navigation in dense urban environments. The approach strikes a balance between rule-based and learning-based methodologies, resulting in both interpretable and accurate driving risk assessments.

From a theoretical perspective, the paper advances multi-agent prediction models by proposing a unified approach that considers multi-modal and agent interactions simultaneously. This could influence future research in autonomous systems where the interaction dynamics are complex and require sophisticated modeling techniques.

In terms of practical applications, CooperRisk sets a precedent for developing integrated systems that not only perceive but also predict and plan efficiently by referencing quantified risk maps. Future developments could include expanding the application of similar frameworks to other domains of autonomous navigation and transport safety systems, potentially incorporating advancements in machine learning and increased computational power.

Overall, the contribution of CooperRisk is significant in enhancing the safety and efficiency of autonomous driving through advanced V2X communications and an integrated perception-prediction-planning pipeline. It marks a step forward in using collaborative technologies to address safety challenges in autonomous vehicles.