Planning-Oriented Cooperative Perception among Heterogeneous Vehicles

Vehicle-to-vehicle (V2V) based cooperative perception enhances autonomous driving by overcoming single-agent perception limitations such as occlusions, without relying on extensive infrastructure. However, most existing methods have two key limitations. They treat cooperative perception in isolation, with little consideration for downstream tasks such as planning, leading to poor coordination and inefficient planning decisions. They also assume perception model homogeneity across all vehicles, which can be impractical among vehicles from different manufacturers. To bridge such gaps, we propose Scout, an early-fusion framework for planning-oriented cooperative perception among vehicles of heterogeneous models. Specifically, we formalize a notion of Δθ-Risk Increment Distribution (RID) to capture the distribution of the risk increment by incomplete perception to the current trajectory plan, and define a Priority Index (PI) metric for prioritizing cooperative perception on riskier regions. We develop algorithms to estimate Δθ-RID and PI at run-time with theoretical bounds. Empirical results demonstrate that Scout surpasses state-of-the-art methods and strong baselines on challenging benchmarks, achieving higher success rates with only 3-10% of their communication volume.