CooperDrive: Enhancing Driving Decisions Through Cooperative Perception

TL;DR

CooperDrive is a cooperative perception framework for autonomous vehicles that improves safety and reaction times in occlusion-heavy scenarios by sharing lightweight object-level data.

Contribution

It introduces a novel, low-latency object-level sharing and fusion strategy that enhances situational awareness without heavy encoders or high bandwidth.

Findings

Increases reaction lead time and minimum TTC in real-world tests.

Requires only 90 kbps bandwidth and 89 ms latency.

Enhances safety by enabling earlier, predictive driving decisions.

Abstract

Autonomous vehicles equipped with robust onboard perception, localization, and planning still face limitations in occlusion and non-line-of-sight (NLOS) scenarios, where delayed reactions can increase collision risk. We propose CooperDrive, a cooperative perception framework that augments situational awareness and enables earlier, safer driving decisions. CooperDrive offers two key advantages: (i) each vehicle retains its native perception, localization, and planning stack, and (ii) a lightweight object-level sharing and fusion strategy bridges perception and planning. Specifically, CooperDrive reuses detector Bird's-Eye View (BEV) features to estimate accurate vehicle poses without additional heavy encoders, thereby reconstructing BEV representations and feeding the planner with low latency. On the planning side, CooperDrive leverages the expanded object set to anticipate potential conflicts earlier and adjust speed and trajectory proactively, thereby transforming reactive behaviors into predictive and safer driving decisions. Real-world closed-loop tests at occlusion-heavy NLOS intersections demonstrate that CooperDrive increases reaction lead time, minimum time-to-collision (TTC), and stopping margin, while requiring only 90 kbps bandwidth and maintaining an average end-to-end latency of 89 ms.