  Vehicle-to-infrastructure (V2I) cooperative perception plays a crucial role
in autonomous driving scenarios. Despite its potential to improve perception
accuracy and robustness, the large amount of raw sensor data inevitably results
in high communication overhead. To mitigate this issue, we propose TOCOM-V2I, a
task-oriented communication framework for V2I cooperative perception, which
reduces bandwidth consumption by transmitting only task-relevant information,
instead of the raw data stream, for perceiving the surrounding environment. Our
contributions are threefold. First, we propose a spatial-aware feature
selection module to filter out irrelevant information based on spatial
relationships and perceptual prior. Second, we introduce a hierarchical entropy
model to exploit redundancy within the features for efficient compression and
transmission. Finally, we utilize a scaled dot-product attention architecture
to fuse vehicle-side and infrastructure-side features to improve perception
performance. Experimental results demonstrate the effectiveness of TOCOM-V2I.
