A Synthetic Benchmark for Collaborative 3D Semantic Occupancy Prediction in V2X Autonomous Driving
This work addresses the problem of limited perception in autonomous driving due to occlusion and sensor constraints for researchers, but it is incremental as it builds on existing collaborative perception datasets and methods.
The authors tackled the lack of a dataset for collaborative 3D semantic occupancy prediction in autonomous driving by augmenting an existing dataset with dense annotations in CARLA, establishing benchmarks with varying ranges, and developing a baseline model that outperforms single-agent models, with gains increasing as the prediction range expands.
3D semantic occupancy prediction is an emerging perception paradigm in autonomous driving, providing a voxel-level representation of both geometric details and semantic categories. However, the perception capability of a single vehicle is inherently constrained by occlusion, restricted sensor range, and narrow viewpoints. To address these limitations, collaborative perception enables the exchange of complementary information, thereby enhancing the completeness and accuracy. In the absence of a dedicated dataset for collaborative 3D semantic occupancy prediction, we augment an existing collaborative perception dataset by replaying it in CARLA with a high-resolution semantic voxel sensor to provide dense and comprehensive occupancy annotations. In addition, we establish benchmarks with varying prediction ranges designed to systematically assess the impact of spatial extent on collaborative prediction. We further develop a baseline model that performs inter-agent feature fusion via spatial alignment and attention aggregation. Experimental results demonstrate that our baseline model consistently outperforms single-agent models, with increasing gains observed as the prediction range expands.