A Benchmark for Vision-Centric HD Mapping by V2I Systems
This work addresses the problem of enabling safer autonomous driving through improved HD mapping for researchers and practitioners in the field, though it is incremental as it builds on existing vision-centric V2I systems.
The paper tackles the lack of real-world datasets for vectorized HD mapping in vehicle-infrastructure cooperative autonomous driving by releasing a dataset with collaborative camera frames and human annotations, and presents an end-to-end neural framework (V2I-HD) that achieves superior real-time inference speed and robust map construction quality in complex driving scenes.
Autonomous driving faces safety challenges due to a lack of global perspective and the semantic information of vectorized high-definition (HD) maps. Information from roadside cameras can greatly expand the map perception range through vehicle-to-infrastructure (V2I) communications. However, there is still no dataset from the real world available for the study on map vectorization onboard under the scenario of vehicle-infrastructure cooperation. To prosper the research on online HD mapping for Vehicle-Infrastructure Cooperative Autonomous Driving (VICAD), we release a real-world dataset, which contains collaborative camera frames from both vehicles and roadside infrastructures, and provides human annotations of HD map elements. We also present an end-to-end neural framework (i.e., V2I-HD) leveraging vision-centric V2I systems to construct vectorized maps. To reduce computation costs and further deploy V2I-HD on autonomous vehicles, we introduce a directionally decoupled self-attention mechanism to V2I-HD. Extensive experiments show that V2I-HD has superior performance in real-time inference speed, as tested by our real-world dataset. Abundant qualitative results also demonstrate stable and robust map construction quality with low cost in complex and various driving scenes. As a benchmark, both source codes and the dataset have been released at OneDrive for the purpose of further study.