Development of Occupancy Prediction Algorithm for Underground Parking Lots
This addresses perception issues for autonomous vehicles in dimly lit, challenging environments like basements, but appears incremental as it applies existing methods to a new scenario.
The study tackled perception challenges for autonomous driving in underground parking lots by developing a Transformer-based occupancy prediction algorithm, which was validated on a self-constructed dataset with satisfactory results.
The core objective of this study is to address the perception challenges faced by autonomous driving in adverse environments like basements. Initially, this paper commences with data collection in an underground garage. A simulated underground garage model is established within the CARLA simulation environment, and SemanticKITTI format occupancy ground truth data is collected in this simulated setting. Subsequently, the study integrates a Transformer-based Occupancy Network model to complete the occupancy grid prediction task within this scenario. A comprehensive BEV perception framework is designed to enhance the accuracy of neural network models in dimly lit, challenging autonomous driving environments. Finally, experiments validate the accuracy of the proposed solution's perception performance in basement scenarios. The proposed solution is tested on our self-constructed underground garage dataset, SUSTech-COE-ParkingLot, yielding satisfactory results.