Grunnleggende konsepter
TrajectoryNAS introduces a pioneering method utilizing point cloud data for trajectory prediction, enhancing the performance of autonomous driving systems.
Sammendrag
Autonomous driving systems rely on trajectory prediction to anticipate movements of surrounding objects.
TrajectoryNAS automates the design of trajectory prediction models using Neural Architecture Search (NAS).
The approach integrates object detection, tracking, and forecasting to improve accuracy and efficiency in trajectory modeling.
Experimental results show TrajectoryNAS outperforms competitors with higher accuracy and lower latency on the NuScenes dataset.
Contributions include novel energy function, efficient mini dataset utilization, and multi-objective optimization.
The framework includes VoxelNet Backbone and Sparse Feature Pyramid Network for spatial feature extraction.
TrajectoryNAS demonstrates superior performance in car and pedestrian trajectory prediction compared to state-of-the-art methods.
Statistikk
TrajcetoryNAS yield a minimum of 4.8 higger accuracy and 1.1* lower latency over competing methods on the NuScenes dataset.