A Lightweight Goal-Based model for Trajectory Prediction
We present a lightweight goal-based model for multimodal, probabilistic trajectory prediction for urban driving. Previous conditioned-on-goal methods have used map information in order to establish a set of potential goals and then complete the corresponding full trajectory for each goal. We instead propose two original representations, based on the agent's states and its kinematics, to extract the potential goals. In this paper, we conduct a comparative study between the two representations. We also evaluate our approach on the nuScenes dataset, and show that it outperforms a wide array of state-ofthe-art methods.
WOS:000934720604036
2022-01-01
978-1-6654-6880-0
New York
IEEE International Conference on Intelligent Transportation Systems-ITSC
4209
4214
REVIEWED
Event name | Event place | Event date |
Macau, PEOPLES R CHINA | Oct 08-12, 2022 | |