We propose a new image-free semantic segmentation model, referred to as the IFSeg.
We propose a patch sampling method, referred to as the K-centered patch sampling, which uses the greedy K-center search for video transformers.
We propose a novel prediction model, referred to as the lane-aware prediction (LaPred) network, which uses the instance-level lane entities extracted from a semantic map to predict the multi-modal future trajectories.
We propose a model that synthesizes multiple input signals from the multimodal world|the environment’s scene context and interactions between multiple surrounding agents|to best model all diverse and admissible trajectories.
We propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time.