Search Results for author:
Found 6 papers, 3 papers with code
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
We introduce DriveVLM, an autonomous driving system leveraging Vision-Language Models (VLMs) for enhanced scene understanding and planning capabilities.
VectorFlow: Combining Images and Vectors for Traffic Occupancy and Flow Prediction
Recently, the occupancy flow fields representation was proposed to represent joint future states of road agents through a combination of occupancy grid and flow, which supports efficient and consistent joint predictions.
ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries
In this work, we propose ViP3D, a query-based visual trajectory prediction pipeline that exploits rich information from raw videos to directly predict future trajectories of agents in a scene.
M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction
Predicting future motions of road participants is an important task for driving autonomously in urban scenes.
DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets
In this work, we propose an anchor-free and end-to-end trajectory prediction model, named DenseTNT, that directly outputs a set of trajectories from dense goal candidates.
DenseTNT: Waymo Open Dataset Motion Prediction Challenge 1st Place Solution
In autonomous driving, goal-based multi-trajectory prediction methods are proved to be effective recently, where they first score goal candidates, then select a final set of goals, and finally complete trajectories based on the selected goals.
