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Found 9 papers, 2 papers with code
Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning
Aspect Sentiment Triplet Extraction (ASTE) is a burgeoning subtask of fine-grained sentiment analysis, aiming to extract structured sentiment triplets from unstructured textual data.
Large Trajectory Models are Scalable Motion Predictors and Planners
STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task.
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments
Offline Reinforcement Learning (RL) has emerged as a promising framework for learning policies without active interactions, making it especially appealing for autonomous driving tasks.
Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills
Learning-based vehicle planning is receiving increasing attention with the emergence of diverse driving simulators and large-scale driving datasets.
P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving
Motion prediction is crucial in enabling safe motion planning for autonomous vehicles in interactive scenarios.
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.
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: 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.
Metaknowledge Extraction Based on Multi-Modal Documents
In this paper, we introduce the concept of metaknowledge to knowledge engineering research for the purpose of structural knowledge construction.
