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Found 6 papers, 1 papers with code
Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles
A method that facilitates easily controllable scenario generation for efficient autonomous vehicles (AV) testing with realistic and challenging situations is greatly needed.
Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems
The complexity of predicting criticality arises from the extreme data imbalance caused by rare events in high dimensional variables associated with the rare events, a challenge we refer to as the curse of rarity.
Action-conditioned On-demand Motion Generation
We propose a novel framework, On-Demand MOtion Generation (ODMO), for generating realistic and diverse long-term 3D human motion sequences conditioned only on action types with an additional capability of customization.
Ranked #1 on
Human action generation
on UESTC RGB-D
KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long-Term Trajectory Prediction
Predicting future trajectories of road agents is a critical task for autonomous driving.
Diverse Imitation Learning via Self-Organizing Generative Models
Imitation learning is the task of replicating expert policy from demonstrations, without access to a reward function.
Diverse Imitation Learning via Self-OrganizingGenerative Models
To address this problem, we introduce a novel generative model for behavior cloning, in a mode-separating manner.
