4 citations · 6 across the 8 of their papers we have counts for
8 papers
Adaptive Prediction Ensemble: Improving Out-of-Distribution Generalization of Motion Forecasting
Jinning Li, Jiachen Li, Sangjae Bae +1
Deep learning-based trajectory prediction models for autonomous driving often struggle with generalization to out-of-distribution (OOD) scenarios, sometimes performing worse than s…
Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration
Jinning Li, Xinyi Liu, Banghua Zhu +4
Safe Reinforcement Learning (RL) aims to find a policy that achieves high rewards while satisfying cost constraints. When learning from scratch, safe RL agents tend to be overly co…
Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization
Yuxin Chen, Chen Tang, Ran Tian +4
Generalization poses a significant challenge in Multi-agent Reinforcement Learning (MARL). The extent to which an agent is influenced by unseen co-players depends on the agent's po…
Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning
Jinning Li, Chen Tang, Masayoshi Tomizuka +1
Offline Reinforcement learning (RL) has shown potent in many safe-critical tasks in robotics where exploration is risky and expensive. However, it still struggles to acquire skills…
Dealing with the Unknown: Pessimistic Offline Reinforcement Learning
Jinning Li, Chen Tang, Masayoshi Tomizuka +1
Reinforcement Learning (RL) has been shown effective in domains where the agent can learn policies by actively interacting with its operating environment. However, if we change the…
Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking
Jiachen Li, Hengbo Ma, Zhihao Zhang +2
An effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are indispensable for intelligent mobile systems (e.g. autonomous…
Interaction-Aware Behavior Planning for Autonomous Vehicles Validated with Real Traffic Data
Jinning Li, Liting Sun, Wei Zhan +1
Autonomous vehicles (AVs) need to interact with other traffic participants who can be either cooperative or aggressive, attentive or inattentive. Such different characteristics can…
A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning
Jinning Li, Liting Sun, Jianyu Chen +2
Autonomous vehicles need to handle various traffic conditions and make safe and efficient decisions and maneuvers. However, on the one hand, a single optimization/sampling-based mo…