2 citations · 4 across the 5 of their papers we have counts for
5 papers
GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction
Muleilan Pei, Shaoshuai Shi, Lu Zhang +2
Trajectory prediction for surrounding agents is a challenging task in autonomous driving due to its inherent uncertainty and underlying multimodality. Unlike prevailing data-driven…
Multi-modal Integrated Prediction and Decision-making with Adaptive Interaction Modality Explorations
Tong Li, Lu Zhang, Sikang Liu +1
Navigating dense and dynamic environments poses a significant challenge for autonomous driving systems, owing to the intricate nature of multimodal interaction, wherein the actions…
SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving
Lu Zhang, Peiliang Li, Sikang Liu +1
This paper presents a Simple and effIcient Motion Prediction baseLine (SIMPL) for autonomous vehicles. Unlike conventional agent-centric methods with high accuracy but repetitive c…
MARC: Multipolicy and Risk-aware Contingency Planning for Autonomous Driving
Tong Li, Lu Zhang, Sikang Liu +1
Generating safe and non-conservative behaviors in dense, dynamic environments remains challenging for automated vehicles due to the stochastic nature of traffic participants' behav…
An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments
Zhichao Han, Yuwei Wu, Tong Li +8
As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial…