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Guofa Li

4 accepted papers

2024

A Cognitive-Driven Trajectory Prediction Model for Autonomous Driving in Mixed Autonomy Environments

IJCAI 2024poster

As autonomous driving technology progresses, the need for precise trajectory prediction models becomes paramount. This paper introduces an innovative model that infuses cognitive insights into trajectory prediction, focusing on perceived safety and dynamic decision-making. Distinct from traditional…

Cited by 11SourcePDFScholar
2024

BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

AAAI 2024technical

The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. To address this challenge, we pioneer a novel behavior-aware trajectory prediction model (BAT) that incorporates insights and findings from traffic p…

2024

MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving

IJCAI 2024poster

This paper introduces a trajectory prediction model tailored for autonomous driving, focusing on capturing complex interactions in dynamic traffic scenarios without reliance on high-definition maps. The model, termed MFTraj, harnesses historical trajectory data combined with a novel dynamic geometri…

Cited by 11SourcePDFScholar
2024

Physics-Informed Trajectory Prediction for Autonomous Driving under Missing Observation

IJCAI 2024poster

This paper introduces a novel trajectory prediction approach for autonomous vehicles (AVs), adeptly addressing the challenges of missing observations and the need for adherence to physical laws in real-world driving environments. This study proposes a hierarchical two-stage trajectory prediction mod…

Cited by 10SourcePDFScholar