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Jiachao Liu

3 accepted papers

2026

Decoupling Scene Perception and Ego Status: A Multi-Context Fusion Approach for Enhanced Generalization in End-to-End Autonomous Driving

AAAI 2026technical

Modular design of planning-oriented autonomous driving has markedly advanced end-to-end systems. However, existing architectures remain constrained by an over-reliance on ego status, hindering generalization and robust scene understanding. We identify the root cause as an inherent design within thes

Cited by 0SourcePDFScholar
2025

Certificating Safety of Imitation Learning for Autonomous Driving With Learnable Weighted Control Barrier Functions

RA-L 2025

Imitation learning is increasingly utilized to improve driving performance using real-world data, yet ensuring the safety of its outputs remains a fundamental challenge. While differentiable optimization-based methods are widely employed to enhance safety of imitation planner, their joint training o

Cited by 0SourceScholar
2025

TrajFlow: Multi-modal Motion Prediction via Flow Matching

IROS 2025

Efficient and accurate motion prediction is crucial for ensuring safety and informed decision-making in autonomous driving, particularly under dynamic real-world conditions that necessitate multi-modal forecasts. We introduce TrajFlow, a novel flow matching-based motion prediction framework that add

Cited by 5SourcecodeScholar