← Search

Xingcheng Liu

2 accepted papers

2026

Differentiable Semantic Meta-Learning Framework for Long-Tail Motion Forecasting in Autonomous Driving

AAAI 2026technical

Long-tail motion forecasting is a core challenge for autonomous driving, where rare yet safety-critical events-such as abrupt maneuvers and dense multi-agent interactions-dominate real-world risk. Existing approaches struggle in these scenarios because they rely on either non-interpretable clusterin

Cited by 0SourcePDFScholar
2026

Predict and Resist: Long-Term Accident Anticipation Under Sensor Noise

AAAI 2026technical

Accident anticipation is essential for proactive and safe autonomous driving, where even a brief advance warning can enable critical evasive actions. However, two key challenges hinder real-world deployment: (1) noisy or degraded sensory inputs from weather, motion blur, or hardware limitations, and

Cited by 0SourcePDFScholar