← Search

Anzheng Wang

2 accepted papers

2026

ARTEMIS: Autoregressive End-To-End Trajectory Planning with Mixture of Experts for Autonomous Driving

ICRA 2026poster

This paper presents ARTEMIS, an end-to-end autonomous driving framework that combines autoregressive trajectory planning with Mixture-of-Experts (MoE). Traditional modular methods suffer from error propagation, while existing end-to-end models typically employ static one-shot inference paradigms tha…

2026

ARTEMIS: Autoregressive End-to-End Trajectory Planning With Mixture of Experts for Autonomous Driving

RA-L 2026

This paper presents ARTEMIS, an end-to-end autonomous driving framework that combines autoregressive trajectory planning with Mixture-of-Experts (MoE). Traditional modular methods suffer from error propagation, while existing end-to-end models typically employ static one-shot inference paradigms tha

Cited by 38SourcecodeScholar