19 citations · 28 across the 14 of their papers we have counts for
Showing 2024Show all
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cs.CV2024★ 1 cited
EgoFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving
Haisheng Su, Wei Wu, Zhenjie Yang +1
Current End-to-End Autonomous Driving (E2E-AD) methods resort to unifying modular designs for various tasks (e.g. perception, prediction and planning). Although optimized with a fu…
cs.LG2024
UniZero: Generalized and Efficient Planning with Scalable Latent World Models
Yuan Pu, Yazhe Niu, Zhenjie Yang +3
Learning predictive world models is crucial for enhancing the planning capabilities of reinforcement learning (RL) agents. Recently, MuZero-style algorithms, leveraging the value e…
cs.RO2024★ 3 cited
Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
Xiaosong Jia, Zhenjie Yang, Qifeng Li +2
In an era marked by the rapid scaling of foundation models, autonomous driving technologies are approaching a transformative threshold where end-to-end autonomous driving (E2E-AD)…