1 code implementation • 16 Jun 2025 • Zewei Zhou, Tianhui Cai, Seth Z. Zhao, Yun Zhang, Zhiyu Huang, Bolei Zhou, Jiaqi Ma
Recent advancements in Vision-Language-Action (VLA) models have shown promise for end-to-end autonomous driving by leveraging world knowledge and reasoning capabilities.
1 code implementation • 2 Dec 2024 • Zewei Zhou, Hao Xiang, Zhaoliang Zheng, Seth Z. Zhao, Mingyue Lei, Yun Zhang, Tianhui Cai, Xinyi Liu, Johnson Liu, Maheswari Bajji, Jacob Pham, Xin Xia, Zhiyu Huang, Bolei Zhou, Jiaqi Ma
Vehicle-to-everything (V2X) technologies offer a promising paradigm to mitigate the limitations of constrained observability in single-vehicle systems.
2 code implementations • 21 Jun 2024 • Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng, Zhiyu Huang, Zetong Yang, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, Kashyap Chitta
On a large set of challenging scenarios, we observe that simple methods with moderate compute requirements such as TransFuser can match recent large-scale end-to-end driving architectures such as UniAD.
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no code implementations • 4 Feb 2024 • Haochen Liu, Zhiyu Huang, Wenhui Huang, Haohan Yang, Xiaoyu Mo, Chen Lv
First, we introduce marginal-conditioned occupancy prediction to align joint occupancy with agent-wise perceptions.
1 code implementation • 24 Aug 2022 • Haochen Liu, Zhiyu Huang, Xiaoyu Mo, Chen Lv
Decision-making for urban autonomous driving is challenging due to the stochastic nature of interactive traffic participants and the complexity of road structures.
1 code implementation • 31 Jul 2022 • Haochen Liu, Zhiyu Huang, Chen Lv
Therefore, this paper proposes a novel Multi-modal Hierarchical Transformer network that fuses the vectorized (agent motion) and visual (scene flow, map, and occupancy) modalities and jointly predicts the flow and occupancy of the scene.
1 code implementation • IEEE Transactions on Intelligent Transportation Systems 2022 • Xiaoyu Mo, Zhiyu Huang, Yang Xing, Chen Lv
Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential for safe and efficient operation of connected automated vehicles under complex driving situations.
1 code implementation • 26 Sep 2021 • Jingda Wu, Zhiyu Huang, Wenhui Huang, Chen Lv
A novel prioritized experience replay mechanism that adapts to human guidance in the reinforcement learning process is proposed to boost the efficiency and performance of the reinforcement learning algorithm.
no code implementations • 23 Jun 2021 • Jingda Wu, Zhiyu Huang, Chen Lv
Then, a novel uncertainty-aware model-based RL framework is developed based on the adaptive truncation approach, providing virtual interactions between the agent and environment model, and improving RL's training efficiency and performance.