activity
20242026
most citedVADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning

3 citations · 4 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2026

ConDenseDrive: Knowledge-Dense, Token-Sparse Visual Encoder for End-to-End Autonomous Driving

Shengzhen Li, Yongkang Li, Hao Gao +5

End-to-end driving requires rich spatiotemporal representations to support scene understanding and trajectory planning under strict latency constraints. However, most systems initi…

cs.CV2026

Instance-level Visual Active Tracking with Occlusion-Aware Planning

Haowei Sun, Kai Zhou, Hao Gao +5

Visual Active Tracking (VAT) aims to control cameras to follow a target in 3D space, which is critical for applications like drone navigation and security surveillance. However, it…

cs.CV2026

RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework

Hao Gao, Shaoyu Chen, Yifan Zhu +4

High-level autonomous driving requires motion planners capable of modeling multimodal future uncertainties while remaining robust in closed-loop interactions. Although diffusion-ba…

cs.CV2025

RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning

Hao Gao, Shaoyu Chen, Bo Jiang +11

Existing end-to-end autonomous driving (AD) algorithms typically follow the Imitation Learning (IL) paradigm, which faces challenges such as causal confusion and an open-loop gap.…

cs.RO2025★ 1 cited

RoboBERT: An End-to-end Multimodal Robotic Manipulation Model

Sicheng Wang, Sheng Liu, Weiheng Wang +2

Embodied intelligence seamlessly integrates vision, language, and action.~However, most multimodal robotic models rely on massive fine-tuning, incurring high time and hardware cost…

cs.RO2024

LASER: Script Execution by Autonomous Agents for On-demand Traffic Simulation

Hao Gao, Jingyue Wang, Wenyang Fang +4

Autonomous Driving Systems (ADS) require diverse and safety-critical traffic scenarios for effective training and testing, but the existing data generation methods struggle to prov…

cs.CV2024★ 3 cited

VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning

Bo Jiang, Shaoyu Chen, Hao Gao +4

Learning a human-like driving policy from large-scale driving demonstrations is promising, but the uncertainty and non-deterministic nature of planning make it challenging. Existin…