8 citations · 32 across the 11 of their papers we have counts for
13 papers
Empowering Multi-Robot Cooperation via Sequential World Models
Zijie Zhao, Honglei Guo, Shengqian Chen +4
Model-based reinforcement learning (MBRL) has achieved remarkable success in robotics due to its high sample efficiency and planning capability. However, extending MBRL to physical…
AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning
Bo Jiang, Shaoyu Chen, Qian Zhang +2
OpenAI o1 and DeepSeek R1 achieve or even surpass human expert-level performance in complex domains like mathematics and science, with reinforcement learning (RL) and reasoning pla…
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving
Zebin Xing, Xingyu Zhang, Yang Hu +5
We propose GoalFlow, an end-to-end autonomous driving method for generating high-quality multimodal trajectories. In autonomous driving scenarios, there is rarely a single suitable…
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.…
DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving
Bencheng Liao, Shaoyu Chen, Haoran Yin +8
Recently, the diffusion model has emerged as a powerful generative technique for robotic policy learning, capable of modeling multi-mode action distributions. Leveraging its capabi…
Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving
Bo Jiang, Shaoyu Chen, Bencheng Liao +6
End-to-end autonomous driving demonstrates strong planning capabilities with large-scale data but still struggles in complex, rare scenarios due to limited commonsense. In contrast…
Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning
Hongpei Li, Han Zhang, Ziyan He +4
The Integrated Process Planning and Scheduling (IPPS) problem combines process route planning and shop scheduling to achieve high efficiency in manufacturing and maximize resource…
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…
MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction
Bencheng Liao, Shaoyu Chen, Yunchi Zhang +5
High-definition (HD) map provides abundant and precise static environmental information of the driving scene, serving as a fundamental and indispensable component for planning in a…
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
Bo Jiang, Shaoyu Chen, Qing Xu +7
Autonomous driving requires a comprehensive understanding of the surrounding environment for reliable trajectory planning. Previous works rely on dense rasterized scene representat…
Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction
Bencheng Liao, Shaoyu Chen, Bo Jiang +5
Online lane graph construction is a promising but challenging task in autonomous driving. Previous methods usually model the lane graph at the pixel or piece level, and recover the…
Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Prediction
Bo Jiang, Shaoyu Chen, Xinggang Wang +7
Motion prediction is highly relevant to the perception of dynamic objects and static map elements in the scenarios of autonomous driving. In this work, we propose PIP, the first en…
HOPE: Hierarchical Spatial-temporal Network for Occupancy Flow Prediction
Yihan Hu, Wenxin Shao, Bo Jiang +6
In this report, we introduce our solution to the Occupancy and Flow Prediction challenge in the Waymo Open Dataset Challenges at CVPR 2022, which ranks 1st on the leaderboard. We h…