69 citations · 215 across the 26 of their papers we have counts for
31 papers
ReDrive: Shaping Representations with World Modeling for End-to-End Driving
Yueting Zhu, Shaoyu Chen, Yuehao Song +4
Driving policies require capabilities of scene understanding and future evolution prediction. To achieve this goal, current end-to-end models typically construct complex perception…
Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation
Yueting Zhu, Yuehao Song, Kaicheng Zhang +5
Streaming video generation holds strong potential for world modeling, where future frames must be inferred online sequentially to form a continuous video stream. However, streaming…
REAP: Reinforcement-Learning End-to-End Autonomous Parking with Gaussian Splatting Simulator for Real2Sim2Real Transfer
Changze Li, Zhe Chen, Shaoyu Chen +7
In recent years, autonomous parking has made significant advances, yet parking tasks still face challenges in extreme scenarios such as mechanical and dead-end parking slots, often…
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…
Senna-2: Aligning VLM and End-to-End Driving Policy for Consistent Decision Making and Planning
Yuehao Song, Shaoyu Chen, Hao Gao +8
Vision-language models (VLMs) enhance the planning capability of end-to-end (E2E) driving policy by leveraging high-level semantic reasoning. However, existing approaches often ove…
DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving
Jialv Zou, Shaoyu Chen, Bencheng Liao +6
Generative diffusion models for end-to-end autonomous driving often suffer from mode collapse, tending to generate conservative and homogeneous behaviors. While DiffusionDrive empl…
InfiniteVL: Synergizing Linear and Sparse Attention for Highly-Efficient, Unlimited-Input Vision-Language Models
Hongyuan Tao, Bencheng Liao, Shaoyu Chen +4
Vision-Language Models (VLMs) are increasingly tasked with ultra-long multimodal understanding. While linear architectures offer constant computation and memory footprints, they of…
ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving
Zhiyu Zheng, Shaoyu Chen, Haoran Yin +5
End-to-end autonomous driving (E2EAD) systems, which learn to predict future trajectories directly from sensor data, are fundamentally challenged by the inherent spatio-temporal im…
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…
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…
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…
ARGUS: Visualization of AI-Assisted Task Guidance in AR
Sonia Castelo, Joao Rulff, Erin McGowan +15
The concept of augmented reality (AR) assistants has captured the human imagination for decades, becoming a staple of modern science fiction. To pursue this goal, it is necessary t…
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…
TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors
Shaoyu Chen, Tianheng Cheng, Jiemin Fang +4
Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightwei…
VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale Driving Scene
Shaoyu Chen, Yunchi Zhang, Bencheng Liao +7
High-definition (HD) map serves as the essential infrastructure of autonomous driving. In this work, we build up a systematic vectorized map annotation framework (termed VMA) for e…
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…
BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation
Tianheng Cheng, Xinggang Wang, Shaoyu Chen +2
Labeling objects with pixel-wise segmentation requires a huge amount of human labor compared to bounding boxes. Most existing methods for weakly supervised instance segmentation fo…
MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction
Bencheng Liao, Shaoyu Chen, Xinggang Wang +4
High-definition (HD) map provides abundant and precise environmental information of the driving scene, serving as a fundamental and indispensable component for planning in autonomo…
Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation
Zhi Liu, Shaoyu Chen, Xiaojie Guo +6
In this work, we propose PolarBEV for vision-based uneven BEV representation learning. To adapt to the foreshortening effect of camera imaging, we rasterize the BEV space both angu…
Polar Parametrization for Vision-based Surround-View 3D Detection
Shaoyu Chen, Xinggang Wang, Tianheng Cheng +3
3D detection based on surround-view camera system is a critical technique in autopilot. In this work, we present Polar Parametrization for 3D detection, which reformulates position…