322 citations · 524 across the 10 of their papers we have counts for
11 papers
Language2Drive: Do Vision-Language-Action Models Really Follow the Instructions in Autonomous Driving?
Zihan You, Zehua Fan, Wenqi Lyu +19
Recent Vision-Language-Action (VLA) models increasingly use natural-language instructions to condition autonomous driving behavior. Yet existing closed-loop benchmarks primarily me…
AnyScene: Towards Highly Controllable Driving Scene Generation at Anywhere and Beyond
Haiming Zhang, Junfei Zhou, Feng Jiang +6
Generating high-fidelity and controllable synthetic data is critical for advancing end-to-end autonomous driving, particularly for addressing the long tail of rare safety-critical…
Driving Intents Amplify Planning-Oriented Reinforcement Learning
Hengtong Lu, Victor Shea-Jay Huang, Chengmin Yang +4
Continuous-action policies trained on a single demonstrated trajectory per scene suffer from mode collapse: samples cluster around the demonstrated maneuver and the policy cannot r…
MindVLA-U1: VLA Beats VA with Unified Streaming Architecture for Autonomous Driving
Yuzhou Huang, Benjin Zhu, Hengtong Lu +6
Autonomous driving has progressed from modular pipelines toward end-to-end unification, and Vision-Language-Action (VLA) models are a natural extension of this journey beyond Visio…
Action Emergence from Streaming Intent
Pengfei Jing, Victor Shea-Jay Huang, Hengtong Lu +3
We formalize action emergence as a target capability for end-to-end autonomous driving: the ability to generate physically feasible, semantically appropriate, and safety-compliant…
TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses
Xuesong Chen, Shaoshuai Shi, Chao Zhang +5
3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots. With the commonly used tracking-by-detection paradigm, 3D MO…
ConQueR: Query Contrast Voxel-DETR for 3D Object Detection
Benjin Zhu, Zhe Wang, Shaoshuai Shi +3
Although DETR-based 3D detectors can simplify the detection pipeline and achieve direct sparse predictions, their performance still lags behind dense detectors with post-processing…
MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection
Xuesong Chen, Shaoshuai Shi, Benjin Zhu +3
Accurate and reliable 3D detection is vital for many applications including autonomous driving vehicles and service robots. In this paper, we present a flexible and high-performanc…
EqCo: Equivalent Rules for Self-supervised Contrastive Learning
Benjin Zhu, Junqiang Huang, Zeming Li +2
In this paper, we propose EqCo (Equivalent Rules for Contrastive Learning) to make self-supervised learning irrelevant to the number of negative samples in the contrastive learning…
AutoAssign: Differentiable Label Assignment for Dense Object Detection
Benjin Zhu, Jianfeng Wang, Zhengkai Jiang +4
Determining positive/negative samples for object detection is known as label assignment. Here we present an anchor-free detector named AutoAssign. It requires little human knowledg…
Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
Benjin Zhu, Zhengkai Jiang, Xiangxin Zhou +2
This report presents our method which wins the nuScenes3D Detection Challenge [17] held in Workshop on Autonomous Driving(WAD, CVPR 2019). Generally, we utilize sparse 3D convoluti…