publications

2026

1. 

   TIC-VLA: A Think-in-Control Vision-Language-Action Model for Robot Navigation in Dynamic Environments

   International Conference on Machine Learning (ICML), 2026

   Zhiyu Huang*, Yun Zhang*, Johnson Liu, Rui Song, Chen Tang, and Jiaqi Ma

   Abs

   Robots in dynamic, human-centric environments must follow language instructions while maintaining real-time reactive control. Vision-language-action (VLA) models offer a promising framework, but they assume temporally aligned reasoning and control, despite semantic inference being inherently delayed relative to real-time action. We introduce Think-in-Control (TIC)-VLA, a latency-aware framework that explicitly models delayed semantic reasoning during action generation. TIC-VLA defines a delayed semantic-control interface that conditions action generation on delayed vision-language semantic states and explicit latency metadata, in addition to current observations, enabling policies to compensate for asynchronous reasoning. We further propose a latency-consistent training pipeline that injects reasoning inference delays during imitation learning and online reinforcement learning, aligning training with asynchronous deployment. To support realistic evaluation, we present DynaNav, a physics-accurate, photo-realistic simulation suite for language-guided navigation in dynamic environments. Extensive experiments in simulation and on a real robot show that TIC-VLA consistently outperforms prior VLA models while maintaining robust real-time control under multi-second reasoning latency.

2025

1. 

   MIC-BEV: Multi-Infrastructure Camera Bird’s-Eye-View Transformer with Relation-Aware Fusion for 3D Object Detection

   Under Review, Best Paper Award (Third Prize)for ICCV 2025DriveX Workshop, 2025

   Yun Zhang, Zhaoliang Zheng, Johnson Liu, Zhiyu Huang, Zewei Zhou, Zonglin Meng, Tianhui Cai, and Jiaqi Ma

   Abs

   Infrastructure-based perception plays a crucial role in intelligent transportation systems, offering global situational awareness and enabling cooperative autonomy. However, existing camera-based detection models often underperform in such scenarios due to challenges such as multi-view infrastructure setup, diverse camera configurations, degraded visual inputs, and various road layouts. We introduce MIC-BEV, a Transformer-based bird’s-eye-view (BEV) perception framework for infrastructure-based multi-camera 3D object detection. MIC-BEV flexibly supports a variable number of cameras with heterogeneous intrinsic and extrinsic parameters and demonstrates strong robustness under sensor degradation. The proposed graph-enhanced fusion module in MIC-BEV integrates multi-view image features into the BEV space by exploiting geometric relationships between cameras and BEV cells alongside latent visual cues. To support training and evaluation, we introduce M2I, a synthetic dataset for infrastructure-based object detection, featuring diverse camera configurations, road layouts, and environmental conditions. Extensive experiments on both M2I and the real-world dataset RoScenes demonstrate that MIC-BEV achieves state-of-the-art performance in 3D object detection. It also remains robust under challenging conditions, including extreme weather and sensor degradation. These results highlight the potential of MIC-BEV for real-world deployment.

2. 

   InSPE: Rapid Evaluation of Heterogeneous Multi-Modal Infrastructure Sensor Placement

   Under Review, 2025

   Zhaoliang Zheng*, Yun Zhang*, Zonglin Meng, Johnson Liu, Xin Xia, Jiaqi Ma

   Abs

   This paper introduces InSPE, a framework for evaluating heterogeneous multi-modal infrastructure sensor placement by integrating metrics like sensor coverage, occlusion, and information gain, supported by a new dataset and benchmarking experiments to optimize perception in intelligent intersections.

3. 

   AgentAlign: Misalignment-Adapted Multi-Agent Perception for Resilient Inter-Agent Sensor Correlations

   IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2025

   Zonglin Meng, Yun Zhang, Zhaoliang Zheng, Seth Z. Zhao, Jiaqi Ma

   Abs

   This work presents AgentAlign, a real-world multi-agent perception framework that mitigates multi-modality misalignment in cooperative autonomous systems using cross-modality feature alignment and introduces the V2XSet-Noise dataset for robust evaluation.

4. 

   RelMap: Enhancing Online Map Construction with Class-Aware Spatial Relation and Semantic Priors

   International Conference on Robotics and Automation (ICRA), 2025

   Tianhui Cai, Yun Zhang, Zewei Zhou, Zhiyu Huang, Jiaqi Ma

   Abs

   RelMap is an online HD map construction framework that enhances vectorized map generation using class-aware spatial relations and semantic priors, significantly improving accuracy and data efficiency on nuScenes and Argoverse 2 datasets.

5. 

   V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction

   International Conference on Computer Vision (ICCV), 2025

   Zewei Zhou, Hao Xiang, Zhaoliang Zheng, Seth Z. Zhao, Mingyue Lei, Yun Zhang, Tianhui Cai, Xinyi Liu, Johnson Liu, Maheswari Bajji, Xin Xia, Zhiyu Huang, Bolei Zhou, Jiaqi Ma

   Abs

   V2XPnP proposes a spatio-temporal fusion framework for multi-agent perception and prediction, leveraging a Transformer-based architecture and a novel sequential dataset to benchmark when, what, and how to fuse information in V2X scenarios.

6. 

   MDG: Multi-Agent Behavior Modeling in Traffic Scenarios through Masked Denoising Generation

   CVPR 2026 Findings Track, 2025

   Zhiyu Huang, Zewei Zhou, Tianhui Cai, Yun Zhang, Jiaqi Ma

   Abs

   MDG is a novel generative model that formulates multi-agent trajectory generation as a masked denoising process.

7. 

   AutoVLA: Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning

   Neural Information Processing Systems (NeurIPS), 2025

   Zhiyu Huang, Zewei Zhou, Tianhui Cai, Seth Z. Zhao, Yun Zhang, Jiaqi Ma

   Abs

   AutoVLA is a vision-language-action model for end-to-end autonomous driving with adaptive reasoning and reinforcement fine-tuning capabilities.