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Xingcheng Zhou

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model

Xingcheng Zhou
Xuyuan Han
Feng Yang
Yunpu Ma
Volker Tresp
Alois Knoll

We present OpenDriveVLA, a Vision-Language Action (VLA) model designed for end-to-end autonomous driving, built upon open-source large language models. OpenDriveVLA generates spatially-grounded driving actions by leveraging multimodal inputs, including both 2D and 3D instance-aware visual representations, ego vehicle states, and language commands. To bridge the modality gap between driving visual representations and language embeddings, we introduce a hierarchical vision-language alignment process, projecting both 2D and 3D structured visual tokens into a unified semantic space. Furthermore, we incorporate structured agent–environment–ego interaction modeling into the autoregressive decoding process, enabling the model to capture fine-grained spatial dependencies and behavior-aware dynamics critical for reliable trajectory planning. Extensive experiments on the nuScenes dataset demonstrate that OpenDriveVLA achieves state-of-the-art results across open-loop trajectory planning and driving-related question-answering tasks. Qualitative analyses further illustrate its superior capability to follow high-level driving commands and robustly generate trajectories under challenging scenarios, highlighting its potential for next-generation end-to-end autonomous driving.

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IROS Conference 2025 Conference Paper

MambaSFLNet: A Mamba-based Model for Low-Light Image Enhancement with Spatial and Frequency Features

Mingyu Liu
Yuning Cui 0001
Leah Strand
Xingcheng Zhou
Jiajie Zhang
Alois C. Knoll

Low-light image enhancement (LLIE) aims to enhance the illumination of images that are captured under dark conditions, which is critical for various applications in dim environments, such as robotics and autonomous driving. Existing convolutional neural network (CNN)-based methods usually struggle to capture long-range dependencies, while transformer-based methods, despite their effectiveness, are resource-consuming. Besides, the frequency domain includes important lightness degradation information. To this end, we propose a Mamba-based framework called MambaSFLNet to effectively address LLIE by integrating spatial and frequency features. Our approach utilizes the Visual State Space Module to establish relationships across different regions of the input image while maintaining low model complexity. Furthermore, The spatial module not only balances illumination distribution but also suppresses noise and artifacts during enhancement. In addition, the frequency module enhances image contrast and sharpness by leveraging frequency-domain information. Extensive experiments on nine widely used benchmarks demonstrate that our approach achieves superior performance and exhibits strong generalization capabilities compared to existing methods. The codes are available at https://github.com/MingyuLiu1/MambaSFLNet.git

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ICML Conference 2025 Conference Paper

TUMTraf VideoQA: Dataset and Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes

Xingcheng Zhou
Konstantinos Larintzakis
Hao Guo
Walter Zimmer
Mingyu Liu
Hu Cao
Jiajie Zhang
Venkatnarayanan Lakshminarasimhan

We present TUMTraf VideoQA, a novel dataset and benchmark designed for spatio-temporal video understanding in complex roadside traffic scenarios. The dataset comprises 1, 000 videos, featuring 85, 000 multiple-choice QA pairs, 2, 300 object captioning, and 5, 700 object grounding annotations, encompassing diverse real-world conditions such as adverse weather and traffic anomalies. By incorporating tuple-based spatio-temporal object expressions, TUMTraf VideoQA unifies three essential tasks—multiple-choice video question answering, referred object captioning, and spatio-temporal object grounding—within a cohesive evaluation framework. We further introduce the TraffiX-Qwen baseline model, enhanced with visual token sampling strategies, providing valuable insights into the challenges of fine-grained spatio-temporal reasoning. Extensive experiments demonstrate the dataset’s complexity, highlight the limitations of existing models, and position TUMTraf VideoQA as a robust foundation for advancing research in intelligent transportation systems. The dataset and benchmark are publicly available to facilitate further exploration.

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