18 papers
ReWAM: Reciprocal World Action Models for Interactive Autonomous Driving
Benshan Ma, Pei Liu, Ruiguo Zhong +4
In interactive scenarios, an autonomous driving system is required to generate ego actions under the influence of other agents' behaviors. Existing World Action Models (WAMs) typic…
Where Do Embodied Decisions Come From? Rethinking Latent and Explicit Reasoning
Yuan Lin, Ziyue Zhou, JinLong Zhao +4
Chain-of-Thought (CoT) reasoning is increasingly incorporated into Vision-Language-Action (VLA) models, yet it can degrade the performance of stronger embodied agents. We investiga…
From Scene Graphs to Answers: Selective Neuro-Symbolic Reasoning for Autonomous Driving
Yiyao Wang, Pei Liu, Fangzhou Liu +1
Autonomous-driving question answering requires reasoning over structured scene information, yet existing vision-language approaches largely delegate heterogeneous reasoning operati…
Continuous Actions from Discrete Minds: Latent-Aligned Planning for End-to-End Autonomous Driving
Ruoyu Yao, Yusen Xie, Qingzhao Liu +5
Bridging the gap between the discrete reasoning of Vision-Language Models and the continuous, physics-constrained nature of autonomous driving remains a significant challenge. In t…
DA-WAM: Decision-Aligned Future Latents for Driving World Models
Ruiguo Zhong, Benshan Ma, Xiaolong Chen +5
World models for autonomous driving should anticipate the consequences that matter for driving decisions rather than reconstruct every detail of future scenes. However, predictive…
LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments
Pei Liu, Nan Zheng, Lang Zhang +8
World Action Models (WAMs) have emerged as a powerful paradigm for embodied intelligence, yet the prevailing reliance on pixel-level video generation creates a fundamental bottlene…
LUNA-AD: Lightweight Uncertainty-Aware Language Model with Lifelong Learning for Autonomous Driving
Ruoyu Yao, Pei Liu, Ruiguo Zhong +3
While large language models (LLMs) offer promising reasoning capabilities, their integration into safety-critical driving systems is hindered by limited reasoning diversity, high c…
Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving
Chengzhen Meng, Pei Liu, Zhiyu Huang +2
Complex, dynamic, and interactive driving environments pose significant challenges for autonomous driving, primarily due to the pervasive uncertainty of surrounding traffic. A fund…
Decision-Making with Lightweight Confidence-Aware Language Model for Autonomous Driving
Ruoyu Yao, Ruiguo Zhong, Pei Liu +3
Large Language Models (LLMs) and Multimodal LLMs (MLLMs) have demonstrated immense potential in autonomous driving (AD) by offering human-like reasoning and open-world generalizati…
Closed Loop Dynamic Driving Data Mixture for Real-Synthetic Co-Training
Hongzhi Ruan, Pei Liu, Weiliang Ma +5
Data scaling is fundamental to modern deep learning, and grows increasingly critical as autonomous driving shifts to end-to-end learning. Real-world driving data is expensive to an…
Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Staining
Yucheng Xing, Pei Liu, Jingying Ma +6
Multiple instance learning (MIL) is the dominant framework for whole-slide image analysis in computational pathology, typically combining a frozen patch encoder, a projection layer…
Xiaomi OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation
Jinghui Lu, Jiayi Guan, Zhijian Huang +47
Chain-of-Thought (CoT) reasoning has become a powerful driver of trajectory prediction in VLA-based autonomous driving, yet its autoregressive nature imposes a latency cost that is…
LiSTAR: Ray-Centric World Models for 4D LiDAR Sequences in Autonomous Driving
Pei Liu, Songtao Wang, Lang Zhang +9
Synthesizing high-fidelity and controllable 4D LiDAR data is crucial for creating scalable simulation environments for autonomous driving. This task is inherently challenging due t…
OmniScene: Attention-Augmented Multimodal 4D Scene Understanding for Autonomous Driving
Pei Liu, Hongliang Lu, Haichao Liu +5
Human vision is capable of transforming two-dimensional observations into an egocentric three-dimensional scene understanding, which underpins the ability to translate complex scen…
CoPlanner: An Interactive Motion Planner with Contingency-Aware Diffusion for Autonomous Driving
Ruiguo Zhong, Ruoyu Yao, Pei Liu +3
Accurate trajectory prediction and motion planning are crucial for autonomous driving systems to navigate safely in complex, interactive environments characterized by multimodal un…
CogDriver: Integrating Cognitive Inertia for Temporally Coherent Planning in Autonomous Driving
Pei Liu, Qingtian Ning, Xinyan Lu +6
The pursuit of autonomous agents capable of temporally coherent planning is hindered by a fundamental flaw in current vision-language models (VLMs): they lack cognitive inertia. Op…
VLM-UDMC: VLM-Enhanced Unified Decision-Making and Motion Control for Urban Autonomous Driving
Haichao Liu, Haoren Guo, Pei Liu +4
Scene understanding and risk-aware attentions are crucial for human drivers to make safe and effective driving decisions. To imitate this cognitive ability in urban autonomous driv…
DSDrive: Distilling Large Language Model for Lightweight End-to-End Autonomous Driving with Unified Reasoning and Planning
Wenru Liu, Pei Liu, Jun Ma
We present DSDrive, a streamlined end-to-end paradigm tailored for integrating the reasoning and planning of autonomous vehicles into a unified framework. DSDrive leverages a compa…