10 papers
CrowdOcc: Monocular Semantic Scene Completion for Quadruped Robots in Crowded Indoor Environments
Feiyang Chen, Jincheng Hu, Yiduo Chen +5
Monocular semantic scene completion (SSC) for quadruped robots remains underexplored in real crowded indoor environments, where human-scene occlusion disrupts static geometry and h…
VehDyn: A Driving World Model Benchmark for Vehicle Dynamics
Tianyi Wang, Wangsheng Du, Jiazhou Chen +12
Video world models are emerging as data engines, action planners, and generative simulators for autonomous driving, but existing benchmarks primarily assess visual fidelity and coa…
SpatioLM: Towards General Physical Spatial Intelligence in Vision-Language Models
Jing Wu, Jianhua Wu, Jiayi Guan +5
Vision-Language Models (VLMs) perform well on commonsense reasoning tasks but struggle with visual spatial reasoning. Most existing solutions introduce extra 3D prior inputs or ext…
ScenePilot-4K: A Large-Scale First-Person Dataset and Benchmark for Vision-Language Models in Autonomous Driving
Yujin Wang, Yutong Zheng, Wenxian Fan +7
In this paper, we introduce ScenePilot-4K, a large-scale first-person dataset for safety-aware vision-language learning and evaluation in autonomous driving. Built from public onli…
Relative Position Matters: Trajectory Prediction and Planning with Polar Representation
Bozhou Zhang, Nan Song, Bingzhao Gao +1
Trajectory prediction and planning in autonomous driving are highly challenging due to the complexity of predicting surrounding agents' movements and planning the ego agent's actio…
BIDA: A Bi-level Interaction Decision-making Algorithm for Autonomous Vehicles in Dynamic Traffic Scenarios
Liyang Yu, Tianyi Wang, Junfeng Jiao +3
In complex real-world traffic environments, autonomous vehicles (AVs) need to interact with other traffic participants while making real-time and safety-critical decisions accordin…
RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving
Yujin Wang, Quanfeng Liu, Zhengxin Jiang +5
Accurately understanding and deciding high-level meta-actions is essential for ensuring reliable and safe autonomous driving systems. While vision-language models (VLMs) have shown…
MLLM-SUL: Multimodal Large Language Model for Semantic Scene Understanding and Localization in Traffic Scenarios
Jiaqi Fan, Jianhua Wu, Jincheng Gao +4
Multimodal large language models (MLLMs) have shown satisfactory effects in many autonomous driving tasks. In this paper, MLLMs are utilized to solve joint semantic scene understan…
Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios
Jiaqi Fan, Jianhua Wu, Hongqing Chu +2
Large vision-language models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding and generation tasks. However, these models occasionally generate halluci…
RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models
Yujin Wang, Quanfeng Liu, Jiaqi Fan +5
Understanding and addressing corner cases is essential for ensuring the safety and reliability of autonomous driving systems. Vision-language models (VLMs) play a crucial role in e…