12 papers
LoongSage: A Production-Grade, High-Efficiency Agentic RL Framework for Frontier LLMs with Validated Recipes
LoonSage Team, LoongSage Team, Bowei Song +46
Reinforcement learning (RL) has emerged as a critical phase in the post-training pipeline of large language models (LLMs). As the frontier of LLM capabilities shifts from static re…
Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation
Jingyu Li, Zhe Liu, Dongnan Hu +10
World action models~(WAMs) have shown great promise for autonomous driving and urban navigation. Built upon Vision-Language-Action models or video generation models, existing appro…
Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Driving
Qiqi Liu, Huan Xu, Jingyu Li +5
Autonomous driving requires reasoning about how the environment evolves and planning actions accordingly. Existing world-model-based approaches typically predict future scenes firs…
DriveCombo: Benchmarking Compositional Traffic Rule Reasoning in Autonomous Driving
Enhui Ma, Jiahuan Zhang, Guantian Zheng +10
Multimodal Large Language Models (MLLMs) are rapidly becoming the intelligence brain of end-to-end autonomous driving systems. A key challenge is to assess whether MLLMs can truly…
Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving
Xuanjin Jin, Yanxin Dong, Bin Sun +4
Planning under uncertainty for real-world robotics tasks, such as autonomous driving, requires reasoning in enormous high-dimensional belief spaces, rendering the problem computati…
PlannerRFT: Reinforcing Diffusion Planners through Closed-Loop and Sample-Efficient Fine-Tuning
Hongchen Li, Tianyu Li, Jiazhi Yang +10
Diffusion-based planners have emerged as a promising approach for human-like trajectory generation in autonomous driving. Recent works incorporate reinforcement fine-tuning to enha…
SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving
Jingyu Li, Junjie Wu, Dongnan Hu +6
Recent end-to-end autonomous driving approaches have leveraged Vision-Language Models (VLMs) to enhance planning capabilities in complex driving scenarios. However, VLMs are inhere…
FLARE: Learning Future-Aware Latent Representations from Vision-Language Models for Autonomous Driving
Chengen Xie, Chonghao Sima, Tianyu Li +4
While Vision-Language Models (VLMs) offer rich world knowledge for end-to-end autonomous driving, current approaches heavily rely on labor-intensive language annotations (e.g., VQA…
DriveVGGT: Calibration-Constrained Visual Geometry Transformers for Multi-Camera Autonomous Driving
Xiaosong Jia, Yanhao Liu, Yu Hong +5
Feed-forward reconstruction has been progressed rapidly, with the Visual Geometry Grounded Transformer (VGGT) being a notable baseline. However, directly applying VGGT to autonomou…
SparseWorld-TC: Trajectory-Conditioned Sparse Occupancy World Model
Jiayuan Du, Yiming Zhao, Zhenglong Guo +5
This paper introduces a novel architecture for trajectory-conditioned forecasting of future 3D scene occupancy. In contrast to methods that rely on variational autoencoders (VAEs)…
GaussianAD: Gaussian-Centric End-to-End Autonomous Driving
Wenzhao Zheng, Junjie Wu, Yao Zheng +8
Vision-based autonomous driving shows great potential due to its satisfactory performance and low costs. Most existing methods adopt dense representations (e.g., bird's eye view) o…
S2-Track: A Simple yet Strong Approach for End-to-End 3D Multi-Object Tracking
Tao Tang, Lijun Zhou, Pengkun Hao +9
3D multiple object tracking (MOT) plays a crucial role in autonomous driving perception. Recent end-to-end query-based trackers simultaneously detect and track objects, which have…