9 papers
FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning
Xirui Li, Zhe Liu, Xiaoqing Ye +4
Multimodal driving planning faces a long-standing tension between two paradigms: scoring-based methods benefit from dense reward supervision but are confined to a fixed action voca…
UniLION: Towards Unified Autonomous Driving Model with Linear Group RNNs
Zhe Liu, Jinghua Hou, Xiaoqing Ye +3
Although transformers have demonstrated remarkable capabilities across various domains, their quadratic attention mechanisms introduce significant computational overhead when proce…
SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection
Dingkang Liang, Wei Hua, Chunsheng Shi +3
Semi-supervised object detection (SSOD), leveraging unlabeled data to boost object detectors, has become a hot topic recently. However, existing SSOD approaches mainly focus on hor…
PointMamba: A Simple State Space Model for Point Cloud Analysis
Dingkang Liang, Xin Zhou, Wei Xu +5
Transformers have become one of the foundational architectures in point cloud analysis tasks due to their excellent global modeling ability. However, the attention mechanism has qu…
Make Your ViT-based Multi-view 3D Detectors Faster via Token Compression
Dingyuan Zhang, Dingkang Liang, Zichang Tan +4
Slow inference speed is one of the most crucial concerns for deploying multi-view 3D detectors to tasks with high real-time requirements like autonomous driving. Although many spar…
EasyChauffeur: A Baseline Advancing Simplicity and Efficiency on Waymax
Lingyu Xiao, Jiang-Jiang Liu, Xiaoqing Ye +2
Recent advancements in deep-learning-based driving planners have primarily focused on elaborate network engineering, yielding limited improvements. This paper diverges from convent…