8 papers
DLWM: Dual Latent World Models enable Holistic Gaussian-centric Pre-training in Autonomous Driving
Yiyao Zhu, Ying Xue, Haiming Zhang +8
Vision-based autonomous driving has gained much attention due to its low costs and excellent performance. Compared with dense BEV (Bird's Eye View) or sparse query models, Gaussian…
WPT: World-to-Policy Transfer via Online World Model Distillation
Guangfeng Jiang, Yueru Luo, Jun Liu +6
Recent years have witnessed remarkable progress in world models, which primarily aim to capture the spatio-temporal correlations between an agent's actions and the evolving environ…
MSSF: A 4D Radar and Camera Fusion Framework With Multi-Stage Sampling for 3D Object Detection in Autonomous Driving
Hongsi Liu, Jun Liu, Guangfeng Jiang +1
As one of the automotive sensors that have emerged in recent years, 4D millimeter-wave radar has a higher resolution than conventional 3D radar and provides precise elevation measu…
DSBench: A Comprehensive Benchmark for Evaluating External and In-Cabin Risks
Xianhui Meng, Yuchen Zhang, Zhijian Huang +12
Vision-Language Models (VLMs) show great promise for autonomous driving, but their suitability for safety-critical scenarios is largely unexplored, raising safety concerns. This is…
ALISE: Annotation-Free LiDAR Instance Segmentation for Autonomous Driving
Yongxuan Lyu, Guangfeng Jiang, Hongsi Liu +1
The manual annotation of outdoor LiDAR point clouds for instance segmentation is extremely costly and time-consuming. Current methods attempt to reduce this burden but still rely o…
MLF-4DRCNet: Multi-Level Fusion with 4D Radar and Camera for 3D Object Detection in Autonomous Driving
Yuzhi Wu, Li Xiao, Jun Liu +2
The emerging 4D millimeter-wave radar, measuring the range, azimuth, elevation, and Doppler velocity of objects, is recognized for its cost-effectiveness and robustness in autonomo…