5 papers
Shift & Drift: A Zero-Shot Benchmark for Generalizable and Robust Autonomous Driving Motion Planning
Alessandro Canevaro, Hang Yu, Julian Schmidt +5
While closed-loop motion planners trained on large-scale, object-level datasets, e.g., nuPlan, demonstrate strong in-distribution (ID) performance, their generalization to novel ur…
G2DP: Diffusion Planning with Spatio-Temporal Grid Guidance
Hang Yu, Ye Jin, Alessandro Canevaro +7
In autonomous driving, diffusion-based planners have emerged as a promising paradigm for robust motion planning in dense and interactive traffic, as they can effectively model dive…
AGO: Adaptive Grounding for Open World 3D Occupancy Prediction
Peizheng Li, Shuxiao Ding, You Zhou +6
Open-world 3D semantic occupancy prediction aims to generate a voxelized 3D representation from sensor inputs while recognizing both known and unknown objects. Transferring open-vo…
TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking
Shuxiao Ding, Yutong Yang, Julian Wiederer +4
Query denoising has become a standard training strategy for DETR-based detectors by addressing the slow convergence issue. Besides that, query denoising can be used to increase the…
SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
Qingwen Zhang, Yi Yang, Peizheng Li +2
Scene flow estimation predicts the 3D motion at each point in successive LiDAR scans. This detailed, point-level, information can help autonomous vehicles to accurately predict and…