7 papers · 1 filter
Is Energy Guidance All You Need? Training-Free Norm Injection for Driving World Models
Xiyan Su, Frank Diermeyer, Markus Lienkamp
Driving world models built on large video-diffusion backbones generate realistic scenes but are hard to control: enforcing a traffic norm typically means retraining the backbone or…
Calibrating the Full Predictive Class Distribution of 3D Object Detectors for Autonomous Driving
Cornelius Schröder, Marius-Raphael Schlüter, Markus Lienkamp
In autonomous systems, precise object detection and uncertainty estimation are critical for self-aware and safe operation. This work addresses confidence calibration for the classi…
To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Software
Loïc Stratil, Felix Fent, Esteban Rivera +1
Autonomous vehicle perception typically relies on modular pipelines that decompose the task into detection, tracking, and prediction. While interpretable, these pipelines suffer fr…
VESPA: Towards un(Human)supervised Open-World Pointcloud Labeling for Autonomous Driving
Levente Tempfli, Esteban Rivera, Markus Lienkamp
Data collection for autonomous driving is rapidly accelerating, but manual annotation, especially for 3D labels, remains a major bottleneck due to its high cost and labor intensity…
HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection
Esteban Rivera, Surya Prabhakaran, Markus Lienkamp
Active Learning has proved to be a relevant approach to perform sample selection for training models for Autonomous Driving. Particularly, previous works on active learning for 3D…
Inconsistency-based Active Learning for LiDAR Object Detection
Esteban Rivera, Loic Stratil, Markus Lienkamp
Deep learning models for object detection in autonomous driving have recently achieved impressive performance gains and are already being deployed in vehicles worldwide. However, c…