51 citations · 80 across the 12 of their papers we have counts for
12 papers
Future-Oriented Navigation: Dynamic Obstacle Avoidance with One-Shot Energy-Based Multimodal Motion Prediction
Ze Zhang, Georg Hess, Junjie Hu +3
This paper proposes an integrated approach for the safe and efficient control of mobile robots in dynamic and uncertain environments. The approach consists of two key steps: one-sh…
SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving
Georg Hess, Carl Lindström, Maryam Fatemi +2
Ensuring the safety of autonomous robots, such as self-driving vehicles, requires extensive testing across diverse driving scenarios. Simulation is a key ingredient for conducting…
Are NeRFs ready for autonomous driving? Towards closing the real-to-simulation gap
Carl Lindström, Georg Hess, Adam Lilja +4
Neural Radiance Fields (NeRFs) have emerged as promising tools for advancing autonomous driving (AD) research, offering scalable closed-loop simulation and data augmentation capabi…
Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing
Juliano Pinto, Georg Hess, Yuxuan Xia +2
Multi-object tracking (MOT) is the task of estimating the state trajectories of an unknown and time-varying number of objects over a certain time window. Several algorithms have be…
NeuRAD: Neural Rendering for Autonomous Driving
Adam Tonderski, Carl Lindström, Georg Hess +3
Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent methods show NeRFs' potential for closed-loop simulation, enabling testing of…
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving
Mina Alibeigi, William Ljungbergh, Adam Tonderski +7
Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360° perception and temporal reasoning. To address this gap, we…
LidarCLIP or: How I Learned to Talk to Point Clouds
Georg Hess, Adam Tonderski, Christoffer Petersson +2
Research connecting text and images has recently seen several breakthroughs, with models like CLIP, DALL-E 2, and Stable Diffusion. However, the connection between text and other v…
Masked Autoencoder for Self-Supervised Pre-training on Lidar Point Clouds
Georg Hess, Johan Jaxing, Elias Svensson +3
Masked autoencoding has become a successful pretraining paradigm for Transformer models for text, images, and, recently, point clouds. Raw automotive datasets are suitable candidat…
Object Detection as Probabilistic Set Prediction
Georg Hess, Christoffer Petersson, Lennart Svensson
Accurate uncertainty estimates are essential for deploying deep object detectors in safety-critical systems. The development and evaluation of probabilistic object detectors have b…
Can Deep Learning be Applied to Model-Based Multi-Object Tracking?
Juliano Pinto, Georg Hess, William Ljungbergh +3
Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications such as auto…
Deep Deterministic Path Following
Georg Hess, William Ljungbergh
This paper deploys the Deep Deterministic Policy Gradient (DDPG) algorithm for longitudinal and lateral control of a simulated car to solve a path following task. The DDPG agent wa…
Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning
Juliano Pinto, Georg Hess, William Ljungbergh +3
Multitarget Tracking (MTT) is the problem of tracking the states of an unknown number of objects using noisy measurements, with important applications to autonomous driving, survei…