14 citations · 36 across the 15 of their papers we have counts for
15 papers
Towards Zero-Shot Transfer Across Embodiments For Driving VLAs
Caio Azevedo, Stefano Sabatini, Sascha Hornauer +1
Vision-Language-Action models (VLAs) have shown strong potential in autonomous driving by leveraging multimodal pretraining for instruction following, visual reasoning, and scene-l…
Future-Interactions-Aware Trajectory Prediction via Braid Theory
Caio Azevedo, Stefano Sabatini, Sascha Hornauer +1
To safely operate, an autonomous vehicle must know the future behavior of a potentially high number of interacting agents around it, a task often posed as multi-agent trajectory pr…
Improving Consistency in Vehicle Trajectory Prediction Through Preference Optimization
Caio Azevedo, Lina Achaji, Stefano Sabatini +4
Trajectory prediction is an essential step in the pipeline of an autonomous vehicle. Inaccurate or inconsistent predictions regarding the movement of agents in its surroundings lea…
AVS-Net: Audio-Visual Scale Net for Self-supervised Monocular Metric Depth Estimation
Xiaohu Liu, Sascha Hornauer, Fabien Moutarde +1
Metric depth prediction from monocular videos suffers from bad generalization between datasets and requires supervised depth data for scale-correct training. Self-supervised traini…
NeRAF: 3D Scene Infused Neural Radiance and Acoustic Fields
Amandine Brunetto, Sascha Hornauer, Fabien Moutarde
Sound plays a major role in human perception. Along with vision, it provides essential information for understanding our surroundings. Despite advances in neural implicit represent…
Mesoscale Traffic Forecasting for Real-Time Bottleneck and Shockwave Prediction
Raphael Chekroun, Han Wang, Jonathan Lee +4
Accurate real-time traffic state forecasting plays a pivotal role in traffic control research. In particular, the CIRCLES consortium project necessitates predictive techniques to m…
MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly
Raphael Chekroun, Thomas Gilles, Marin Toromanoff +2
We present MBAPPE, a novel approach to motion planning for autonomous driving combining tree search with a partially-learned model of the environment. Leveraging the inherent expla…
The Audio-Visual BatVision Dataset for Research on Sight and Sound
Amandine Brunetto, Sascha Hornauer, Stella X. Yu +1
Vision research showed remarkable success in understanding our world, propelled by datasets of images and videos. Sensor data from radar, LiDAR and cameras supports research in rob…
GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving
Raphael Chekroun, Marin Toromanoff, Sascha Hornauer +1
Deep reinforcement learning (DRL) has been demonstrated to be effective for several complex decision-making applications such as autonomous driving and robotics. However, DRL is no…
Unsupervised Discriminative Learning of Sounds for Audio Event Classification
Sascha Hornauer, Ke Li, Stella X. Yu +2
Recent progress in network-based audio event classification has shown the benefit of pre-training models on visual data such as ImageNet. While this process allows knowledge transf…
Unsupervised deep learning for grading of age-related macular degeneration using retinal fundus images
Baladitya Yellapragada, Sascha Hornhauer, Kiersten Snyder +2
Many diseases are classified based on human-defined rubrics that are prone to bias. Supervised neural networks can automate the grading of retinal fundus images, but require labor-…
BatVision with GCC-PHAT Features for Better Sound to Vision Predictions
Jesper Haahr Christensen, Sascha Hornauer, Stella Yu
Inspired by sophisticated echolocation abilities found in nature, we train a generative adversarial network to predict plausible depth maps and grayscale layouts from sound. To ach…
BatVision: Learning to See 3D Spatial Layout with Two Ears
Jesper Haahr Christensen, Sascha Hornauer, Stella Yu
Many species have evolved advanced non-visual perception while artificial systems fall behind. Radar and ultrasound complement camera-based vision but they are often too costly and…
Fast Recurrent Fully Convolutional Networks for Direct Perception in Autonomous Driving
Yiqi Hou, Sascha Hornauer, Karl Zipser
Deep convolutional neural networks (CNNs) have been shown to perform extremely well at a variety of tasks including subtasks of autonomous driving such as image segmentation and ob…
Learning to Roam Free from Small-Space Autonomous Driving with A Path Planner
Sascha Hornauer, Karl Zipser, Stella X. Yu
Modern autonomous driving algorithms often rely on learning the mapping from visual inputs to steering actions from human driving data in a variety of scenarios and visual scenes.…