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Found 18 papers, 13 papers with code
SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving
Ensuring the safety of autonomous robots, such as self-driving vehicles, requires extensive testing across diverse driving scenarios.
NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving
We present a versatile NeRF-based simulator for testing autonomous driving (AD) software systems, designed with a focus on sensor-realistic closed-loop evaluation and the creation of safety-critical scenarios.
Are NeRFs ready for autonomous driving? Towards closing the real-to-simulation gap
Specifically, we evaluate object detectors and an online mapping model on real and simulated data, and study the effects of different fine-tuning strategies. Our results show notable improvements in model robustness to simulated data, even improving real-world performance in some cases.
NeuRAD: Neural Rendering for Autonomous Driving
Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community.
You can have your ensemble and run it too -- Deep Ensembles Spread Over Time
We find that DESOT obtains the benefits of deep ensembles, in terms of predictive and uncertainty estimation performance, while avoiding the added computational cost.
HEAL-SWIN: A Vision Transformer On The Sphere
High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving.
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving
The dataset is composed of Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatio-temporal learning, sensor fusion, localization, and mapping.
Raw or Cooked? Object Detection on RAW Images
Images fed to a deep neural network have in general undergone several handcrafted image signal processing (ISP) operations, all of which have been optimized to produce visually pleasing images.
LidarCLIP or: How I Learned to Talk to Point Clouds
We also explore zero-shot classification and show that LidarCLIP outperforms existing attempts to use CLIP for point clouds by a large margin.
Towards Explainable Motion Prediction using Heterogeneous Graph Representations
In this work, we aim to improve the explainability of motion prediction systems by using different approaches.
Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs
First, we comprehensively analyse the evaluation metrics, identify the main gaps of current benchmarks, and propose a new holistic evaluation framework.
Masked Autoencoder for Self-Supervised Pre-training on Lidar Point Clouds
Masked autoencoding has become a successful pretraining paradigm for Transformer models for text, images, and, recently, point clouds.
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml
In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving.
Future Object Detection with Spatiotemporal Transformers
In order to make accurate predictions about the future, it is necessary to capture the dynamics in the scene, both object motion and the movement of the ego-camera.
Object Detection as Probabilistic Set Prediction
Accurate uncertainty estimates are essential for deploying deep object detectors in safety-critical systems.
Equivariance versus Augmentation for Spherical Images
We compare the performance of the group equivariant networks known as S2CNNs and standard non-equivariant CNNs trained with an increasing amount of data augmentation.
Geometric Deep Learning and Equivariant Neural Networks
We also discuss group equivariant neural networks for homogeneous spaces $\mathcal{M}=G/K$, which are instead equivariant with respect to the global symmetry $G$ on $\mathcal{M}$.
Fast convolutional neural networks on FPGAs with hls4ml
We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs.
