Search Results for author:
Found 7 papers, 5 papers with code
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.
TimePillars: Temporally-Recurrent 3D LiDAR Object Detection
Object detection applied to LiDAR point clouds is a relevant task in robotics, and particularly in autonomous driving.
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.
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.
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.
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.
