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.
NeuRAD: Neural Rendering for Autonomous Driving
Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community.
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.
Can Deep Learning be Applied to Model-Based Multi-Object Tracking?
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 autonomous driving, tracking animal behavior, defense systems, and others.
Deep Deterministic Path Following
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.
Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning
We show that the proposed model outperforms state-of-the-art Bayesian filters in complex scenarios, while matching their performance in simpler cases, which validates the applicability of deep-learning also in the model-based regime.
