Sbof: An end-to-end framework for simulative black-box optimization of hybrid perceptive functions
The adaptation of perceptive functions to new tasks or new domains is currently a major
challenge in the field of autonomous driving. Since the creation of manually labeled datasets …
challenge in the field of autonomous driving. Since the creation of manually labeled datasets …
CornerSim: A Virtualization Framework to Generate Realistic Corner-Case Scenarios for Autonomous Driving Perception Testing
Autonomous driving development requires rigorous testing in real-world scenarios,
including adverse weather, unpredictable events, object variations, and sensor limitations …
including adverse weather, unpredictable events, object variations, and sensor limitations …
End-to-end autonomous driving perception with sequential latent representation learning
Current autonomous driving systems are composed of a perception system and a decision
system. Both of them are divided into multiple subsystems built up with lots of human …
system. Both of them are divided into multiple subsystems built up with lots of human …
Global optimization for noisy expensive black-box multi-modal functions via radial basis function surrogate
Y Shen, CA Shoemaker - 2020 Winter Simulation Conference …, 2020 - ieeexplore.ieee.org
This study proposes a new surrogate global optimization algorithm that solves problems with
expensive black-box multi-modal objective functions subject to homogeneous evaluation …
expensive black-box multi-modal objective functions subject to homogeneous evaluation …
Exploring dimensionality reduction techniques for efficient surrogate-assisted optimization
Constructing surrogate models of high dimensional optimization problems is challenging
due to the computational complexity involved. This paper empirically investigates the …
due to the computational complexity involved. This paper empirically investigates the …
PyBADS: Fast and robust black-box optimization in Python
PyBADS is a Python implementation of the Bayesian Adaptive Direct Search (BADS)
algorithm for fast and robust black-box optimization (Acerbi and Ma 2017). BADS is an …
algorithm for fast and robust black-box optimization (Acerbi and Ma 2017). BADS is an …
Efficient bayesian optimization with deep kernel learning and transformer pre-trained on multiple heterogeneous datasets
W Lyu, S Hu, J Chuai, Z Chen - arXiv preprint arXiv:2308.04660, 2023 - arxiv.org
Bayesian optimization (BO) is widely adopted in black-box optimization problems and it
relies on a surrogate model to approximate the black-box response function. With the …
relies on a surrogate model to approximate the black-box response function. With the …
Multi-Fidelity Bayesian Optimization With Across-Task Transferable Max-Value Entropy Search
In many applications, ranging from logistics to engineering, a designer is faced with a
sequence of optimization tasks for which the objectives are in the form of black-box functions …
sequence of optimization tasks for which the objectives are in the form of black-box functions …
Towards Scenario-and Capability-Driven Dataset Development and Evaluation: An Approach in the Context of Mapless Automated Driving
The foundational role of datasets in defining the capabilities of deep learning models has
led to their rapid proliferation. At the same time, published research focusing on the process …
led to their rapid proliferation. At the same time, published research focusing on the process …
Evaluating validity of synthetic data in perception tasks for autonomous vehicles
D Talwar, S Guruswamy, N Ravipati… - 2020 IEEE …, 2020 - ieeexplore.ieee.org
Autonomous vehicles have the potential to completely upend the way we transport today,
however deploying them safely at scale is not an easy task. Any autonomous driving system …
however deploying them safely at scale is not an easy task. Any autonomous driving system …