Enhancing autonomous systems with bayesian neural networks: a probabilistic framework for navigation and decision-making
The rapid evolution of autonomous systems is reshaping urban mobility and accelerating the development of intelligent transportation networks. A key challenge in real-world deployment is the ability to operate reliably under uncertainty–arising from sensor noise, dynamic agents, and adverse weather...
Saved in:
| Main Authors: | Ngartera Lebede, Saralees Nadarajah |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-05-01
|
| Series: | Frontiers in Built Environment |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbuil.2025.1597255/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Designing an Adaptive Velocity Obstacle Avoidance System for Autonomous Mars Rover Navigation in Dynamic Terrains
by: Karim Ahmadi Dastgerdi, et al.
Published: (2024-12-01) -
Application of Bayesian Neural Networks in Healthcare: Three Case Studies
by: Lebede Ngartera, et al.
Published: (2024-11-01) -
ROS-Based Navigation and Obstacle Avoidance: A Study of Architectures, Methods, and Trends
by: Zhe Wei, et al.
Published: (2025-07-01) -
Reinforcement learning for autonomous underwater vehicles (AUVs): navigating challenges in dynamic and energy-constrained environments
by: Mohab M. Eweda, et al.
Published: (2024-12-01) -
UAV Autonomous Navigation Based on Deep Reinforcement Learning in Highly Dynamic and High-Density Environments
by: Yuanyuan Sheng, et al.
Published: (2024-09-01)