Multilevel Coordination Mechanisms for Real-Time Autonomous Agents

Software architecture for real-time autonomous agents: a case study for digital train system

Proceedings of the IEEE Internatinal Symposium on Intelligent Control ◽

10.1109/isic.2002.1157797 ◽

2003 ◽

Cited By ~ 4

Author(s):

Insop Song ◽

F. Karray

Keyword(s):

Software Architecture ◽

Real Time ◽

Autonomous Agents ◽

Time Autonomous ◽

Train System

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A robust variance information fusion technique for real-time autonomous navigation systems

Measurement ◽

10.1016/j.measurement.2021.109441 ◽

2021 ◽

Vol 179 ◽

pp. 109441

Author(s):

Salem Alkhalaf

Keyword(s):

Real Time ◽

Information Fusion ◽

Autonomous Navigation ◽

Navigation Systems ◽

Fusion Technique ◽

Robust Variance ◽

Information Fusion Technique ◽

Time Autonomous

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A taxonomy of coordination mechanisms used by real-time processes

ACM SIGAda Ada Letters ◽

10.1145/249100.249109 ◽

1997 ◽

Vol XVII (2) ◽

pp. 29-54 ◽

Cited By ~ 6

Author(s):

José L. Fernandez

Keyword(s):

Real Time ◽

Coordination Mechanisms

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The design of an intelligent real‐time autonomous vehicle, TAIWANiTS‐1

Journal of the Chinese Institute of Engineers ◽

10.1080/02533839.2007.9671309 ◽

2007 ◽

Vol 30 (5) ◽

pp. 829-842 ◽

Cited By ~ 5

Author(s):

Bing‐Fei Wu ◽

Chao‐Jung Chen ◽

Hsin‐Han Chiang ◽

Hsin‐Yuan Peng ◽

Jau‐Woei Perng ◽

...

Keyword(s):

Real Time ◽

Autonomous Vehicle ◽

Time Autonomous

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Fast Drivable Areas Estimation with Multi-Task Learning for Real-Time Autonomous Driving Assistant

Applied Sciences ◽

10.3390/app112210713 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10713

Author(s):

Dong-Gyu Lee

Keyword(s):

Real Time ◽

Computational Efficiency ◽

Autonomous Driving ◽

Learning Approaches ◽

Practical Applications ◽

Task Learning ◽

Lane Line ◽

High Level ◽

Time Autonomous

Autonomous driving is a safety-critical application that requires a high-level understanding of computer vision with real-time inference. In this study, we focus on the computational efficiency of an important factor by improving the running time and performing multiple tasks simultaneously for practical applications. We propose a fast and accurate multi-task learning-based architecture for joint segmentation of drivable area, lane line, and classification of the scene. An encoder-decoder architecture efficiently handles input frames through shared representation. A comprehensive understanding of the driving environment is improved by generalization and regularization from different tasks. The proposed method learns end-to-end through multi-task learning on a very challenging Berkeley Deep Drive dataset and shows its robustness for three tasks in autonomous driving. Experimental results show that the proposed method outperforms other multi-task learning approaches in both speed and accuracy. The computational efficiency of the method was over 93.81 fps at inference, enabling execution in real-time.

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