Operator interface and situation perception in hierarchical intelligent control: a case study

Abstract The rapid development of intelligent control technology has also brought about changes in the automotive industry and led to development of autonomous or self-driving vehicles. To overcome traffic and environment issues, self-driving cars use a number of sensors for vision as well as a navigation system and actuators to control mechanical systems and computers to process the data. All these points make a self-driving car an interdisciplinary project that requires contribution from different fields. In our particular case, four different university departments and two companies are directly involved in the self-driving car project. The main aim of the paper is to discuss the challenges faced in the development of the first Estonian self-driving car. The project implementation time was 20 months and the project included four work packages: preliminary study, software development, body assembly and system tuning/testing of the self-driving car. This paper describes the development process stages and tasks that were distributed between the sub-teams. Moreover, the paper presents the technical and software solutions that were used to achieve the goal and presents a self-driving last mile bus called ISEAUTO. Special attention is paid to the discussion of safety challenges that a self-driving electrical car project can encounter. The main outcomes and future research possibilities are outlined

Download Full-text

An intelligent control policy for fuel injection control of SI Engines (case study: CNG engine)

2011 15th IEEE International Conference on Intelligent Engineering Systems ◽

10.1109/ines.2011.5954730 ◽

2011 ◽

Cited By ~ 3

Author(s):

F. Barghi ◽

A. A. Safavi

Keyword(s):

Intelligent Control ◽

Fuel Injection ◽

Control Policy ◽

Cng Engine ◽

Si Engines ◽

Injection Control

Download Full-text

Random Forests-Based Operational Status Perception Model in Extra-Long Highway Tunnels with Longitudinal Ventilation: A Case Study in China

Journal of Advanced Transportation ◽

10.1155/2018/5056284 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Chao Qian ◽

Jianxun Chen ◽

Yanbin Luo ◽

Shuguang Li

Keyword(s):

Intelligent Control ◽

Random Forests ◽

Control Strategies ◽

Present Moment ◽

Longitudinal Ventilation ◽

Highway Tunnel ◽

Proposed Model ◽

Perception Model ◽

Traffic Operation

An increasing number of extra-long highway tunnels have been built and put into operation around the world, but the quantified segmentation criteria for evaluating the in-tunnel operational status have not yet been enacted up till the present moment. Meanwhile, ventilation facilities could not satisfy the dynamic requirements of fresh air demand under fast spatial-temporal variation of traffic conditions and operating environment. In this study, the operational data collected from an extra-long highway tunnel were deeply analyzed using big data technology. By combining traffic flow and environmental monitoring data, a data-driven perception model based on the Random Forests was structured. The prediction results show that the proposed model provides better performances as compared to contrast models, indicating it had better ability to adapt to the dynamic changes of in-tunnel operational status while realizing accurate prediction. The designed intelligent control strategies of ventilation facilities and traffic operation applying for different operational status would provide a theoretical basis and data support for lifting the level of intelligent control as well as promoting energy saving and consumption reducing in extra-long highway tunnels.

Download Full-text