Cloud Control System Architectures, Technologies and Applications on Intelligent and Connected Vehicles: a Review

The electrification of vehicle helps to improve its operation efficiency and safety. Due to fast development of network, sensors, as well as computing technology, it becomes realizable to have vehicles driving autonomously. To achieve autonomous driving, several steps, including environment perception, path-planning, and dynamic control, need to be done. However, vehicles equipped with on-board sensors still have limitations in acquiring necessary environmental data for optimal driving decisions. Intelligent and connected vehicles (ICV) cloud control system (CCS) has been introduced as a new concept as it is a potentially synthetic solution for high level automated driving to improve safety and optimize traffic flow in intelligent transportation. This paper systematically investigated the concept of cloud control system from cloud related applications on ICVs, and cloud control system architecture design, as well as its core technologies development. Based on the analysis, the challenges and suggestions on cloud control system development have been addressed.

A New Approach to Realizing the Soft-and Operation in Cloud Model-Based Control

Considering fuzziness, randomness, and the association between them, cloud model-based control is a new way to address uncertainty in the inference system. Similar to fuzzy control theory, this method includes an important step of dealing with the logic concept “and”, which is defined as the operation of soft-and between several antecedents and has not been scientifically solved in the current literatures. The traditional method of realizing soft-and is to use multi-dimensional cloud model theory, which lacks a theoretical basis. Based on the fuzzy and random theory, this paper proposes a novel approach using numeric simulation to calculate the soft-and in the cloud control system. In this method, the theory to determine the distribution of the minimum value between two random variables is applied. Compared with the traditional method, the considered approach is more reliable and reasonable, and its result is also in accordance with the standard fuzzy inference system.

An Interactive Real-Time SCADA Platform With Customizable Virtual Instruments for Cloud Control Systems

This paper studies the design and implementation of an interactive real-time cloud supervisory control and data acquisition (SCADA) platform. The platform relying on C# and client/server architecture provides full support for data supervision of the cloud control system (CCS). Users are allowed to design supervisory interfaces by dynamically creating and customizing virtual instruments, which are seamlessly integrated into the platform by reconstructing it. Both the scalar and matrix data from different cloud nodes are supported for supervising simultaneously in real-time through receiving data asynchronously. The user can tune the parameters of the CCS online via duplex channels based on the transmission control protocol/internet protocol (IP). To overcome the disturbance of network delays to data display, a stable data and real-time data communication scheme are proposed. All the supervised data can be stored in separate files for further analysis. Finally, the online simulation and experiment are provided to demonstrate the feasibility of the designed SCADA platform.