Connected Automated Vehicles

Dear Colleagues,

The purpose of research and development of functions and components of connected and automated vehicles is to enhance various aspects of mobility. The most important tasks are to guarantee efficient, comfortable, safe, and economical transport by exploiting signals from sensors and communications. Since recent developments and commercialized products are at still relatively low levels concerning SAE automation requirements, there is a wide range of further research and development possibilities. At the same time, technical and technological tools, methods, and solutions are improving continuously and providing greater possibilities for development.

The development of connected and autonomous driving can be classified into various disciplines. In environment perception and situation evaluation communication technologies, sensor fusion, mapping, and localization and evaluation methods provide data for the decision-maker layer. At this layer, interconnection provides additional signals. Based on the monitored situation, decisions must be made about vehicle maneuvers, such as route planning, trajectory design, speed selection, obstacle avoidance, overtaking, etc. The most important evaluation methods include Bayesian inference, game theory, optimization approaches, and areas of machine learning and deep learning.

Cooperative control of autonomous vehicles is aimed at guaranteeing a large number of performance requirements in traffic. Recently, a great deal of emphasis has been placed on state-of-the-art control design methods based on machine learning tasks and efficient optimization procedures. The purpose is to combine classical control design methods and various learning structures in order to provide less complex and computationally-intensive solutions. Guaranteeing stability and performances for complex management architectures is also a considerable challenge. These architectures require the development of new types of testing and validation procedures, as well as prototype constructs for testing purposes. Moreover, alternative procedures such as simulations, HIL/SIL tests, and virtual reality are also in the focus of research.

Potential topics include but are not limited to the following:

• Environment perception and situation evaluation;
• On-board sensor signals, signals from V2X communications, sensor fusion;
• Performances of cooperative control systems;
• Hierarchical and heterogeneous cooperative structures;
• Machine learning, reinforcement learning;
• Cooperative control, robust control;
• Mixed traffic of autonomous and human-driven vehicles;
• Testing and validation of connected vehicles;
• Development frameworks, prototype constructions;
• Potential impact of connected vehicles on traffic control.

Prof. Dr. Peter Gaspar
Guest Editor

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• sensor fusion
• V2X communication
• situation evaluation
• cooperative control
• autonomous control
• machine learning
• reinforcement learning
• validation
• prototype