Dear Colleagues,

Today, recent technological breakthroughs in actuation, perception and artificial intelligence herald a new era for driver assistance and highly automated vehicles. These technologies bring the promise of improving safety, in particular, by reducing human error, which is the main cause of road accidents. However, experience has shown that these systems raise new issues in terms of safety and driver acceptability. Indeed, the increasing complexity of these in-vehicle technologies changes the nature of the driving task and profoundly modifies the driver's activity, often in ways that were unforeseen and unexpected by the designer. In addition to supervising the driving task, the driver is given the additional task of supervising the activity of the artificial agent. Therefore, the design of the interaction with the driver requires special attention in order to guarantee good cooperation and to ensure harmonization of the two agents' actions that now share the vehicle driving.

From a human–machine cooperation point-of-view, the goal is to end with a supervisor (control authority allocation) that will provide a user-friendly assistance. We claim that the deeper the interconnections between AI-learning and robust control are, the more genericity and performances will be gained, in terms of user-transparency and smooth driver-assistance interactions.

The objective of this research topic is to gather results on novel modeling and adaptive control design approaches for hierarchical human–machine cooperation in automated driving. A particular emphasis is placed on the integration of self-learning capabilities in a robust control framework to build generic strategies while ensuring robustness and safety. This research topic seeks to foster progressive learning and model-free control approaches to improve driver–vehicle cooperation and to design a driving system able to anticipate and adapt its behavior, in order to define safe trajectories and decisions that better suits the driver’s intention.

Dr. Chouki Sentouh
Dr. Jagat Jyoti Rath
Dr. Ye-Chen Qin
Prof. Dr. Baslamisli S. Caglar
Guest Editors

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• human-machine cooperation
• adaptive shared control
• human behavior modeling
• adaptive cooperative control design
• learning techniques in human-machine systems
• model-free control