IRIT - CLLE, Toulouse University


Autonomous vehicles, self-adaptive multi-agent systems, driving control recovery.

Connected autonomous vehicles of level 3, called "conditioned automation", are vehicles in which the human driver delegates driving control in specific situations. During these situations, it may be necessary for the human to regain control of the driving activity. The main objective of this thesis is to develop a supervision system adapted to each driver, by integrating human factors, to allow a safe and efficient transition of two-way control between the human and the autonomous vehicle. For this, the system must identify, by self-observation and in real-time, situations in which the current driver will no longer be able to ensure driving. He must also provide a context for assessing the criticality of the situation as quickly as possible to anticipate and react to it as best as possible. The driving context is composed of indicators that characterize the elements that describe part of the driving process: human, vehicle, and environment. The system is based on self-adaptive multi-agentlearning systems.

Figure 1: Dynamic Learning using self-adaptive multi-agent system

Scientific goals

- Dynamic learning using multi-agent systems

- Generic approach to supervise the activity of a system

- Study the impact of the factors describing the different elements present in the system context on the ability of the system to converge towards a solution.

- Insure the acceptability of the system by human driver