Intelligent control for trustworthy autonomous vehicles

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"Autonomous vehicle control presents a significant challenge for artificial intelligence and control theory. To safely control the autonomous vehicle systems, the control system needs to understand the behavior of system dynamics under the effects of model uncertainties and environmental disturbances. This can be achieved by embedding a control scheme that integrates: (1) the design of model-based controllers to ensure the safety of the system, (2) the design of online learning technique for estimating the unknown system dynamics, and (3) the design of model free-learning technique such as reinforcement learning to estimate the unknown environment. The integration between the model-based control with online system dynamics approximation and environment learning will enable the autonomous vehicle to highly have confidence on safety during driving. Therefore, the aim of the project is to design a controller architecture that combines: (1) a model-based controllers utilizing the safety critical control concepts, (2) an online learning of the unknown system dynamics, and (3) a model-free reinforcement learning technique, which estimates the external environment, to ensure safety during learning and exploration.The objectives of the project include:1. To design a model-based safety critical control that guarantees the autonomous vehicles will always operate within the safety zone.2. To develop dynamic identification technique, for example Gaussian Processes (GPs), to model the system dynamics.3. To develop learning algorithms based on (deep)-reinforcement learning to understand the surrounding environment, i.e, human, obstacles.4. To implement and validate the proposed algorithms in computer simulation platforms: Gazebo platform or Matlab/Simulink.5. To implement the developed algorithms in a UAV platform, i.e., HuskyA200 AGV robot, PAWs robot."