Navigation of autonomous vehicles in highly dynamic urban environments are extremely challenging due to perceptually degraded situations and vehicle instability incidents, impacting decision-making. The project will develop and test a situation-aware learning control strategy, robust to time delays, for remote take-over and assistance for automated driving in complex environments.

Automated driving systems (ADS) are leveraging advances in connectivity and sensing technologies to drive innovation in major markets across the globe. However, navigation of autonomous vehicles in highly dynamic urban environments, with human presence, are extremely challenging due to perceptually degraded situations (e.g., dynamic objects that deteriorate visual odometry, or dark/bright scenes) and vehicle instability incidents, impacting ADS model’s predictive capacity for decision-making. Remote take-over/assistance by a human operator provides a resilient decision-making replacement for controlling the vehicle and safe pullover in such situations. Designed from an interdisciplinary perspective, the project will develop and test a situation-aware learning control strategy, robust to time delays, for remote take-over and assistance for automated driving in complex environments, through the following three integrated objectives:

O1. Situation awareness enhancement through visual-inertial remote sensing
O2. Development of a remote human-centered control framework for shared autonomy
O3. Implementation and experimental studies