Abstract:

Every year more than 20 million people are involved in road accidents, mostly caused by human errors. According to the World Health Organization, approximately 1.3 million people lost their lives in these accidents.  Half of the victims are vulnerable road users (VRUs), such as pedestrians and cyclists.  Self-driving vehicles can help reduce these fatalities.  This talk presents a VRUs-aware local motion planner based on model predictive control (MPC).  Our planner strongly relies on the interaction with the environmental perception for navigation in complex urban environments.  The perception module detects and estimates the paths of the VRUs over a prediction horizon, while the planning module exploits these paths to plan collision-free trajectories. Real-life experiments shows the potential of our design for the future of urban driving. 

Bio:

Laura Ferranti received her Ph.D. from Delft University of Technology (TU Delft), Delft, The Netherlands, in 2017.  She is currently a postdoctoral researcher in TU Delft. Her research interests include: optimization and optimal control, model predictive control, embedded optimization-based control with application in flight control, automotive, maritime transportation, and robotics.  The main techniques involved in her research are proximal gradient methods (deterministic and stochastic versions) and splitting methods (such as, the alternating minimization algorithm and the alternating direction method of multipliers) for convex and nonconvex (nonlinear) optimization.