Seminar 1: BrIAS Fellow Prof. Paolo Falcone
Cautious-by-design motion planning. The role of prediction
Abstract: Safety of passengers and surrounding road users is the most important challenge in the design and deployment of autonomous driving technologies. In fact, the highest Automotive Safety Integrity Level (ASIL-D) will likely be required for autonomous driving functionalities. While fulfilling such safety requirements involves special design efforts at all levels of the autonomous driving stack, in this talk we will focus on the control design of a safe motion planner in urban environments.
We will start by illustrating a model-based control design approach to vehicle motion
planning problem, in the presence of human road users (pedestrians, cyclists, human-driven vehicles). We will show that, under mild assumptions, vehicle behavior can be made cautious
in the presence of road users and guaranteed to be persistently safe. Experimental results obtained with a passenger vehicle negotiating an intersection with a simulated pedestrian, will
be shown. An important ingredient of the proposed motion planning framework is a prediction model of the surrounding traffic. In the second part of the seminar, we will illustrate our ongoing research on humans' intent prediction in traffic environments. We will show how the evolution of a traffic scene can be predicted using very simple models and motion data (position, velocity) of road users observed in similar traffic scenes.
Seminar 2: BrIAS Fellow Prof. Hideyuki Sawada
Robotics and machine learning techniques for understanding, controlling and predicting physical phenomena
Abstract: Machine learning techniques are widely applied to the variety of fields to provide human-like intelligence for flexibly understanding, controlling and predicting daily issues. In this lecture, the history of the development of AIs is firstly introduced from the viewpoint of computational capacity and human ability, then the basic idea for machine learning will be presented, together with the robotic technologies. Several applications that have been conducted in our research team will be introduced as examples of the effective use of machine learning and the robotics technologies.
