Description
Date depot: 20 mars 2019
Titre: Probabilistic Behavioral Planning for Self-driving Vehicles
Directeur de thèse:
Pietro MICHIARDI (Eurecom)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Non defini
Resumé:
Autonomous vehicles (AVs) could bring
great benefits to society, from reducing road fatalities and injuries, to
drastically reducing the carbon footprint of transportation systems, to providing
independence to those unable to drive. Further, AVs offer the AI community
manyhigh-impact research problems in diverse fields including: computer vision,
probabilistic modelling, and multi-agent decision making, to name a few.
In the context of this Ph.D. Thesis
project, the focus is on the development of new methodologies to enable
decision components to operate an AV, based on its current understanding of the
surrounding environment, while taking into account the probabilistic – and thus
uncertain – nature of the problem, such that safety considerations and
objectives can be systematically fulfilled. In particular, the focus of this
Thesis is on the topic of probabilistic, hierarchical reinforcement learning,
by addressing the following challenges: 1) integration of the notion of
uncertainty, which we will achieve through the language of probability used in
the context of Bayesian inference applied to the basic reinforcement learning
method; 2) automatic learning of abstract representation of actions, such as to
render the reinforcement learning method more data efficient, the learned
procedures transferable across sub-actions, or in other words, to learn skills
rather than tasks; 3) cast the overall learning problem as a multi-objective
optimisation task, whereby aspects related to efficiency, safety and comfort
are jointly optimised.
This Thesis is supported by Renault Software
Labs, which will provide a useful environment to evaluate the mechanisms
proposed in the project, for example by using proprietary or open-source
driving simulators, and raw data or derived data to build the environment in
which the reinforcement learning agent will operate.
Doctorant.e: Lecerf Ugo