Project Details
Projekt
Learning Cooperative Trajectories in Mixed Traffic
Subject Area
Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
Term
from 2015 to 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 273350361
The traffic of the future is likely to be significantly different from today. Currently, there is widespread global effort to autamate traffic by using self-driving vehicles. But as there will be vehicles for a long time, who can not drive independently and at the same time there will be drivers who like to drive their vehicle themselves, at least for a significant transitional period we will have mixed traffic consisting of self-driving and man-driven vehicles. Most of the current research on self-driving vehicles is focused, however, on the autonomy of each individual vehicle. In the project proposed here, which fits seamlessly into the priority program "Cooperative Interacting Automobiles", we investigate how to efficiently plan and control the trajectories of a number of vehicles in a mixed traffic scenario.The aim of the proposed project is thus to explore learning methods for provably safe maneuver and trajectory collectives in mixed traffic. From a theoretical point of view optimal scheduling for multiple agents constitutes a challenge for real-time applications on a vehicle that we approach in this project. For this purpose, current learning methods shall be applied, such as Reinforcement Learning, which is based on Markov Decision Process models, and deep Q-networks using a common holistic quality function.The developed methods shall be integrated, demonstrated and validated in the simulation environment of the priority program and in real experimental vehicles.
DFG Programme
Priority Programmes
Subproject of
SPP 1835:
Kooperativ interagierende Automobile
