Model-based deadlock prevention for traffic planning of autonomous vehicles
Ladda ner
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Data science and AI (MPDSC), MSc
Computer science – algorithms, languages and logic (MPALG), MSc
Computer science – algorithms, languages and logic (MPALG), MSc
Publicerad
2023
Författare
Möller, David
Ohlin, Alexander
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Volvo Autonomous Solutions are developing a system for planning the routes of
fleets of autonomous vehicles. Autonomous control creates several problems that
must be solved; among these is the possibility for the policy of said vehicles to end
up in deadlock. This thesis proposes new concepts to describe the problem and
methods for preventing a vehicle fleet from deadlocking. As the action that led to
deadlock might not be recent, the term implicit deadlock was introduced, which is
a configuration of vehicle positions from which deadlock is inevitable. The methods
developed successfully prevent deadlocks at several pilot and test sites. However, results
indicate that time for computing implicit deadlocks grows exponentially in the
size of the site and the number of vehicles in the fleet. A neural network model was
also trained using data generated from preprocessing of deadlocks to approximate
the process and enable deadlock predictions not discovered before.
Beskrivning
Ämne/nyckelord
Computer science , neural networks , graph theory , deadlock , autonomous vehicles