Agent Behavior Modeling in Roundabout Traffic
Name
Heidi Korp
Abstract
Autonomous vehicle (AV) industry has grown immensely in the last few years. Different aspects of assisted and autonomous driving, including perception, state estimation, motion planning etc. have received a lot of attention from the research and industrial community. Achievements in hardware industry have enabled to make real-time analysis about the situation in traffic. One of the major challenges that the AV industry faces today is understanding and predicting the behavior and future states of road users. Modeling such behaviors is not a trivial task and depends on multiple factors including traffic rules, the geometrical shape of the road, number of traffic participants etc.
In this paper we propose two methods for predicting the future action of a vehicle that is about to enter the roundabout. The first method is based on the Recurrent Neural Network (RNN) architecture and aims to predict the destination of a vehicle. The second method uses the information about Surrounding Vehicles (SV) in addition to the Target Vehicle’s (TV) data to predict the course of action in terms of velocity. The results indicate that a correct assumption about the vehicle’s destination can be achieved in less than 0.4 seconds and that taking the SVs’ data into consideration is very helpful in modeling the vehicle’s future behavior.
In this paper we propose two methods for predicting the future action of a vehicle that is about to enter the roundabout. The first method is based on the Recurrent Neural Network (RNN) architecture and aims to predict the destination of a vehicle. The second method uses the information about Surrounding Vehicles (SV) in addition to the Target Vehicle’s (TV) data to predict the course of action in terms of velocity. The results indicate that a correct assumption about the vehicle’s destination can be achieved in less than 0.4 seconds and that taking the SVs’ data into consideration is very helpful in modeling the vehicle’s future behavior.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Naveed Muhammad, Yar Muhammad
Defence year
2022