Long-term prediction of surrounding agents’ motion in an urban environment for self-driving vehicles

Bohdan Biesiedin

Master's thesis

Abstract:

In the scope of this research, an investigation of the area of self-driving vehicles was done. As a result of the investigation a model which implements long-term prediction of surrounding agents motion in urban environment for self-driving vehicle was implemented. The proposed model is capable of capturing and recognizing the surrounding agents, tracking their motion, memorizing the history of their motion and making prediction about the future movements and maneuvers. The output of the proposed model is a set of trajectories for each of the surrounding vehicles. At the beginning of the research, an investigation of the history and current state of the field was conducted. In the scope of the investigation, appropriate papers, books and articles were studied. In addition, a number of experts in the field of autonomous vehicles and motion prediction were interviewed in order to get more insights about the current state of art and approaches in the field. The results of the first part of research were used in order to design and construct a model which fulfils the main aim of the work. In order to have a clear view on what should be done and how the final solution should look like at the result of the design process, a set of system and functional requirements were proposed and further realized in the design process. The main components of the model were developed and described in the practical part of the work. Flowcharts were used in order to depict algorithms of main components of the model. In conclusion, developed model is performing well for the tested time ranges and bypasses the system created on the basis of the standard model in predicting the trajectories of agents. It can be used as it is in the result of the work or modified for a particular use case and integrated into a control system.

Abstract:

In the scope of this research, an investigation of the area of self-driving vehicles was done. As a result of the investigation a model which implements long-term prediction of surrounding agents motion in urban environment for self-driving vehicle was implemented. The proposed model is capable of capturing and recognizing the surrounding agents, tracking their motion, memorizing the history of their motion and making prediction about the future movements and maneuvers. The output of the proposed model is a set of trajectories for each of the surrounding vehicles. At the beginning of the research, an investigation of the history and current state of the field was conducted. In the scope of the investigation, appropriate papers, books and articles were studied. In addition, a number of experts in the field of autonomous vehicles and motion prediction were interviewed in order to get more insights about the current state of art and approaches in the field. The results of the first part of research were used in order to design and construct a model which fulfils the main aim of the work. In order to have a clear view on what should be done and how the final solution should look like at the result of the design process, a set of system and functional requirements were proposed and further realized in the design process. The main components of the model were developed and described in the practical part of the work. Flowcharts were used in order to depict algorithms of main components of the model. In conclusion, developed model is performing well for the tested time ranges and bypasses the system created on the basis of the standard model in predicting the trajectories of agents. It can be used as it is in the result of the work or modified for a particular use case and integrated into a control system.

Keywords

Self-Driving Cars, Autonomy, GPS, LIDAR, RADAR, Autonomous Driving, Neural Networks, Prediction, AI, Trajectory, Machine Learning, Agents, Urban Environment, Motion Prediction, Algorithm

Keywords

Neural Networks, Autonomy, GPS, LIDAR, RADAR, AI, Trajectory, Machine Learning, Agents, Urban Environment, Motion Prediction, Algorithm, Self-Driving Cars, Autonomous Driving, Prediction

Language used: English

Date on which the thesis was submitted / produced: 3. 11. 2019

Thesis defence

• Date of defence: 13. 1. 2021
• Supervisor: Martin Potančok
• Reader: Filip Vencovský

Vysoká škola ekonomická v Praze

Master programme / field:
Aplikovaná informatika / Information Systems Management