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Predicting Trajectories of Vehicles and Pedestrians for Driving Assistent Systems
Mudroň, Marek ; Musil, Petr (referee) ; Smrž, Pavel (advisor)
This bachelor thesis deals with representation of a traffic scene by processing monocular video sequence. I try to predict a trajectory of detected vehicles in a short time horizon, based on created representation. Current approaches use multiple expensive sensors to gather instant information of environment. In the thesis I introduce technique, which is able to extract data from an environment by image processing techniques without the need of expensive sensors. The result of this work is a system creating opportunity to reduce the sensor costs of a system for scene representation and trajectory prediction of vehicles in the scene. In addition, comparison of models trained on differently processed data is provided, as well as data about how my system approximates the most reliable prediction models.
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Computational model of the environment of an autonomous vehicle
Doležel, Radek ; Píštěk, Václav (referee) ; Kučera, Pavel (advisor)
The aim of this thesis is to develop a functional computational model for vehicle motion prediction based on a search of sensors and their locations on the vehicle, neural networks for computer vision, datasets for network learning, and programs for creating simulations and virtual environments. The paper describes the process of creating the vehicle virtual environment and simulation. In addition, sensor placement designs including their parameters are developed. Subsequently, the programmed vehicle trajectory prediction algorithm including learning and neural network implementation is presented. Finally, the results of the developed algorithm are presented.
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Predicting Trajectories of Vehicles and Pedestrians for Driving Assistent Systems
Mudroň, Marek ; Musil, Petr (referee) ; Smrž, Pavel (advisor)
This bachelor thesis deals with representation of a traffic scene by processing monocular video sequence. I try to predict a trajectory of detected vehicles in a short time horizon, based on created representation. Current approaches use multiple expensive sensors to gather instant information of environment. In the thesis I introduce technique, which is able to extract data from an environment by image processing techniques without the need of expensive sensors. The result of this work is a system creating opportunity to reduce the sensor costs of a system for scene representation and trajectory prediction of vehicles in the scene. In addition, comparison of models trained on differently processed data is provided, as well as data about how my system approximates the most reliable prediction models.
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