Rodrigues Petry, M. Gabriel (2018) End-to-end driving for autonomous vehicles - development of a test platform PFE - Project Graduation, ENSTA.
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This report describes the work executed during an engineering internship at AKKA autonomous vehicles group. The project conducted during this time consisted in implementing a test platform for end-to-end driving policies for autonomous vehicles, in the form of a software architecture and also a physical prototype. This kind of approach has been gaining relevance in the last years due to the advances in the machine learning field, making possible a direct association between sensor data and driving commands through deep learning techniques. The vehicle’s architecture was developed in ROS, with a simulation on Gazebo and interface to the driving agent achieved through the OpenAI Gym toolkit. System behavior was validated in the conducted tests, and the result is a modular and flexible design, where developing driving algorithms is independent of low level vehicle dynamics and the nature of the environment. Though preliminary tests with a Q-learning algorithm did not give the expected results, the studied approach has potential to improve after addressing the difficulties exposed in this text.
|Item Type:||Thesis (PFE - Project Graduation)|
|Subjects:||Mathematics and Applications|
|Deposited By:||Gabriel Petry|
|Deposited On:||03 avr. 2019 11:25|
|Dernière modification:||03 avr. 2019 11:25|
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