- title
- Autonomous Navigation with Deep Reinforcement Learning in Carla Simulator
- Please use the following URL when quoting:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-885522
- Date of submission
- 02.11.2023
- Date of defense
- 29.11.2023
- Abstract (EN)
- With the rapid development of autonomous driving and artificial intelligence technology, end-to-end autonomous driving technology has become a research hotspot. This thesis aims to explore the application of deep reinforcement learning in the realizing of end-to-end autonomous driving. We built a deep reinforcement learning virtual environment in the Carla simulator, and based on it, we trained a policy model to control a vehicle along a preplanned route. For the selection of the deep reinforcement learning algorithms, we have used the Proximal Policy Optimization algorithm due to its stable performance. Considering the complexity of end-to-end autonomous driving, we have also carefully designed a comprehensive reward function to train the policy model more efficiently. The model inputs for this study are of two types: firstly, real-time road information and vehicle state data obtained from the Carla simulator, and secondly, real-time images captured by the vehicle's front camera. In order to understand the influence of different input information on the training effect and model performance, we conducted a detailed comparative analysis. The test results showed that the accuracy and significance of the information has a significant impact on the learning effect of the agent, which in turn has a direct impact on the performance of the model. Through this study, we have not only confirmed the potential of deep reinforcement learning in the field of end-to-end autonomous driving, but also provided an important reference for future research and development of related technologies.
- Keywords (EN)
- Deep Reinforcement Learning, End to End Navigation, PPO, Carla
- Classification (DDC)
- 380
- Classification (RVK)
- ZO 4650
- Examiner
- Prof. Dr. Ostap Okhrin
- Prof. Dr. Georg Hirte
- supervisor
- M.Sc. Dianzhao Li
- Awarding institution
- Technische Universität Dresden, Dresden
- version
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-885522
- Qucosa date of publication
- 08.12.2023
- Document type
- diploma_thesis
- Document language
- English
- licence
CC BY 4.0