Autonomous Navigation with Deep Reinforcement Learning in Carla Simulator

Peilin Wang

AutorIn

Peilin Wang

Titel

Autonomous Navigation with Deep Reinforcement Learning in Carla Simulator

Zitierfähige Url:

https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-885522

Datum der Einreichung

02.11.2023

Datum der Verteidigung

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.

Freie Schlagwörter (EN)

Deep Reinforcement Learning, End to End Navigation, PPO, Carla

Klassifikation (DDC)

380

Klassifikation (RVK)

ZO 4650

GutachterIn