- Titel
- Autonomous Navigation with Deep Reinforcement Learning in CARLA Simulator
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-959145
- Übersetzter Titel (DE)
- Autonome Navigation mit Deep Reinforcement Learning im CARLA-Simulator
- Erstveröffentlichung
- 2025
- Datum der Einreichung
- 07.01.2025
- Datum der Verteidigung
- 11.02.2025
- Abstract (EN)
- Autonomous navigation is a critical component in the development of self-driving vehicles. This thesis explores the application of deep reinforcement learning (DRL) for autonomous navigation within the CARLA simulator, an open-source simulation plat form designed for autonomous driving research. The work focuses on training agents to make optimal driving decisions in dynamic urban environments without human inter vention. Deep learning models were combined with reinforcement learning techniques so the vehicle could perceive its surroundings, predict outcomes, and take appropriate actions to navigate safely. The study evaluates the performance of a state-of-the-art DRL algorithm, Proxi mal policy optimization (PPO), while actively addressing and overcoming challenges like sparse rewards, training stability, and generalization to unseen scenarios. A cus tom reward function was crafted to prioritize collision avoidance, lane-keeping, smooth acceleration, and steering, ensuring the agent adheres to realistic driving behavior. Experimental results demonstrated that the DRL-based agent achieved promising per formance in various simulated driving tasks, including maintaining speed, following traffic signals, lane-following, and intersection handling. Furthermore, the agent ex hibited commendable performance in novel environments, highlighting its capacity to generalize and adapt efficiently. This thesis contributes to the understanding of integrating DRL for autonomous navigation in simulation-based environments and highlights the CARLA simulator’s role as a robust testing ground. The findings lay the groundwork for further ad vancements in sim-to-real transfer and scalable training methods for autonomous vehicles.
- Freie Schlagwörter (EN)
- Autonomous Navigation, Reinforcement Learning, Deep Reinforcement Learning, CARLA Simulator, Proxi mal policy optimization (PPO), collision avoidance, lane-keeping, smooth acceleration, steering, Reward Function
- Klassifikation (DDC)
- 380
- Klassifikation (RVK)
- ZO 4650
- GutachterIn
- Dianzhao Li
- Den akademischen Grad verleihende / prüfende Institution
- Technische Universität Dresden, Dresden
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-959145
- Veröffentlichungsdatum Qucosa
- 26.02.2025
- Dokumenttyp
- Masterarbeit / Staatsexamensarbeit
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0- Inhaltsverzeichnis
1 Introduction 1.1 Problem Statement 5 1.2 Thesis Structure 6 2 Background 2.1 Machine Learning 7 2.2 Deep Learning 8 2.2.1 Feed-Forward Network 9 2.3 Reinforcement Learning 10 2.3.1 Markov Decision Process 10 2.3.2 Bellman Equation 11 2.3.3 Reward Function 13 2.3.4 Action Spaces 13 2.4 Deep Reinforcement Learning 15 2.4.1 Policy-Based Approaches 15 2.4.2 Proximal Policy Optimization 17 3 Experiment Setup 3.1 CARLA Simulator 21 3.1.1 Vehicle Control 22 3.1.2 Maps 23 3.1.3 Waypoints and Routes 24 3.2 Environment Setup 25 3.3 Deep Reinforcement Learning Setup 26 3.3.1 State Space 26 3.3.2 Action Space 29 3.3.3 Reward Function 30 3.4 Network Architecture 33 3.5 Model Training 34 4 Evaluation 4.1 Evaluating Agent on New Maps 38 5 Conclusion