Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control
The use of Deep Learning algorithms in the domain of Decision Making for Autonomous Vehicles has garnered significant attention in the literature in recent years, showcasing considerable potential. Nevertheless, most of the solutions proposed by the scientific community encounter difficulties in rea...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/117 |
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| author | Rodrigo Gutiérrez-Moreno Rafael Barea Elena López-Guillén Felipe Arango Fabio Sánchez-García Luis M. Bergasa |
| author_facet | Rodrigo Gutiérrez-Moreno Rafael Barea Elena López-Guillén Felipe Arango Fabio Sánchez-García Luis M. Bergasa |
| author_sort | Rodrigo Gutiérrez-Moreno |
| collection | DOAJ |
| description | The use of Deep Learning algorithms in the domain of Decision Making for Autonomous Vehicles has garnered significant attention in the literature in recent years, showcasing considerable potential. Nevertheless, most of the solutions proposed by the scientific community encounter difficulties in real-world applications. This paper aims to provide a realistic implementation of a hybrid Decision Making module in an Autonomous Driving stack, integrating the learning capabilities from the experience of Deep Reinforcement Learning algorithms and the reliability of classical methodologies. Our Decision Making system is in charge of generating steering and velocity signals using the HD map information and sensors pre-processed data. This work encompasses the implementation of concatenated scenarios in simulated environments, and the integration of Autonomous Driving modules. Specifically, the authors address the Decision Making problem by employing a Partially Observable Markov Decision Process formulation and offer a solution through the use of Deep Reinforcement Learning algorithms. Furthermore, an additional control module to execute the decisions in a safe and comfortable way through a hybrid architecture is presented. The proposed architecture is validated in the CARLA simulator by navigating through multiple concatenated scenarios, outperforming the CARLA Autopilot in terms of completion time, while ensuring both safety and comfort. |
| format | Article |
| id | doaj-art-1fc818fc019b4787977ed0a809f55322 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1fc818fc019b4787977ed0a809f553222025-08-20T02:47:13ZengMDPI AGSensors1424-82202024-12-0125111710.3390/s25010117Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical ControlRodrigo Gutiérrez-Moreno0Rafael Barea1Elena López-Guillén2Felipe Arango3Fabio Sánchez-García4Luis M. Bergasa5Electronics Departament, University of Alcalá (UAH), 28805 Alcalá de Henares, Madrid, SpainElectronics Departament, University of Alcalá (UAH), 28805 Alcalá de Henares, Madrid, SpainElectronics Departament, University of Alcalá (UAH), 28805 Alcalá de Henares, Madrid, SpainElectronics Departament, University of Alcalá (UAH), 28805 Alcalá de Henares, Madrid, SpainElectronics Departament, University of Alcalá (UAH), 28805 Alcalá de Henares, Madrid, SpainElectronics Departament, University of Alcalá (UAH), 28805 Alcalá de Henares, Madrid, SpainThe use of Deep Learning algorithms in the domain of Decision Making for Autonomous Vehicles has garnered significant attention in the literature in recent years, showcasing considerable potential. Nevertheless, most of the solutions proposed by the scientific community encounter difficulties in real-world applications. This paper aims to provide a realistic implementation of a hybrid Decision Making module in an Autonomous Driving stack, integrating the learning capabilities from the experience of Deep Reinforcement Learning algorithms and the reliability of classical methodologies. Our Decision Making system is in charge of generating steering and velocity signals using the HD map information and sensors pre-processed data. This work encompasses the implementation of concatenated scenarios in simulated environments, and the integration of Autonomous Driving modules. Specifically, the authors address the Decision Making problem by employing a Partially Observable Markov Decision Process formulation and offer a solution through the use of Deep Reinforcement Learning algorithms. Furthermore, an additional control module to execute the decisions in a safe and comfortable way through a hybrid architecture is presented. The proposed architecture is validated in the CARLA simulator by navigating through multiple concatenated scenarios, outperforming the CARLA Autopilot in terms of completion time, while ensuring both safety and comfort.https://www.mdpi.com/1424-8220/25/1/117autonomous drivingdeep reinforcement learningdecision-makingvehicle controlCARLA simulator |
| spellingShingle | Rodrigo Gutiérrez-Moreno Rafael Barea Elena López-Guillén Felipe Arango Fabio Sánchez-García Luis M. Bergasa Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control Sensors autonomous driving deep reinforcement learning decision-making vehicle control CARLA simulator |
| title | Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control |
| title_full | Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control |
| title_fullStr | Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control |
| title_full_unstemmed | Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control |
| title_short | Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control |
| title_sort | enhancing autonomous driving in urban scenarios a hybrid approach with reinforcement learning and classical control |
| topic | autonomous driving deep reinforcement learning decision-making vehicle control CARLA simulator |
| url | https://www.mdpi.com/1424-8220/25/1/117 |
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