Automatic brake Driver Assistance System based on deep learning and fuzzy logic.
Advanced Driver Assistance Systems (ADAS) aim to automate transportation fully. A key part of this automation includes tasks such as traffic light detection and automatic braking. While indoor experiments are prevalent due to computational demands and safety concerns, there is a pressing need for re...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0308858 |
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| _version_ | 1841555507319406592 |
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| author | A R García-Escalante R Q Fuentes-Aguilar A Palma-Zubia E Morales-Vargas |
| author_facet | A R García-Escalante R Q Fuentes-Aguilar A Palma-Zubia E Morales-Vargas |
| author_sort | A R García-Escalante |
| collection | DOAJ |
| description | Advanced Driver Assistance Systems (ADAS) aim to automate transportation fully. A key part of this automation includes tasks such as traffic light detection and automatic braking. While indoor experiments are prevalent due to computational demands and safety concerns, there is a pressing need for research and development of new features to achieve complete automation, addressing real-world implementation challenges by testing them in outdoor environments. These systems seek to provide precise synchronization for decision-making processes and explore algorithms beyond emergency responses, enabling braking actions with short reaction times. Therefore, this work proposes a level 1 ADAS for automatic braking. The implementation uses an NVIDIA Jetson TX2 and a ZED stereo camera for traffic light detection, which, in addition to the depth map provided by the camera and a fuzzy inference system, make the decision to perform automatic braking based on the distance and current state of the traffic light. The contributions of this research work are the development and validation of a one-stage traffic light state detector using EfficientDet D0, a brake profile using fuzzy logic, and the validation with an on-road experiment in Mexico. The traffic light detection model obtained a mAP of 0.96 for distances less than 13 m and 0.89 for 15 m, with an average RMSE of 0.9 m and 0.05 m in the braking force applied, respectively. Integrated systems have a response time of 0.23 s, taking a step further in the state-of-the-art. |
| format | Article |
| id | doaj-art-1efb5730f42d43b1885e6e34327e19e7 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-1efb5730f42d43b1885e6e34327e19e72025-01-08T05:32:20ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e030885810.1371/journal.pone.0308858Automatic brake Driver Assistance System based on deep learning and fuzzy logic.A R García-EscalanteR Q Fuentes-AguilarA Palma-ZubiaE Morales-VargasAdvanced Driver Assistance Systems (ADAS) aim to automate transportation fully. A key part of this automation includes tasks such as traffic light detection and automatic braking. While indoor experiments are prevalent due to computational demands and safety concerns, there is a pressing need for research and development of new features to achieve complete automation, addressing real-world implementation challenges by testing them in outdoor environments. These systems seek to provide precise synchronization for decision-making processes and explore algorithms beyond emergency responses, enabling braking actions with short reaction times. Therefore, this work proposes a level 1 ADAS for automatic braking. The implementation uses an NVIDIA Jetson TX2 and a ZED stereo camera for traffic light detection, which, in addition to the depth map provided by the camera and a fuzzy inference system, make the decision to perform automatic braking based on the distance and current state of the traffic light. The contributions of this research work are the development and validation of a one-stage traffic light state detector using EfficientDet D0, a brake profile using fuzzy logic, and the validation with an on-road experiment in Mexico. The traffic light detection model obtained a mAP of 0.96 for distances less than 13 m and 0.89 for 15 m, with an average RMSE of 0.9 m and 0.05 m in the braking force applied, respectively. Integrated systems have a response time of 0.23 s, taking a step further in the state-of-the-art.https://doi.org/10.1371/journal.pone.0308858 |
| spellingShingle | A R García-Escalante R Q Fuentes-Aguilar A Palma-Zubia E Morales-Vargas Automatic brake Driver Assistance System based on deep learning and fuzzy logic. PLoS ONE |
| title | Automatic brake Driver Assistance System based on deep learning and fuzzy logic. |
| title_full | Automatic brake Driver Assistance System based on deep learning and fuzzy logic. |
| title_fullStr | Automatic brake Driver Assistance System based on deep learning and fuzzy logic. |
| title_full_unstemmed | Automatic brake Driver Assistance System based on deep learning and fuzzy logic. |
| title_short | Automatic brake Driver Assistance System based on deep learning and fuzzy logic. |
| title_sort | automatic brake driver assistance system based on deep learning and fuzzy logic |
| url | https://doi.org/10.1371/journal.pone.0308858 |
| work_keys_str_mv | AT argarciaescalante automaticbrakedriverassistancesystembasedondeeplearningandfuzzylogic AT rqfuentesaguilar automaticbrakedriverassistancesystembasedondeeplearningandfuzzylogic AT apalmazubia automaticbrakedriverassistancesystembasedondeeplearningandfuzzylogic AT emoralesvargas automaticbrakedriverassistancesystembasedondeeplearningandfuzzylogic |