Environmental-Driven Approach towards Level 5 Self-Driving
As technology advances in almost all areas of life, many companies and researchers are working to develop fully autonomous vehicles. Such level 5 autonomous driving, unlike levels 0 to 4, is a driverless vehicle stage and so the leap from level 4 to level 5 autonomous driving requires much more rese...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/2/485 |
| _version_ | 1797339397832572928 |
|---|---|
| author | Mohammad Hurair Jaeil Ju Junghee Han |
| author_facet | Mohammad Hurair Jaeil Ju Junghee Han |
| author_sort | Mohammad Hurair |
| collection | DOAJ |
| description | As technology advances in almost all areas of life, many companies and researchers are working to develop fully autonomous vehicles. Such level 5 autonomous driving, unlike levels 0 to 4, is a driverless vehicle stage and so the leap from level 4 to level 5 autonomous driving requires much more research and experimentation. For autonomous vehicles to safely drive in complex environments, autonomous cars should ensure end-to-end delay deadlines of sensor systems and car-controlling algorithms including machine learning modules, which are known to be very computationally intensive. To address this issue, we propose a new framework, i.e., an environment-driven approach for autonomous cars. Specifically, we identify environmental factors that we cannot control at all, and controllable internal factors such as sensing frequency, image resolution, prediction rate, car speed, and so on. Then, we design an admission control module that allows us to control internal factors such as image resolution and detection period to determine whether given parameters are acceptable or not for supporting end-to-end deadlines in the current environmental scenario while maintaining the accuracy of autonomous driving. The proposed framework has been verified with an RC car and a simulator. |
| first_indexed | 2024-03-08T09:46:22Z |
| format | Article |
| id | doaj.art-31d62936828b49c895231285fb7ad1f2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T09:46:22Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-31d62936828b49c895231285fb7ad1f22024-01-29T14:15:15ZengMDPI AGSensors1424-82202024-01-0124248510.3390/s24020485Environmental-Driven Approach towards Level 5 Self-DrivingMohammad Hurair0Jaeil Ju1Junghee Han2School of Electronics and Information Engineering, Korea Aerospace University, 76 Hanggongdaehang-ro, Goyang-si 412-791, Gyeonggi-do, Republic of KoreaSchool of Electronics and Information Engineering, Korea Aerospace University, 76 Hanggongdaehang-ro, Goyang-si 412-791, Gyeonggi-do, Republic of KoreaSchool of Electronics and Information Engineering, Korea Aerospace University, 76 Hanggongdaehang-ro, Goyang-si 412-791, Gyeonggi-do, Republic of KoreaAs technology advances in almost all areas of life, many companies and researchers are working to develop fully autonomous vehicles. Such level 5 autonomous driving, unlike levels 0 to 4, is a driverless vehicle stage and so the leap from level 4 to level 5 autonomous driving requires much more research and experimentation. For autonomous vehicles to safely drive in complex environments, autonomous cars should ensure end-to-end delay deadlines of sensor systems and car-controlling algorithms including machine learning modules, which are known to be very computationally intensive. To address this issue, we propose a new framework, i.e., an environment-driven approach for autonomous cars. Specifically, we identify environmental factors that we cannot control at all, and controllable internal factors such as sensing frequency, image resolution, prediction rate, car speed, and so on. Then, we design an admission control module that allows us to control internal factors such as image resolution and detection period to determine whether given parameters are acceptable or not for supporting end-to-end deadlines in the current environmental scenario while maintaining the accuracy of autonomous driving. The proposed framework has been verified with an RC car and a simulator.https://www.mdpi.com/1424-8220/24/2/485autonomous drivinglevel 5realtimeend-to-end delaymachine learning |
| spellingShingle | Mohammad Hurair Jaeil Ju Junghee Han Environmental-Driven Approach towards Level 5 Self-Driving Sensors autonomous driving level 5 realtime end-to-end delay machine learning |
| title | Environmental-Driven Approach towards Level 5 Self-Driving |
| title_full | Environmental-Driven Approach towards Level 5 Self-Driving |
| title_fullStr | Environmental-Driven Approach towards Level 5 Self-Driving |
| title_full_unstemmed | Environmental-Driven Approach towards Level 5 Self-Driving |
| title_short | Environmental-Driven Approach towards Level 5 Self-Driving |
| title_sort | environmental driven approach towards level 5 self driving |
| topic | autonomous driving level 5 realtime end-to-end delay machine learning |
| url | https://www.mdpi.com/1424-8220/24/2/485 |
| work_keys_str_mv | AT mohammadhurair environmentaldrivenapproachtowardslevel5selfdriving AT jaeilju environmentaldrivenapproachtowardslevel5selfdriving AT jungheehan environmentaldrivenapproachtowardslevel5selfdriving |