Evaluation Model of Autonomous Vehicles’ Speed Suitability Based on Overtaking Frequency
Speed judgment is a vital component of autonomous driving perception systems. Automobile drivers were able to evaluate their speed as a result of their driving experience. However, driverless automobiles cannot autonomously evaluate their speed suitability through external environmental factors such...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/2/371 |
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| author | Shiwu Li Mengyuan Huang Mengzhu Guo Miao Yu |
| author_facet | Shiwu Li Mengyuan Huang Mengzhu Guo Miao Yu |
| author_sort | Shiwu Li |
| collection | DOAJ |
| description | Speed judgment is a vital component of autonomous driving perception systems. Automobile drivers were able to evaluate their speed as a result of their driving experience. However, driverless automobiles cannot autonomously evaluate their speed suitability through external environmental factors such as the surrounding conditions and traffic flows. This study introduced the parameter of overtaking frequency (OTF) based on the state of the traffic flow on both sides of the lane to reflect the difference between the speed of a driverless automobile and its surrounding traffic to solve the above problem. In addition, a speed evaluation algorithm was proposed based on the long short-term memory (LSTM) model. To train the LSTM model, we extracted OTF as the first observation variable, and the characteristic parameters of the vehicle’s longitudinal motion and the comparison parameters with the leading vehicle were used as the second observation variables. The algorithm judged the velocity using a hierarchical method. We conducted a road test by using real vehicles and the algorithms verified the data, which showed the accuracy rate of the model is 93%. As a result, OTF is introduced as one of the observed variables that can support the accuracy of the algorithm used to judge speed. |
| first_indexed | 2024-03-09T05:45:32Z |
| format | Article |
| id | doaj.art-e97fa9c04a4f497c9d6a60ea169467ae |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:45:32Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e97fa9c04a4f497c9d6a60ea169467ae2023-12-03T12:21:52ZengMDPI AGSensors1424-82202021-01-0121237110.3390/s21020371Evaluation Model of Autonomous Vehicles’ Speed Suitability Based on Overtaking FrequencyShiwu Li0Mengyuan Huang1Mengzhu Guo2Miao Yu3School of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, ChinaSchool of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, ChinaSchool of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, ChinaSchool of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, ChinaSpeed judgment is a vital component of autonomous driving perception systems. Automobile drivers were able to evaluate their speed as a result of their driving experience. However, driverless automobiles cannot autonomously evaluate their speed suitability through external environmental factors such as the surrounding conditions and traffic flows. This study introduced the parameter of overtaking frequency (OTF) based on the state of the traffic flow on both sides of the lane to reflect the difference between the speed of a driverless automobile and its surrounding traffic to solve the above problem. In addition, a speed evaluation algorithm was proposed based on the long short-term memory (LSTM) model. To train the LSTM model, we extracted OTF as the first observation variable, and the characteristic parameters of the vehicle’s longitudinal motion and the comparison parameters with the leading vehicle were used as the second observation variables. The algorithm judged the velocity using a hierarchical method. We conducted a road test by using real vehicles and the algorithms verified the data, which showed the accuracy rate of the model is 93%. As a result, OTF is introduced as one of the observed variables that can support the accuracy of the algorithm used to judge speed.https://www.mdpi.com/1424-8220/21/2/371overtaking frequencyautonomous vehicleshierarchical judgmentvehicle speed suitabilitymillimeter-wave radarintelligent driving system |
| spellingShingle | Shiwu Li Mengyuan Huang Mengzhu Guo Miao Yu Evaluation Model of Autonomous Vehicles’ Speed Suitability Based on Overtaking Frequency Sensors overtaking frequency autonomous vehicles hierarchical judgment vehicle speed suitability millimeter-wave radar intelligent driving system |
| title | Evaluation Model of Autonomous Vehicles’ Speed Suitability Based on Overtaking Frequency |
| title_full | Evaluation Model of Autonomous Vehicles’ Speed Suitability Based on Overtaking Frequency |
| title_fullStr | Evaluation Model of Autonomous Vehicles’ Speed Suitability Based on Overtaking Frequency |
| title_full_unstemmed | Evaluation Model of Autonomous Vehicles’ Speed Suitability Based on Overtaking Frequency |
| title_short | Evaluation Model of Autonomous Vehicles’ Speed Suitability Based on Overtaking Frequency |
| title_sort | evaluation model of autonomous vehicles speed suitability based on overtaking frequency |
| topic | overtaking frequency autonomous vehicles hierarchical judgment vehicle speed suitability millimeter-wave radar intelligent driving system |
| url | https://www.mdpi.com/1424-8220/21/2/371 |
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