{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:50:50Z","timestamp":1760161850973,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T00:00:00Z","timestamp":1609977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFB1600501"],"award-info":[{"award-number":["2018YFB1600501"]}]},{"name":"Jilin province transportation science and technology project","award":["2019-1-16"],"award-info":[{"award-number":["2019-1-16"]}]},{"name":"National Natural Science Foundation Item","award":["71971097"],"award-info":[{"award-number":["71971097"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s21020371","type":"journal-article","created":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T23:03:42Z","timestamp":1610319822000},"page":"371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evaluation Model of Autonomous Vehicles\u2019 Speed Suitability Based on Overtaking Frequency"],"prefix":"10.3390","volume":"21","author":[{"given":"Shiwu","family":"Li","sequence":"first","affiliation":[{"name":"School of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyuan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengzhu","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, China"},{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, 5988 Renmin Street, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miao","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TIV.2016.2578706","article-title":"A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles","volume":"1","author":"Paden","year":"2016","journal-title":"IEEE Trans. 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