{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T20:37:18Z","timestamp":1764103038462,"version":"3.37.3"},"reference-count":18,"publisher":"World Scientific Pub Co Pte Ltd","issue":"05","funder":[{"name":"Research on Innovative Technology of Electric Vehicle Lightweight","award":["XJZK201813"],"award-info":[{"award-number":["XJZK201813"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2020,5]]},"abstract":"<jats:p> With the economic growth of our country and the continuous improvement of people\u2019s living standards, cars have begun to enter thousands of households and become a necessity for people. However, the rapid growth of the number of automobiles has led to a sustained increase in carbon dioxide emissions and a significant decline in urban air quality, which seriously restricts the sustainable development of cities. With the introduction of the national air quality protection policy, electric vehicles will eventually replace the existing fuel vehicles and become a new generation of transportation for people to travel. At the same time, the large expansion of the number of cars has increased the hidden dangers of traffic accidents. In order to ensure the safety of pedestrians, drivers are given a more intelligent driving environment. This paper presents the research of pedestrian detection and pedestrian distance algorithm based on image processing. By comparing the performance of pedestrian detection algorithm based on SSD with traditional HOG+SVM pedestrian detection algorithm, the results of pedestrian\u2013vehicle distance calculation are detected, and the feasibility and effectiveness of the algorithm are obtained. The results show that the proposed algorithm has good feasibility and practicability, and provide a good reference for the research of pedestrian detection algorithm for electric vehicles. <\/jats:p>","DOI":"10.1142\/s0218001420540142","type":"journal-article","created":{"date-parts":[[2019,7,4]],"date-time":"2019-07-04T05:59:22Z","timestamp":1562219962000},"page":"2054014","source":"Crossref","is-referenced-by-count":3,"title":["Research on Pedestrian Detection and Vehicle Distance Algorithms of Electric Vehicle Based on Image Processing"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4528-0889","authenticated-orcid":false,"given":"Jingyi","family":"Liu","sequence":"first","affiliation":[{"name":"School of Intelligent Manufacturing and Automobile, Chongqing College of Electronic Engineering, Chongqing, P.\u00a0R.\u00a0China"},{"name":"Applied Mathematics and Mechanics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2019,9,11]]},"reference":[{"key":"S0218001420540142BIB002","first-page":"245","volume":"18","author":"Cheng J.","year":"2017","journal-title":"Automob. 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