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Intell. Syst. Technol."],"published-print":{"date-parts":[[2022,6,30]]},"abstract":"<jats:p>Sampling-based motion planning (SBMP) is a major algorithmic trajectory planning approach in autonomous driving given its high efficiency and outstanding performance in practice. However, driving safety still calls for further refinement of SBMP. In this article we organically integrate algorithmic motion planning with learning models to improve SBMP in highway traffic scenarios from the following two perspectives. First, given the number of points to be sampled, we develop a new model to sample \u201cimportant\u201d points for SBMP by predicting the intention of surrounding vehicles and learning the distribution of human drivers\u2019 trajectory. Second, we empirically study the relationship between the number of sample points and the environment, which is largely ignored in conventional SBMP. Then, we provide a guideline to select the appropriate number of points to be sampled under different scenarios to guarantee efficiency. The simulation experiments are conducted based on the vehicle trajectory dataset NGSIM. The results show that the proposed sampling strategy outperforms existing sampling strategies in terms of the computing time, traveling time, and smoothness of the trajectory.<\/jats:p>","DOI":"10.1145\/3469086","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T15:36:16Z","timestamp":1642520176000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Integrating Algorithmic Sampling-Based Motion Planning with Learning in Autonomous Driving"],"prefix":"10.1145","volume":"13","author":[{"given":"Yifan","family":"Zhang","sequence":"first","affiliation":[{"name":"City University of Hong Kong, Hong Kong SAR and City University of Hong Kong Shenzhen Research Institute, Nanshan District, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghuai","family":"Zhang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong SAR and City University of Hong Kong Shenzhen Research Institute, Nanshan District, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jindi","family":"Zhang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong SAR and City University of Hong Kong Shenzhen Research Institute, Nanshan District, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9318-1482","authenticated-orcid":false,"given":"Jianping","family":"Wang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong SAR and City University of Hong Kong Shenzhen Research Institute, Nanshan District, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kejie","family":"Lu","sequence":"additional","affiliation":[{"name":"University of Puerto Rico at Mayag\u00fcez, Mayag\u00fcez, Puerto Rico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeff","family":"Hong","sequence":"additional","affiliation":[{"name":"Fudan University, Yangpu District, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,1,18]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2013.6630906"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2015.7353738"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2017.8317757"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2019.2955371"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2019.2904394"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/CCTA.2018.8511371"},{"key":"e_1_3_1_8_2","first-page":"180","volume-title":"IEEE Intelligent Vehicles Symposium (IV\u201917)","author":"Bounini F.","year":"2017","unstructured":"F. 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