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In order to sense the intention of human-driven vehicles and reduce the self-driving collision avoidance rate, an improved intention prediction method for human-driving vehicles based on unsupervised, deep inverse reinforcement learning is proposed. Firstly, a contrast discriminator module was proposed to extract richer features. Then, the residual module was created to overcome the drawbacks of gradient disappearance and network degradation with the increase in network layers. Furthermore, the dropout layer was generated to prevent the over-fitting phenomenon in the whole training process of the GRU network, so as to improve the generalization ability of the network model. Finally, abundant experiments were conducted on datasets to evaluate our proposed method. The pass rate of self-driving vehicles with conservative driver probabilities of p = 0.25, p = 0.4, and p = 0.6 improved by a maximum of 8%, 10%, and 3%, compared with the classical method LSTM and VAE + RNN. It indicates that the prediction results of our proposed method fit more with the basic structure of the given traffic scenario in a long-term prediction range, which verifies the effectiveness of our proposed method.<\/jats:p>","DOI":"10.3390\/s22249943","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T09:31:01Z","timestamp":1671442261000},"page":"9943","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Driver Intent-Based Intersection Autonomous Driving Collision Avoidance Reinforcement Learning Algorithm"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8134-6913","authenticated-orcid":false,"given":"Ting","family":"Chen","sequence":"first","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youjing","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huizhao","family":"Tu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10408","DOI":"10.1109\/JIOT.2019.2939180","article-title":"Toward collision-free and efficient coordination for automated vehicles at unsignalized intersection","volume":"6","author":"Qian","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sunberg, Z.N., Ho, C.J., and Kochenderfer, M.J. 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