{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T17:56:28Z","timestamp":1783014988351,"version":"3.54.6"},"reference-count":45,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Humanities and Social Science of Education Ministry of China","award":["24YJA630013"],"award-info":[{"award-number":["24YJA630013"]}]},{"DOI":"10.13039\/100007834","name":"Ningbo Natural Science Foundation of China","doi-asserted-by":"crossref","award":["2024J125"],"award-info":[{"award-number":["2024J125"]}],"id":[{"id":"10.13039\/100007834","id-type":"DOI","asserted-by":"crossref"}]},{"name":"\u201cInnovation Yongjiang 2035\u201d Key R&D Programme","award":["2024H032"],"award-info":[{"award-number":["2024H032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>With the widespread application of autonomous vehicles (AVs), their dynamic interactions with other road users pose significant challenges to trajectory planning. Previous research on trajectory planning in shared spaces has mainly focused on generating smooth trajectories, while research considering the risks of human\u2013vehicle interactions remains insufficient. Therefore, a risk-considered trajectory planning framework for autonomous vehicles is proposed. This framework includes two modules: pedestrian trajectory prediction and vehicle planning. In the prediction module, Social-STGCNN is used to predict pedestrian trajectories, obtaining a series of trajectories and probabilities, which serve as input to the planning module. To ensure the rationality of trajectory planning, a planning model is established in Frenet coordinates based on a quintic polynomial. Combining Bayesian and equality principles, a risk-considered cost function is designed. Under this framework, the risk value is calculated using the pedestrian trajectory prediction probability, and further Bayesian and equality costs are calculated. Based on the constraints, the trajectory with the minimum cost is solved. To evaluate the rationality of this framework, we designed simulation experiments for five typical high-conflict scenarios: overtaking in the same direction, head-on collision, pedestrian crossing, encountering pedestrians from multiple directions, and turning while encountering pedestrians crossing. Simultaneously, the framework is validated in a real-world environment. The results show that the proposed method can accurately capture pedestrians\u2019 crossing intentions and effectively avoid pedestrians. The trajectory generated in the real environment is highly consistent with that of a driver, and it exhibits excellent adaptability and robustness in high-density mixed traffic environments.<\/jats:p>","DOI":"10.3390\/systems14040434","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T10:14:46Z","timestamp":1776334486000},"page":"434","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["From Probabilistic Pedestrian Intent to Risk-Optimal Trajectories: A Prediction-Driven Planning Framework in Shared Spaces"],"prefix":"10.3390","volume":"14","author":[{"given":"Yi","family":"Luo","sequence":"first","affiliation":[{"name":"Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Data Science, New York University Shanghai, Shanghai 200072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongjun","family":"Cheng","sequence":"additional","affiliation":[{"name":"Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"125144","DOI":"10.1016\/j.eswa.2024.125144","article-title":"PI-STGnet: Physics-Integrated Spatiotemporal Graph Neural Network with Fundamental Diagram Learner for Highway Traffic Flow Prediction","volume":"258","author":"Wang","year":"2024","journal-title":"Expert Syst. 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