Behavioral Intention Prediction in Driving Scenes: A Survey
In driving scenes, road agents often engage in frequent interaction and strive to understand their surroundings. Ego-agent (each road agent itself) predicts what behavior will be engaged by other road users all the time and expects a shared and consistent understanding for safe movement. To achieve...
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| Published in | IEEE transactions on intelligent transportation systems Vol. 25; no. 8; pp. 8334 - 8355 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.08.2024
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| Subjects | |
| Online Access | Get full text |
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| Abstract | In driving scenes, road agents often engage in frequent interaction and strive to understand their surroundings. Ego-agent (each road agent itself) predicts what behavior will be engaged by other road users all the time and expects a shared and consistent understanding for safe movement. To achieve this, Behavioral Intention Prediction (BIP) simulates such a human consideration process to anticipate specific behaviors, and the rapid development of BIP inevitably leads to new issues and challenges. To catalyze future research, this work provides a comprehensive review of BIP from the available datasets, key factors, challenges, pedestrian-centric and vehicle-centric BIP approaches, and BIP-aware applications. The investigation reveals that data-driven deep learning approaches have become the primary pipelines, while the behavioral intention types are still limited in most current datasets and methods (e.g., Crossing (C) and Not Crossing (NC) for pedestrians and Lane Changing (LC) for vehicles) in this field. In addition, current research on BIP in safe-critical scenarios (e.g., near-crashing situations) is limited. Through this investigation, we identify open issues in behavioral intention prediction and suggest possible insights for future research. |
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| AbstractList | In driving scenes, road agents often engage in frequent interaction and strive to understand their surroundings. Ego-agent (each road agent itself) predicts what behavior will be engaged by other road users all the time and expects a shared and consistent understanding for safe movement. To achieve this, Behavioral Intention Prediction (BIP) simulates such a human consideration process to anticipate specific behaviors, and the rapid development of BIP inevitably leads to new issues and challenges. To catalyze future research, this work provides a comprehensive review of BIP from the available datasets, key factors, challenges, pedestrian-centric and vehicle-centric BIP approaches, and BIP-aware applications. The investigation reveals that data-driven deep learning approaches have become the primary pipelines, while the behavioral intention types are still limited in most current datasets and methods (e.g., Crossing (C) and Not Crossing (NC) for pedestrians and Lane Changing (LC) for vehicles) in this field. In addition, current research on BIP in safe-critical scenarios (e.g., near-crashing situations) is limited. Through this investigation, we identify open issues in behavioral intention prediction and suggest possible insights for future research. |
| Author | Chua, Tat-Seng Fang, Jianwu Wang, Fan Xue, Jianru |
| Author_xml | – sequence: 1 givenname: Jianwu orcidid: 0000-0002-0300-6892 surname: Fang fullname: Fang, Jianwu email: fangjianwu@mail.xjtu.edu.cn organization: National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China – sequence: 2 givenname: Fan surname: Wang fullname: Wang, Fan organization: College of Transportation Engineering, Chang'an University, Xi'an, China – sequence: 3 givenname: Jianru orcidid: 0000-0002-4994-9343 surname: Xue fullname: Xue, Jianru email: jrxue@mail.xjtu.edu.cn organization: National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China – sequence: 4 givenname: Tat-Seng orcidid: 0000-0001-6097-7807 surname: Chua fullname: Chua, Tat-Seng email: dcscts@nus.edu.sg organization: Sea-NExT++ Joint Research Centre, School of Computing, National University of Singapore, Cluny Road, Singapore |
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| Snippet | In driving scenes, road agents often engage in frequent interaction and strive to understand their surroundings. Ego-agent (each road agent itself) predicts... |
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| SubjectTerms | Behavioral intention prediction Behavioral sciences Benchmark testing benchmarks challenges Image analysis Pedestrians Predictive models promising approaches Reviews road agents Roads Surveys Trajectory |
| Title | Behavioral Intention Prediction in Driving Scenes: A Survey |
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