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S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

文献类型：期刊论文

作者Wang, Xiao1; Tang, Ke2; Dai, Xingyuan3,4; Xu, Jintao2; Du, Quancheng5; Ai, Rui2; Wang, Yuxiao3,6; Gu, Weihao2
刊名IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
出版日期2024-02-01
卷号9期号:2页码:3220-3231
关键词Autonomous driving social interactive traffic scenarios trajectory planning intention prediction Social-Aware Driving Risk Field
ISSN号2379-8858
DOI10.1109/TIV.2023.3338483
通讯作者Wang, Xiao(xiao.wang@ahu.edu.cn)
英文摘要In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning ((STP)-T-4) framework. Specifically, (STP)-T-4 integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV's planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. (STP)-T-4 employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, (STP)-T-4 executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed (STP)-T-4 in terms of safety and rationality. (STP)-T-4 achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%.
WOS关键词PREDICTION ; MODEL
资助项目National Natural Science Foundation of China
WOS研究方向Computer Science ; Engineering ; Transportation
语种英语
WOS记录号WOS:001215322100025
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/59085]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Wang, Xiao
作者单位1.Anhui Univ, Engn Res Ctr Autonomous Unmanned Syst Technol, Minist Educ, Hefei 230031, Peoples R China
2.Haomo Technol Co Ltd, Beijing 100192, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
4.Qingdao Acad Intelligent Ind, Qingdao 266114, Peoples R China
5.Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China
6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Xiao,Tang, Ke,Dai, Xingyuan,et al. S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2024,9(2):3220-3231.
APA Wang, Xiao.,Tang, Ke.,Dai, Xingyuan.,Xu, Jintao.,Du, Quancheng.,...&Gu, Weihao.(2024).S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,9(2),3220-3231.
MLA Wang, Xiao,et al."S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 9.2(2024):3220-3231.

入库方式： OAI收割

来源：自动化研究所

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