Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges

被引:197
作者
Xing, Yang [1 ,2 ]
Lv, Chen [1 ]
Wang, Huaji [3 ]
Wang, Hong [4 ]
Ai, Yunfeng [5 ]
Cao, Dongpu [4 ]
Velenis, Efstathios [6 ]
Wang, Fei-Yue [7 ]
机构
[1] Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore
[2] Vehicle Intelligence Pioneers Ltd, Qingdao 26600, Shandong, Peoples R China
[3] AVL Powertrain UK Ltd, Coventry CV4 7EZ, W Midlands, England
[4] Univ Waterloo, Mech & Mechatron Engn, Waterloo, ON N2L 3G1, Canada
[5] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
[6] Cranfield Univ, Adv Vehicle Engn Ctr, Cranfield MK43 0AL, Beds, England
[7] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
基金
中国国家自然科学基金;
关键词
Intelligent vehicle; ADAS; lane change; driver intention; parallel driving; BAYESIAN NETWORKS; COMPUTER VISION; ASSISTANCE; BEHAVIOR; RECOGNITION; PREDICTION; MODEL; MOVEMENT; CONTEXT; SYSTEM;
D O I
10.1109/TVT.2019.2903299
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context, as well as the driver status since ADAS share the vehicle control authorities with the human driver. This paper provides an overview of the ego-vehicle driver intention inference (DII), which mainly focuses on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consist of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted.
引用
收藏
页码:4377 / 4390
页数:14
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