Virtual Target-Based Longitudinal Motion Planning of Autonomous Vehicles at Urban Intersections: Determining Control Inputs of Acceleration With Human Driving Characteristic-Based Constraints

作     者：Yoo, Jinsoo Michael Jeong, Yonghwan Yi, Kyongsu 

作者机构：Seoul Natl Univ Mech Engn Seoul 08826 South Korea Seoul Natl Univ Sci & Technol Dept Mech & Automot Engn Seoul 01811 South Korea Seoul Natl Univ Sch Mech Engn Seoul 08826 South Korea 

出 版 物：《IEEE VEHICULAR TECHNOLOGY MAGAZINE》 (IEEE车载技术杂志)

年 卷 期：2021年第16卷第3期

页      面：38-46页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0823[工学-交通运输工程] 

基　　金：Technology Innovation Program (Development and Evaluation of Automated Driving Systems for Motorways and City Roads) - Korean Ministry of Trade, Industry, and Energy, South Korea Ministry of Land, Infrastructure, and Transport through the Connected and Automated Public Transport Innovation (National Research and Development Project) [18TLRP-B146733-01] Ministry of Land, Infrastructure, and Transport through Research on Safety and Infrastructure of Connected Automated Driving at the Urban Road (National Research and Development Project) [19PQOW-B152473-01] Institute of Advanced Machinery and Design, Seoul National University (SNU-IAMD), South Korea National Research Foundation (NRF) of Korea - Ministry of Science, ICT, and Future Planning (MSIP) [NRF-2016R1E1A1A01943543] Institute of Engineering Research, Seoul National University, South Korea 

主　　题：Road traffic Intelligent sensors Autonomous vehicles Vehicle safety Hidden Markov models Predictive models Laser radar Predictive control Predictive algorithms Information filters 

摘      要：This article describes the development and implementation of virtual target-based longitudinal motion planning of autonomous vehicles at urban intersections ensuring safety and ride comfort. In this study, virtual targets are designed to cope with oncoming vehicles in the blind zone at the intersection for safety. The true field of view (FOV) of cognitive sensors and the virtual target states are constructed based on the sensor specification and intersection road information from a high-definition (HD) map. The future states and intention of sensor-detected targets are inferred and predicted with an interacting multiple model (IMM) filter. The local IMM filters are employed with an intelligent driver model (IDM). Based on predicted target states, two driving modes-cross and stop-under three different intersection stages-approach, intersection in, and intersection out-are determined. The model predictive control (MPC) is formulated to determine the control inputs of acceleration with human driving characteristics-based constraints. The proposed algorithm is evaluated through simulation to indicate the effectiveness of the virtual target. The suggested motion planning has been implemented on an autonomous driving vehicle and tested on urban roads.