智能网联车辆混行交通流中灯语意图识别模型研究

交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (5): 36-44.

所属专题：自动驾驶技术

• 智能交通系统与信息技术 • 上一篇下一篇

智能网联车辆混行交通流中灯语意图识别模型研究

梁军^*1，钱晨阳¹，陈龙¹，王文飒¹，赵彤阳²

1. 江苏大学汽车与交通工程学院，江苏镇江212013； 2. 中国重型汽车集团有限公司汽车研究总院，济南 225000

收稿日期:2020-05-07 修回日期:2020-06-29 出版日期:2020-10-25 发布日期:2020-10-26
作者简介:梁军(1976-)，男，江苏扬州人，教授.
基金资助:
国家重点研发计划/National Key Research and Development Program of China(2018YFB1600503)；江苏省高等学校自然科学研究重大项目/ Major Projects of Natural Science Research in Colleges and Universities of Jiangsu Province (18KJA580002).

Vehicle Headlights Intention Recognition Model for Connected and Automated Vehicles in Mixed Traffic Flow

LIANG Jun¹, QIAN Chen-yang¹, CHEN Long¹, WANG Wen-sa¹, ZHAO Tong-yang²

1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, Jiangsu, China; 2. China National Heavy Duty Truck Group Research Institute, Jinan 225000, China

Received:2020-05-07 Revised:2020-06-29 Online:2020-10-25 Published:2020-10-26

摘要/Abstract

摘要：

为使混行交通流下智能网联车辆(Connected and Automated Vehicles, CAV)实现对人工驾驶车辆(Human-driven Vehicle, HV)前照灯灯语意图(Vehicle Headlights Intention, VHI) 的识别，弥补车对车(Vehicle to Vehicle, V2V)和鸣笛意图识别技术的不足，更好地与HV交互沟通，提出CAV对HV的VHI识别模型.模型包括：灯光感知、光数据处理、VHI识别3个模块，灯光感知模块通过RGB(Red-Green-Blue, RGB)和HSV(Hue-Saturation-Value, HSV)颜色空间感知前照灯(Vehicle Headlights, VH)，采用KLT(Kanade-Lucas-Tomasi Tracking，KLT)和车辆匹配算法定位跟踪发出灯语的HV；光数据处理模块采用光通道增益算法计算光辐射通量变化； VHI识别模块基于双层隐马尔可夫模型(Double-layer Hidden Markov Model，DHMM)辨识VH 闪烁次数和HV行驶状态，实现VHI识别.在3种灯语示意典型场景下的实验结果表明：1 s内 VH感知准确率为96.8%，定位跟踪精度小于1°，VHI识别率为96.6%，满足混行交通环境下 CAV对HV驾驶意图的识别要求，基本保证实时性，为混行交通流中CAV自动驾驶决策提供理论依据.

关键词: 智能交通, 灯语意图识别, 识别模型, 智能网联车辆, 混行交通流

Abstract:

This study proposes a Vehicle Headlights Intention (VHI) recognition model to improve the communication between the Connected and Automated Vehicles (CAV) and Human- driven Vehicles (HV). The VHI recognition model is consist of light perception module, optical data processing module, and VHI recognition module. The light perception module is able to locate and track the HV that sent a light signal through the RedGreen-Blue (RGB) and Hue-Saturation-Value (HSV) color space, the Kanade-Lucas-Tomasi Tracking (KLT), and the vehicle matching algorithm. The optical data processing module calculates the optical radiant flux using optical channel gain algorithm. The VHI recognition module identifies the number of headlight flashing and vehicle driving status based on the Double- layer Hidden Markov Model(DHMM). The experimental results from three typical VHI scenarios indicate that the average accuracy of vehicle headlights perception is 96.8%, and the error of positioning and tracking is within 1 degree. The 1-second VHI recognition rate reaches 96.6%, which enables the driving intention recognition of CAVs and provides the basis for the automated driving decision of the CAV in mixed traffic flow.

Key words: intelligent transportation, vehicle headlights intention(VHI), recognition model, connected and automated vehicle (CAV), mixed traffic flow

中图分类号:

U491

梁军，钱晨阳，陈龙，王文飒，赵彤阳. 智能网联车辆混行交通流中灯语意图识别模型研究[J]. 交通运输系统工程与信息, 2020, 20(5): 36-44.

LIANG Jun, QIAN Chen-yang, CHEN Long, WANG Wen-sa, ZHAO Tong-yang. Vehicle Headlights Intention Recognition Model for Connected and Automated Vehicles in Mixed Traffic Flow[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(5): 36-44.

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智能网联车辆混行交通流中灯语意图识别模型研究

Vehicle Headlights Intention Recognition Model for Connected and Automated Vehicles in Mixed Traffic Flow

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