结合行业场景的实践应用,是当前人工智能技术普遍面临的问题。面向安防应用场景,研究无人驾驶技术的实践落地——智能网联巡逻车。感知系统集成了视觉、雷达、惯性导航等多种传感器,利用人工智能、数据融合等关键技术实现对平台本体、道路环境及目标行为的准确识别。将人类经验(human intelligence, HI)的场景认知和人工智能(artificial intelligence,AI)的计算认知相结合,构建面向安防场景的混合智能认知系统。基于“人在回路”系统架构,依托5G通信、V2X(vehicle to everything)、边缘计算等技术,将自动驾驶和指挥调度融合,设计了人、机器、环境之间等多种交互模式,保证了系统安全、可靠、稳定运行,大幅提高工作效率。
Abstract
Practical applications combined with industry scene is a common problem in the present artificial intelligence technology.Facing the security application scenario
the practice landing of unmanned technology -- intelligent connected patrol vehicle was studied.Integrating different sensors such as camera
LiDAR
inertial navigation system (INS)
etc
the awareness system can accurately identify the platform itself
road conditions and target behavior using key technology such as artificial intelligence (AI)
data fusion.Combining human intelligence (HI) in scene cognition of human experience and artificial intelligence (AI) in cognition of computing
a hybrid intelligent cognitive system using to security scene was built.Based on human in the loop system architecture and technology such as 5G
vehicle to everything (V2X)
edge computing
etc
different interactions between human
machine and environment were designed
which ensured the safe and reliable operation of the system and increased the work efficiency by a wide margin.
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references
FULTON J , JOANNE P . DARPA grand challenge a pioneering event for autonomous robotic ground vehicles [J ] . Industrial Robot , 2004 , 31 ( 5 ):414.
PATTERSON D A . Robots in the desert:a research parable for our times [J ] . Communications of ACM , 2005 , 48 ( 12 ):31.
MONTEMERLO M , BECKER J , BHAT S , et al . Junior:the Stanford entry in the urban challenge [J ] . Journal of Field Robotics , 2008 , 25 ( 9 ): 569 - 597 .
HINTON G E , OSINDERO S , TEH Y W . A fast learning algorithm for deep belief nets [J ] . Neural Computation , 2006 , 18 ( 7 ): 1527 - 1554 .
SAE International.Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles [S ] . Warrendale:SAE International , 2018 .
FAN X M , FAN J J , TIAN F , et al . Human-computer interaction and artificial intelligence:from competition to integration [J ] . Science China Information Sciences , 2019 ( 49 ): 361 - 368 .
HU Y F , QU T , LIU J , et al . Human-machine cooperative control of intelligent vehicle:recent developments and future perspectives [J ] . ACTA Automatica Sinica , 2019 , 45 ( 7 ): 1261 - 1280 .
3GPP.Technical specification group services and system aspects; service requirements for the 5G system,stage1 (Release 16):3GPP TS22.261 V16.4.0 [S ] . 2018 .
SHI W S , ZHANG X Z , WANG Y F , et al . Edge computing:state-of-the-art and future directions [J ] . Journal of Computing Research and Development , 2019 , 56 ( 1 ): 69 - 89 .
MA H Y , XIAO Z Y , BU Z G , et al . 5G edge computing technology and application prospects [J ] . Telecommunications Science , 2019 , 35 ( 6 ): 114 - 123 .
ETSI . Mobile edge computing (MEC) service scenarios [S ] . 2016 .
3GPP.Study on enhancement of 3GPP support for 5G V2X services:TR22.886,v.15.1.0 [S ] . 2017 .
CHEN S Z , HU J L , SHI Y , et al . Technology,standards and applications of LTE-V2X for vehicular networks [J ] . Telecommunications Science , 2018 , 34 ( 4 ): 1 - 11 .