图像识别与分类技术在ADAS中的应用

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Application of Image Recognition and Identification

in ADAS

2013.06.19

PLK Technologies

Company Brief

PLK Technologies

History

•PLK started as a venture TFT in Hyundai Motor Company (2000)•PLK Technologies span off in July 2003

•Developed and successfully launched ADAS vision products

–First LDWS for commercial vehicles in Korea (HMC Trago)–First LDWS for passenger vehicles in Korea (HMC Equus)–First LDWS+HBA in Korea (KMC K9)

Status

•42 Employees (20 Developers/Engineers)

•Products for 12 vehicles (passenger car, bus and trucks)•Verified in many regions

–Korea, China, Japan, Europe, US, Middle East, Australia •

TS16949, ISO9001, HKMC SQ

OEM ADAS Vision Products of PLK

Factory

Production(Test) Facility

Development History

Early Development History

Available OEM Products

Early Development History

•Developed prototypes and product in the early years of PLK

Available OEM Products

•Recent Products available for OEM application

FCW

HBA

LDW

MIPS

FPS

Resolution

Sensor CPU FCW

HBA

LDW

1st Passenger Car Model

(EQUUS)

MIPS

800

FPS

15

Resolution

640 * 480

Sensor MT9V125CPU BF5392009

FCW

HBA LDW

Dual Function

MIPS

FPS Resolution

Sensor CPU 2012

Target Schedule

•Development : Dec. 2012•Production : Oct. 2013

LDWS only

LDWS HBA

LDWS HBA FCW

Functions

Status & Roadmap

Lane Recognition

Light Recognition

Vehicle Recognition

Traffic Sign Recognition

Traffic Light Recognition Pedestrian Recognition

ADAS Vision Functions

Traffic Sign

Front Vehicle

Pedestrian

Lines

Regulation/Market Requirements

ADAS vision functionalities

Implemented in single ADAS camera

PLK ADAS Vision platform

Speed Assist System

AEB City

AEB Interurban

AEB Pedestrian

LDWS/LKAS

HBA/DHB

Lights

Euro NCAP

Recommendation

•Regulation and Market needs drives ADAS vision function requirements

•(LED) Traffic Signs, Road Lines and Lights are only detectable by image recognition •Objects (Vehicles and Pedestrians) are detectable by image recognition and other sensors

–the sensors have pros and cons

–sensor fusion, if well implemented, can produce more reliable sensor system

–image recognition is the better way to ‘identify’ an object, detected by any collaborative sensor

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