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ADAS Target Test System for Autonomous Emergency Braking (AEB)

 

ADAS & Active Safety Solutions for the Electric Vehicle and Autonomous Vehicle Market 

 

Learn More

 

Solution: Execute Real-World AEB Tests Using LHP's ADAS Targets 

 

LHP, Inc. has entered into a letter of intent (LOI) with Hebei Pride Automotive Technology (Pri-Auto) which was established in September 2019. LHP and Pri-Auto are committed to providing complete solutions for automotive active safety/ autonomous driving closed-field evaluation. 

LHP is now offering ADAS targets for Autonomous Emergency Braking (AEB) testing. Our ADAS targets are used in high-precision real-world test environments to measure vulnerable road users, human injury, and vehicle sensor capability in vehicle crashes. 

In addition to ADAS guided soft target equipment, LHP will also be supporting autonomous crash test equipment such as:

  • Standard roadside object surrogates for road/lane departure
  • Portable and reconfigurable night lighting systems
  • Portable near-field RCS measurement system for surrogate
  • Soft 360 vehicle target 

 

Interested in ADAS Targets? Get in Touch Today
 

Designed for SAE Testing Standards' Development 

The ADAS targets were designed and accepted as the basis for the SAE Standard J3234/1_202201 “Active Safety Roadside Metal Guardrail Surrogate Recommendation.” This standard states the “design specifications/requirements for metal guardrail surrogates that represent real metal guardrails to the in-vehicle sensors and can be used for performance assessment of such in-vehicle sensing systems in real-world test scenarios/conditions. This document only includes the recommended metal guardrail surrogate characteristics for automotive cameras, LiDARs, and/or radars – SAE.Org

 

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Launch of ADAS Targets at CES

 

Our ADAS Targets Support The Following SAE Standards

Video: AEB Vulnerable Road User (VRU) Test

 

Product Demo: Autonomous truck utilizes radar, LiDAR, and micro-Doppler technology to sense human and object movement in its path, triggering the autonomous emergency braking system. 

HubSpot Video

 

ADAS Targets

  • All ADAS Targets use patented advanced “skin” technology and 3D human body models via Toyota-funded research to provide human-like RCS characteristics in 360°
  • The targets provide LiDAR reflectivity in the range of 45%-58%, meeting the requirements of SAE & ISO specifications.
  • All targets use high-precision motor control inside the shoulders and hip joints to generate realistic human-like movements with variable walking speeds from 0.3m/s - 2.5m/s.
  • The targets feature fast set-up time, requiring zero calibration as well as shorter after-crash recovery time, thus being able to withstand repeated impacts.
  • The fully automated/integrated drive system is in sync with the targets, allowing multiple modes of operation. 
 

Our ADAS Targets

Pedestrian Targets

  • Used as a reference in the development of SAE standard J3116_201706
  • Satisfies specifications of SAE J3116_201706 and Euro-NCAP
  • Supports arm and leg articulation for mimicking human walking
  • Has the same visual, automotive radar, and LiDAR characteristics of a representative human in all directions
  • Can be crashed at a speed as high as 60km/hour
  • Requires less than 2 minutes of reassembly time after a crash
  • Ideal for testing AEB systems and autonomous driving

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Bicyclist Targets

  • Used as a reference in the development of SAE standard J3157_201902
  • Satisfies specifications of SAE J3157_201902 and Euro-NCAP
  • Supports arm and leg articulation for mimicking bicycle riding
  • Has the same visual, automotive radar, and LiDAR characteristics of a representative bicyclist in all directions
  • Has the same micro-Doppler characteristics of a representative bicyclist
  • Can be crashed at a speed as high as 60km/hour
  • Requires less than 2 minutes of reassembly time after a crash
  • Ideal for testing AEB systems and autonomous driving

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Electric Bicycle Targets

  • Satisfies technical specifications in China National Standard GB/T 17761-2018
  • Size and shape are based on the statistical data of electric bicycle sales in China
  • Wheel rotation speed is synchronized with the electric bicycle motion speed
  • Has the same visual, automotive radar, and LiDAR characteristics of a representative electric bicyclist in all directions
  • Has the same micro-Doppler characteristics of a representative electric bicyclist
  • Can be crashed at a speed as high as 80km/hour
  • Requires less than 2 minutes of reassembly time after a crash
  • Ideal for testing AEB systems and autonomous driving

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Standard Roadside Object Surrogates for Road/Lane Departure Test

  • Used as a reference in the development of SAE standard J3234/1_202201
  • Object surrogates satisfy dimension specifications of corresponding roadside objects in the US, China, Japan and Europe.
  • Can be made to satisfy the dimension specifications of other countries.
  • Object Surrogates are soft and lightweight that will not damage the test vehicle at crash test
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  • Surrogates include metal guardrail (W-beam and three-wave beam), concrete divider, and concrete curb.
  • Surrogates also include grass surrogates of 18 color patterns representing grass in different regions and seasons
  • Has the same visual, automotive radar, and LiDAR characteristics of representative roadside objects in all visible directions on the road
  • Can be crashed at a speed as high as 120km/hour
  • Requires reassembly time of less than 2 minutes per meter after a crash
  • Ideal for testing AEB systems and autonomous driving

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Moped Targets

  • Satisfies technical specifications in C-NCAP 2021
  • Wheel rotation speed is synchronized with the moped speed
  • Has the same micro-Doppler characteristics of a representative moped
  • Ideal for testing AEB systems and autonomous driving

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Baby/Child Target 

  • Child Presence Detection (CPD)/ Occupant Monitoring System (OMS)
  • Supports and mimicking baby breathing and body movement  
  • Represents the size of 0-5 years children
  • Ideal for testing AEB systems and autonomous driving 

     

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