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IWPC Webinars & Q1 2021 “Virtual” Workshops
May 19: V-WORKSHOP: In Search of Optimum Automotive Sensors July 15: V-WORKSHOP: Exploring the 6G Vision
May 26: 3.5 GHz Beam Steering Antenna Design and Test Results July 21: Improving the Integrity of Public Safety and Private Network
Wireless Connectivity through Disruptive Technology
June 9: V-WORKSHOP: End-to-End Network Slicing
July 28: 5G Millimeter Wave: A Paradigm Shift in System Engineering
June 16: iNEMI: 5G Materials Characterization Challenges and DPD Implementation and Customer Value
Opportunities
Aug 11: V-WORKSHOP: CBRS and Private Networks
June 23: V-WORKSHOP: 5G Orchestration and Automation
June 30: How Can Network Cabling Protect Mission-Critical Public Aug 18: Building The 5G Network Of The Future
Safety Communications?
The Hype-Free Global Wireless Community ™
Welcome! The Hype-Free Global Wireless Community ™
Intelligent Edge Processing To Achieve Better
Radar-Fed AI-Models
Speaker
Geert-Jan van Nunen
Chief Commercial Officer IWPC
Teraki The Hype-Free Global Wireless Community ™ Members Only
INTELLIGENT EDGE PROCESSING TO ACHIEVE BETTER RADAR-FED AI-MODELS
MAY 2021
Automotive RADAR market – set for strong growth
400M
Drivers:
7x
• Complementary to
AUTOMOTIVE 223M camera
4x • Lidars too expensive
RADAR RADAR units 40 M L4+ cars
• Tesla
Growth projection 55M • Radars getting more
5.5 M
Automotive Radar – Applications
L4 cars typically have:
The L4 car o 4-6 normal Radars
o 1 long range Radar
L2+ Radar applications
Adaptive Cruise Autonomous
Control (ACC) Emergency Braking
(AEB)
Blind Spot Forward Collision
Detection (BSD) Warning System
Intelligent Park ADAS and L4
Short range RADAR Long range RADAR Assist Applications
RADAR has a strong role to play in the development of safety systems and autonomous driving
Higher resolutions Why and what new challenges come with it?
Need for high-resolution Radar & ML: detect, localize & classify
Low Angular Resolution High Angular Resolution
4Rx/2Tx Antenna Array 32Rx/2Tx Antenna Array
Why higher resolution Radar?
1. Enhance the sensing capability of
automotive radar in dense and
complicated urban environments.
2. Provides high resolution in terms
of number of point cloud per
frame for better object detection,
localization and classification.
3. This enables high performance
perception and sensor fusion
models required for AV level L4+.
For safe L2-models more accurate information is needed. This can only be delivered by higher resolution.
Zoomed-in
For safe L2-models more accurate information is needed. This can only be delivered by higher resolution. High-res radar comes with severe data-challenges
- Application latency
High-res radars are very data intensive: factor 50 to 100 more *).
Leading to severe challenges in: - Hardware costs
- Power consumption
*) based on configuration of previous slide. But can go up to factor 1.000 when comparing older radar with imaging radar
Data cube in Range-Doppler domain of modern high-res radars easily
exceeds hundreds of thousands of points. The entire 4D-cube can be
up to millions of points.
Bandwidth bottleneck for data transmission and application latency.
Good data reduction strategies are necessary to address the data
size challenges (costs, latency, power).
Teraki intelligent pre-processing overcomes the high-res radar data size problemData reduction ML-based
How to reduce data without losing accuracy?
Radar processing
scheme Digitalization Application & decisions
Raw signal Pre-processing & early fusion
FFT FFT
range doppler
Raw ADC Teraki Pre-
Range Profile Range Doppler
3D data cube processing
Teraki pre-processing algorithm:
Target Detection
(CFAR)
- Learns the distribution of the clutter and
background noise in range doppler maps.
- Reduces/suppresses noise components significantly
Direction of Arrival
stronger than those of targets of interest. (DoA)
Introducing Teraki in the conventional FMCW Radar Signal Processing Pipeline SNR improvement with Teraki pre-processing
1.5 ms
SNR gain by applying Teraki pre-processing:
➢ 31% SNR improvement on target
➢ at 90% reduction
31%
Range-doppler map before and after processing
Teraki computes signal's power for target identification (car)
➢ Pre-processing time of 1.5ms for 32bit data points
Teraki software improves Signal to Noise ratio by 31% in pre-processing time of 1.5 ms for 32-bit data points Radar target noise removal
Object Object
Signal strength (dBFS)
Unprocessed signals have more noise around the target making it difficult to identify object
Teraki intelligent pre-processing reduces the noise while preserving the amplitude of the object
Keeps or improves the target-to-noise level while compressing the radar data cube
Teraki extracts the maximum of information and allowing to use low-powered hardwareRadar Detection ROI-based
Sensor Fusion – Radar ROI detector
Step 1: Fast ROI detection
- Lightweight, unsupervised ROI detection to get some coarse ROIs, with high
false-positive rate.
Step 2: Refined ROIs using ML:
- 3D features are computed for each proposed ROI, from the 3D radar cube.
- ROIs are classified as true positives or false positives, to get the TRUE ROIs
coming from road users (pedestrians, cars, etc.) → increasing the accuracy.
- The true ROIs can then be classified as pedestrian, car, static object, etc. Teraki ROI detector accuracy – radar only
Step 1.
Lightweight (unsupervised) ROI detection:
0.56 F1 score - radar only
Step 2:
Refined (ML-based) ROI detection step:
→ 0.83 F1 score *) - radar only
*) Note: Done on limited training. With more
training F1-scores will improve.
ROIs correspond to road users, i.e. cars, trucks or
pedestrians.
Teraki refined ROI detection on radar-only data has an F1 score of 0.83Sensor Fusion On low-powered hardware
Independent ROI-detection using two different sensor types
Camera strengths: Radar strengths:
Classification, detect visual texture. Robust to bad weather conditions,
accurate distance.
Image-based object detector:
• Accurate bounding box Radar-based object detector:
• Accurate object class • Range information
• Velocity information
• Additional 3D features
(intensity, cross section,
object shape, variance etc.)
Teraki "ROI" data strategy applied on two different signal types Teraki hybrid sensor fusion processed on a single core
Object azimuth, range,
velocity, class,
additional features
Raw radar data Lightweight ROI
ROI classification Radar object
(3D cube) detection
Radar object detector
Azimuth x range x velocity
Sensor fusion
Fused object
Aurix (TC4).
TM
1 ARM core (R52, A72).
3D spatial coordinates,
velocity, object class,
object confidence
Raw camera data Joint detection / classification Camera object
Image object detector Object class, pixel
coordinates, confidence
level, additional features
Hybrid sensor fusion approach improves the accuracy and can be processed on a single ARM-core / Aurix TM. ML-based object detection & sensor fusion
Fusion step:
Radar objects are projected on the image, and image objects and radar objects are matched.
Example with 2 cars, and 3 pedestrians walking side-by-side.
Teraki integrates detections from Radar and Camera together to achieve best accuracy of detections Radar and Camera Sensor Fusion in action - city
The radar correctly detects an object behind the trees The camera later correctly identifies the class of that
(early detection). object ("car"). Radar and Camera Sensor Fusion in action - highway
Blue= cars
Green = trucks
Highway scenario. Different environment - higher speed. All road users can be identified, along with their
All objects are detected and classified. corresponding object class, range from the ego vehicle,
and radial velocity.Latency Low for real-time applications
Sensor fusion latency
CAR CLOUD
TOI Inference ROI Inference Encoding Transmission Sensor Fusion Unit Decoding
+
Camera 1920x1024 2D - 5 12.9 Up to 40x with ROI Lidar/camera: 8
ARM A72/Single Core
- Radar/Camera:
Radar 128x128x12 3D - 10.5 10x without ROI
ARM R52/Single Core 3.2
Latencies in ms Latencies in ms
Camera + Radar + IMU data
Reference performances for SoC 3D: ARM A72 1 core CPU Raw vs Reduced:
2D: Nvidia Jetson Nano GPU 2D Camera: 40x faster transmission (10x Codec, 4 x ROI)
Radar: 2Ghz Intel CPU, 1 Core 3D RADAR: 10x faster transmission
PROCESS
RAW Radar Data ROI Inference (RADAR) ROI Encoding (2D)
Teraki accelerating the new L2+ functions with high precision, low latency on series production hardware.Benefits Summary
Value proposition: accurate sensor fusion on production hardware
With With
More accurate machine learning 10x more efficient CPU utilization
Customers can continuously train and Up to 10X more efficient utilization of
update pre-processing and their models in available computing power resources
the drone. SNR is improved with 31% and additional to AI-chip optimization. Up to 6X
L2+ AI-model accuracies are increased more efficient RAM utilisation on top of AI
with 20-30%. chip optimization.
With With
Lower costs Quicker applications
Production-grade hardware can do the job. Customers L2+ models achieve lower
Hereby customers save on hardware costs latencies. 60% of application latency is data
(chipsets, bandwidth) when designing pre-processing and 40% is on ML. Teraki
production cars and lower the production reduces this 60% with factor 10X.
BoM.
Improving accuracy of radar-driven AI-models at lower latencies on low-powered hardware.Thank you For more details contact us at: info@teraki.com
Q&A For more details contact us at: info@teraki.com
IWPC Webinars & Q1 2021 “Virtual” Workshops
May 19: V-WORKSHOP: In Search of Optimum Automotive Sensors July 15: V-WORKSHOP: Exploring the 6G Vision
May 26: 3.5 GHz Beam Steering Antenna Design and Test Results July 21: Improving the Integrity of Public Safety and Private Network
Wireless Connectivity through Disruptive Technology
June 9: V-WORKSHOP: End-to-End Network Slicing
July 28: 5G Millimeter Wave: A Paradigm Shift in System Engineering
June 16: iNEMI: 5G Materials Characterization Challenges and DPD Implementation and Customer Value
Opportunities
Aug 11: V-WORKSHOP: CBRS and Private Networks
June 23: V-WORKSHOP: 5G Orchestration and Automation
June 30: How Can Network Cabling Protect Mission-Critical Public Aug 18: Building The 5G Network Of The Future
Safety Communications?
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