This document discusses key trends in smart transportation, including self-driving vehicles, mobility as a service (MaaS), and edge computing. It notes that advances in sensor technologies are as important as machine intelligence in realizing smart transportation. Self-driving vehicles rely on deep neural networks and multiple integrated sensors. MaaS is expected to spread worldwide due to improvements in sensors. Edge computing is necessary to process huge amounts of data from connected vehicles in real-time, requiring standardization and common data frameworks.

1. 2019FLEX Japan / MEMS
& SENSORS FORUM
Yoshifumi Sakamoto
Engineering and Cognitive Innovation,
Global Business Services, IBM Japan
Key Trends of
Smart Transportation

2. Hi there
2
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Yoshifumi Sakamoto, Ph.D. , PMP
Engineering & Cognitive Innovation,
GBS, IBM Japan, Ltd

3. ç
Self-driving
Vehicle
MaaS
Mobility as a Service
Edge
Computing
Huge amount of
arithmetic processing
Early realization of
automatic driving
Build scalable
services and
platforms
Key Trend of
Smart
Transportation

4. Self-Driving Vehicle

5. 5
Automotive 2025: Industry without borders
Engage with consumers, embrace mobility, exploit the ecosystem

6. Intelligent, intuitive, self-enabling vehicles provide greater personalized experiences through their ability to “take care” of
their occupants, themselves and work with others
6
Self-configuring
Personalization and
customization to environment
Self-learning
Cognitively optimizing performance
to occupants and environment
Self-healing
Analytics and prognostics for
service and maintenance
Self-socializing
Vehicle social networks to assist others,
utilizing the vehicle for
ancillary tasks
Self-integrating
Secure, seamless digital
integration
Self-driving
Automated and autonomous
mobility

7. What vehicle innovations will become commonplace by
2025
In-vehicle cognitive learning
Vehicle digital
persona interchange
Within a brand – 78%
Within an automaker – 62%
Between automakers – 26%
Partially – 84%
Highly – 55%
Fully – 19%
Limited – 38%
Fully – 8%
Automated Autonomous
74%
Social networks for
vehicles
57%
7
Cognitive cars will be commonplace by 2025, but self-driving cars will not.

8. Machine Intelligence
8
Introduction Subject to be solved
CNN
Models
InferenceTraining
Sensor
Data

9. 9
DNN
Deep
Neural
Network
FCC S C S
FCC S C S
backprop
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C: Convolution layer S: Subsampling layer FC: Fully-Connected layer
Forward Propagation (Inference)
Backward Propagation (Training)
All current prototypes of self-driving
vehicle and self-driving vehicles to be
realized in the near future adopt DNN

10. 10
Technology
trend
Inference
Combination of Embedded SoC and GPGPU
-Trained several models, used for inference of real-
time object recognition.
-Those models are stored in the GPGPU.
-The data used for object recognition is video data
recorded by several cameras.
-Embedded SoC and GPGPU which execute the
motion control of car work strongly in cooperation.

11. 11
Technology
trend
Training
Combination of enterprise server and GPGPU
-The training data is mainly video data acquired by
a prototype car.
-The scale of these video data also extends to
petabytes.
-GPGPUs are installed in PCIe slot or nvlink slot of
enterprise server.
-The training model is stored in the GPGPU.

12. Photo source : Volvo,
Volvo Developing Accident-Avoiding
Self-Driving Cars for the Year 2020
Photo source : PIONEER
Pioneer 3D-LiDAR Sensors
The most important thing is to get consistent data
Image data Optical camera
Range data 3D-Lidar and millimeter wave radar
Position data GPS and odometer
Velocity, Acceleration, Attitude data IMU (Inertial Measurement Unit)
Photo source : SONY
Wide angle CCD
All depends on the
data from the sensor

13. Data Type Sensor Type FLEX MEMES
Image data Optical camera
Range data 3D-Lidar and millimeter
wave radar
Position data GPS and odometer
Velocity, Acceleration,
Attitude data
IMU (Inertial
Measurement Unit)
Sensors and applied technologies used in autonomous vehicles
ex. Technology Trend of 3D Lidar
Motor Driven MEMS Optical phased array
Photo source : Quanergy Systems, Inc.
Optical Phased Array
Photo source : Velodyne
Velodyne HDL -64E
important to be resistant to vibration

14. Era of self-driving vehicle
●Multiple sensors are integrated and miniaturized
ex.
• Optical camera and rider
• Rider and laser
• GPS and optical camera
●Communication methods for walking and
human drivers are required
ex.
• Light emission on the side of the body
• Posting information to the road surface
MicroM - Technology to integrate
Flex device - Technology to achieve high durability
Unit and Data
Unit and Data

15. MaaS
Mobility as a Service

16. 86% of people we surveyed said they will own a car sometime during
the next 10 years
61%
82%
85%
85%
85%
85%
87%
87%
88%
88%
88%
90%
91%
91%
93%
97%
4%
7%
8%
5%
6%
7%
6%
5%
4%
5%
8%
4%
6%
6%
4%
2%
35%
11%
7%
10%
9%
8%
7%
8%
8%
7%
4%
6%
3%
3%
3%
1%
Own a car
Not own a car but will actively drive
Neither
Will you own a car or be actively driving
sometime during the next 10 years?
The availability of alternative ownership models could
entice others to purchase
86%will
own a car in
the next 10
years
Mexico
Brazil
India
Italy
Thailand
Indonesia
Australia
US
China
France
Canada
Russia
UK
S. Korea
Germany
Japan
86%
86%
89%
89%
83%
5%
5%
6%
5%
8%
9%
9%
5%
6%
9%
55+
45-54
35-44
25-34
18-24
Males
78%
86%
87%
88%
84%
5%
5%
4%
4%
7%
17%
9%
9%
8%
9%
55+
45-54
35-44
25-34
18-24
Females

17. Note: %Avg is the average of the 6 categories based on
responses of “extremely/very interested” per country
Self-enabling vehicle capability
- ranked by country
Growth markets
Mature markets
Rank
Mexico
Brazil
India
Thailand
Indonesia
China
Russia
S.Korea
Italy
USA
Canada
UK
Australia
Germany
France
Japan
1
2
3
4
5
6
% Avg 77 77 76 70 67 64 64 61 49 47 47 45 43 43 34 20
Integrating
Configuring
LearningHealing
Driving Socializing
Ten of sixteen countries
listed self-healing as top
priority while Asian
countries listed self-
driving and first or
second priority

18. 18
hard to predict that ride
sharing by self-driving
vehicles will spread rapidly
Self-healing
Analytics and prognostics for
service and maintenance
Self-socializing
Vehicle social networks to assist others,
utilizing the vehicle for
ancillary tasks
MaaS
in the near future

19. https://www.bourns.com/ja/products/automotive/automotive-sensors
Self-healing
MaaS is expected to spread
worldwide due to sensor
miniaturization, price reduction
and durability improvement.
The number of sensors mounted on the luxury car still
exceeds 200. However, in a popular car only a few dozen
sensors are installed.

20. Edge Computing

21. As the 5 G network gets
used, a large amount of
data traffic occurs, so
the central server lacks
the computing power for
data processing.
Why Edge Computing?

22. large-scale "parallel & distributed & real time"
framework for connected vehicle
Automotive Edge Computing Consortium
Solve the issue by aggregating the computing power
on a local server close to the Edge device

23. Powerful computing power can be used to analyze sensor data
for that purpose,
• Standardization of sensor data format
• Build a common data processing framework
• Preparation of reference data for use in machine learning
are necessary.
Photo source : RENESAS ,
In-Vehicle Networking Solutions
Real-time
analysis Streaming
processing Data
conversion Compression
Abstraction
Security processing
• Diag data
• Image recognition data
• Voice recognition data
• Sensor Data
• Vehicle-Centric Control data
• Driving control data
• Operation information
• Dynamic Behavior of software
• Location Data
Front end feature for Big Data
CAN
LIN
Ethernet
MOST
Gateway
Edge Servers

24. 24
Conclusions Future of Smart Transportation
Self-driving
Vehicle
MaaS
Mobility as a Service
Edge
Computing
• Multiple sensors are integrated and miniaturized
• Communication methods for walking and human
drivers are required
MaaS is expected to spread worldwide due to sensor
miniaturization, price reduction and durability
improvement.
• Standardization of sensor data format
• Build a common data processing framework
• Preparation of reference data for use in machine learning
Advances in sensor
technologies are as
important as Machine
Intelligence in realizing
Smart Transportation.

25. Thank You!
Yoshifumi Sakamoto, Ph.D.
Engineering & Cognitive Innovation,
GBS, IBM Japan, Ltd