Frequent presentation materials used by me to talk about AI revolution and XPeng Motors' strategy.

1. Self-driving: AI evolution
and Massive deployment in
production cars
Junli GU Vice President of Autonomous Driving
Xmotors.ai

2. Our Mission:
Transform people’s driving experiences
Aimed at young Internet users in China, and
with automatic driving and intelligent network
connections as the core difference,.
Founded in 2014 in Guangzhou, China
Massive production and goes to market, end of 2018

3. 2018
A Electric SUV
L2.5 self-driving
> >
2019
B Electric Sedan
L3 self-driving functions
2020
B Electric SUV
L3 self-driving functions
Product Roadmap

4. Beijing
R&D
Center
Zhaoqing
Factory
Zhengzhou
Factory
Shanghai
R&D center
USA
Silicon valley
R&D center
Guangzhou
Design/Manufacturing
An International Organization

5. Auto Parking

6. X-center

7. X-power
XPENG Brand Book

8. Transportation revolution and autonomous driving
XPENG MOTORS makes things happen and grow
Three pillars of autonomous driving
AI algorithm innovations
Computing engine
Personified, geographically differentiated driving
Big Data Genome
Outline

9. From IHS
Autonomous Cars on the Road by 2020
ADA=Advanced Driver Assist
Functionality
P0:Primitive
Source:IHS
Reactive Safety
Seat-belts
Airbags
Anti-Lock Brakes
Vehicle Dynamics
Traction Control
Electronic Stability Control
Passive Driver Assist
Sensor-based warning
Ride/handling control assist
1900 1995 2000 2005 2010 2015 2020 2025
P1: Analog device
P2：Digitalization
P3：Intelligence
Active Driver Assist
Proactive control systems
Collision impact mitigation
ADA System
Predictive collision avoidance
Intergrated ADA Systems
Autonmous Driving
ADAS&V2X
Semi→Full autonomy
History of
Transportation
Revolution

10. Autonomous driving strategy of XPENG
XPENG MOTORS deploys
Auto parking, highway driving
Cutting in, traffic jam pilot, etc.
L2/L3 Assistant Driving
Operation, Data, Autonomous
Mobility for service

11. e
Western Self-driving does not apply to Asia

12. Deployment
framework
for autonomous
driving products
Driving database
High-performance computing
engine in the cars
Algorithm and software
Cloud training and development
Software OTA upgrade

13. Autonomous driving is one of the most challenging applications
of AI
Autonomous driving is a highly cross-disciplinary application for complicated read world scenarios
Three pillars:
Data
as the foundation
AI algorithm
as core driver
Computing hardware
as the final enabler
AI algorithm Sensor technology
Computer vision
Big Data
Robotics
Processors

14. Breakthrough of AI algorithm will be competitive factors of key players
Limitations of sensors, volume production and sensor fusion
Computing capacity of embedded SOC (direct bottleneck in production deployment)
Vertical integration and fusion with application scenarios
Camera
Radar
Lidar
GPS
Map
Environment perception
2D 3D Temporal
Path planning
Interactions & predictions
Vehicle control
Autonomous driving pipeline
Current challenges of Autonomous driving technology

15. Data Genome for
Autonomous
Driving
Data is the foundation of AI algorithm evolution
The Big Data Genome: “long tail” and requirements
Complete (driving environment)
Balance (weather and road conditions, etc.)
Large-scale types of objects (national and geographical difference)
XPENG MOTORS has its own car fleet
which ensures the data acquisition and continuous growth

16. Create an AI evolution close-loop among Cloud,
Deployment and Data Genome
Scalable training
DeploymentData Genome
Triggered
data
collection
Improving completeness
and balance
Real-time performane; testing
and evaluation
High accuracy;
wild range of object detection

17. Sensor fusion is a big challenge
Fusion between different sensor modality
Model sharing between different cameras
Filed of view misalignment
Higher resolution
far-away objects

18. Real-time computing in embedded environment
Computing is the final enabler
Challenges
Software/hardware heterogeneous
compatibility is a necessity
Software/hardware co-evolution
High-performance inference
engine
Cloud algorithm
Hardware abstraction layer
Scalar
computing
(CPU)
Compatibility with heterogeneous platform
Compliance with reliability requirements for car standard
High-performance machine
learning inference engine
Vector
computing
(GPU)
AI
accelerator
FPGA

19. Personified
Autonomous Driving
and geographically
differentiated in China
• Personified driving is a future trend
• The “personality” of Autonomous Driving
• Self-adaptive to different drivers and driving environment
• Self-adaptive to Chinese traffic and social norm
• Driving habits reflect local culture

20. THANKS