enabling technologies for autonomous...

Enabling Technologies for Autonomous Vehicles

Sanjiv Nanda, VP Technology

Qualcomm Research

August 2017

Qualcomm Research Teams in Seoul, Amsterdam, Bedminster NJ, Philadelphia and San Diego

2

3

Delivering significant economic and societal impactTotal potential economic impact of over $1 Trillion USD per year

1

Less greenhousegas emissions

More predictable,productive travel

Fewer drivingfatalities/injuries

people die each yearon the roads worldwide

2

>1.2Mgallons of fuels wasted due traffic congestion in the US

3

3.1Bof all global warming

emissions from transportation4

14%

1 Rocky Mountain Institute 2016; 2 Global Status Report on Road Safety, World Health Organization 2015; 3 Texas Transportation Institute Urban Mobility Report, 2015;

4 U.S, Environmental Protection Agency (EPA) 2014

Vision Zero: End traffic deaths and serious injuries by 2030

Save Lives Save Time Save Energy

4

Autonomous Vehicles and Mobility as a ServicePace of Innovation, Multiple forces of Disruption

Technology Innovation

(Autonomous Vehicle)

Business Model Innovation

(Mobility as a Service)

Automakers &

Tier 1’s

Vehicle

Ownership

Autonomy

Technology

Providers

Vehicle Fleet

Providers

5

Perception – Diverse Sensing Modalities

6

360◦ PerceptionOn-board Sensors – Camera, Radar and Lidar Cocoon

V2I

V2N

V2V

V2P

Radar

Computer

Vision

7

Perception Beyond the Sensory HorizonHD Maps and V2X Provide Side Information

Improved active safety

Provides 360◦ non-line-of-sight

awareness, e.g.

intersections/on-ramps,

environmental conditions

Better traffic efficiency

Allows vehicles to safely drive

closer to each other and enables

optimization of overall traffic flow

Increased situational awareness

Provides ability to gather data from

further ahead to deliver a more

predictable driving experience

V2I

V2N

V2V

V2P

Radar

Computer

Vision

8

Perception Beyond the Sensory Horizon

9

HD MapsAccess to rich, accurate up-to-date HD Maps will be key to Autonomous Driving

Qualcomm Technologies, Inc.

Semantic InformationLane attributes, direction /access restrictions, merges etc

HD Map Semantic Information Examples

Lane attributes

− Lane direction of travel

− Lane speed limit

− Lane transition status

− Lane type

Lane boundary attributes

− Lane boundary traversal

− Lane marking color

− Lane marking style

− Lane marking material

Conditions

− Access restriction condition

− Direction of travel condition

GPSS/GNSS + VIO + Map FusionSub-meter accuracy at 95th percentile

Localization information6 DOF pose and precise coordinates of signs and other landmarks

Crowdsourced creation and update of HD mapsTriangulation & Bundle adjustment

10

Precise Positioning: Lane Level Accuracy

11

Prediction and PlanningRoad World Model:

Static and Dynamic Objects,

Drivable Space,

Road Semantics

12

Data ManagementTerabytes of data per day; Peta-scale data management

Radars

Drive RouteSpec Capture

Pre- Processing

Validation

Yes

Annotation

Testing/Visualization

Discard

Training/Validation

Storage

External Dataset

SimulatedDataset

Offline Processing

Data Reduction

Data Extraction

Data Capture

(Online Triggers)

Cameras

Lidar

13

The Autonomy BrainNext Generation Compute Platform

• 100x performance compared

to smartphones◦ high throughput sensor processing;

sensor fusion; computer vision;

machine learning / deep learning;

path planning

• Within a tight power/thermal budget

• Meeting security & functional safety requirements

Algorithms: CV, ML, Fusion

Road World Model and Path Prediction

Sensors

Camera• Front-Facing

Mono/Stereo

/Trifocal

• Surround

RADAR• Short-range

• Long-range

• Phased Array

GNSS

IMU

V2X (V2V, V2I, V2P, V2N)

HD

MAP

Ultrasound

Perception

Fusion

L

I

D

A

R

Driver

Monitoring

HMI and Control

Positioning Planning

Pro

du

ct

Pla

tfo

rm

HW Architecture• High Performance Data

Handling & Compute

within Thermal

Envelope

• HW Accelerators

Infrastructure• Functional Safety

• Fault Tolerance

• Timing Synchronization

SW Architecture• Toolchains, libraries,

SDK

• RTOS

Camera

14

Sensing and Algorithms

Massive Sensing Diversity, Fusion

…

Motion Control

Fail Operational

…

Hardware

Redundancy, Reliability, Diagnostics, Self Test

…

Software & Security

Process and Audits, Certificates

…

Testing

Safety Standards, Test Specs, Emulation and Drive Tests

…

Addressed at a System Level

Functional Safety

15

WARNING

Challenges, Innovation

and Disruption Ahead

Leading to and Era of Safe, Efficient

and Green Transportation

Thank you for your kind attention!