Honda’s Initiative on Connected and Automated Driving
Honda R&D Co., Ltd.
Automobile R&D Center
Yoichi Sugimoto
2
Cities
Changes
Future
mobility
needs
・ Increasing congestion with increasing population
・ Lack of parking space
・ Further development of public transport modalities
・ “Super-aging” of the population,
population decline・ Scrapping of public transport
Daily commutes to work or a hospital, shopping, and travels on days off
Door-to-door personal space
Depopulated
regions
・ Physical dispersion of facilities
・ Inadequate public transport
Suburbs
Regional areas
Revolution in mobility through automated driving is expected
3
Creating freedom
of time and space,
make travels enjoyable
Provide freedom of mobility
for everyone
whenever s/he needs it
Realize
collision-free society
(zero human error)
Collision-free Society with the Joy & Freedom of Mobility for Everyone
4
Sense of Confidence and Trust
• Neither approach nor create risky situations
• No anxiety for either a driver or other road users
High Level of Ride Comfort
• Smooth and natural driving characteristics
• Comfortable trips with fun ride
Provide a driver with complete confidence,
prompting the urge to get out on the road
5
2000 2010 2020
Collision-free Society Joy & Freedom of Mobility
YEAR
CMBS
Traffic Jam Assist
●Automated Highway Driving
LKAS/ACC
i-ACC
Workload Reduction
● Automated Surface Street Driving
●Level 4 Automated Driving
Technolo
gic
al evolu
tion
Injury Mitigation
Recognition Support
Collision Avoidance
Narrow offset
Pop-up hoodACE body
Pedestrian protection
City-Brake Active System
ACC with Low-Speed Follow
Pedestrian Collision Mitigation Steering
LaneWatchMulti-view Camera System
Traffic Sign Recognition
Road Departure Mitigation
Night Vision
V2V / V2I communication
Omni-directionalOblique
All-weather
6
Predict and mitigate collisionsDetect vehicle in front using millimeter-wave radar sent from transmitter in front grill
Primary warning Secondary warning Damage mitigation
Encourage drivers to take evasive action Driving assistance, collision mitigation
WarningBrake
WarningBrake
Weak Weak WarningBrake
Strong Strong
2003: World first
7
Responds to high speed
accident
Responds to diverse accident scenarios
Camera Image
recognition
Horizontal control
system function
Integration of sensor
data
Vertical control system
function
Monocular
color camera
77 GHz Millimeter-wave
radar
CameraRecognition of
target object
attribute and
size
RadarRecognition of
target object
position and
speed
Driving Support not only for daily driving but also for collision avoidance
8
Collision Avoidance
Collision Mitigation Braking System (CMBS)
For pedestriansFor preceding vehicles
! Oops
LKAS(Lane Keeping Assist System)
Lead Car Departure Notification System Traffic Sign RecognitionAdaptive Cruise Control (ACC)
with Low-Speed Following
Start
ping
Following
Assists a driver
to keep its lane
Detect departureOf proceeding car
Preventive Safety
© Honda R&D Co., Ltd. All rights reserved.
World’s first Japan's first
Road Departure Mitigation (RDM)
Pedestrian Collision Mitigation Steering
False Start Prevention Function
9
Map ECU +
High-Definition map
TCU(Telecommunications Unit)Backend server
Driver Monitor Camera
Monitoring of driver’s
face direction
Center display (NAVI)Grip sensor
Steering torque detection
Full-LCD meter
Coordinate matching
Lane marking correction
Distance and velocity of obstacles
Selection of optimum
target trajectory
Main-ECU
Head-up display
Steering wheel Indicators
<Stop>Redundancy of braking
<Power supply>Addition of DC/DC power source + 2nd battery
Multi-GNSS ANT
LiDAR×5 Sub-ECU2
Radar×5 Sub-ECU1
Camera
Camera
Radar Fusion
LiDAR Fusion
<Turn>Redundancy of EPS
10
Automated driving
within a lane
Automated pilot
in traffic congestionMerging assistance
Driving
scenarios
Surface street
~GateMerging
Main line
Branching
Request from HMI for transition
of driving task
Lane keeping Traffic jam following Automated lane changing
Relaxed posture
(Hands-free)
Relaxed posture
(Hands-free)
Viewing and operating
TV, etc.Reduced steering wheel
operation
Benefits for
users
Towarddestination
Automated drivingin multiple lanes (serial lane changing towards destination)
11
■Scene understanding and risk prediction●Functions required
for automated driving
1. Keep away from any risk
2. No anxiety to other road users
3. Drive smoothlySubject
vehicle
Rsk
Scene understanding and risk prediction are essential
for automated driving on complex surface streets
Recognition / Scene Understanding / Prediction
Identification of
attributes
Scan a scene
Scene
Understanding
Understand a situation
Risk prediction
Predict future actions
・Oncoming vehicle
will avoid a parent
and a child・Child might jump out
Application of AI technology
Action Plan
Decision on
behavior
Decide how to behave
Perception
of relationships
Recognize each location
・A child shaking a hand
・Standing on a sidewalk
・Looking at the opposite
sidewalk
Trajectory
generation
Generate low risk trajectory
ActionVisual
information
■Scope of AI technology application
12
Detection of objects’
attributes / distance
using Deep Learning
is dramatically improving
performance in the areas of:
AI technology is improving recognition performance remarkably,
whether during daytime or nighttime
■Detection of drivable space
■Vehicle detection
■Pedestrian detection
■Nighttime
Detection of stop positions at intersections
Pedestrian detection Nighttime
Drivable space detection
13
Support fuel-efficient, safe, and smooth driving using traffic signal information from advanced IR light beacons
■Configuration of on-board system
Traffic Control Center
Pass throughtraffic lights
Red light deceleration
Delayed start prevention
■Effectiveness
• Advanced IR light beacon receiver
• Meter display HMI
Show time to green light
Shows when to release the gas pedal
Shows right speed to pass
Strong acceleration / declaration reduced
World’s 1st
※as V2I by IR light beacon
V2I Infrastructure(IR beacon)
Acceleration
Distribution
Introduced in May, 2016
ACCORD
14
Conducted with the approval of the Honda R&D Co. Bioethics Committee
Drivers
Vehicles used
Test period
Driving time period
Method of driving
During support
Test drivers
(system developers)
FIT 1.5-L gasoline
vehicle
Without support 5 days
With support 5 days
07:00 to 21:00
Drive according to supporting
recommendations
66 commuters
(non system developers)
Test subjects’ commuter
vehicles
Without support 4 weeks
With support 4 weeks
Test subjects’ commuting
times, mainly
Free driving
Micro AVENUE
i-Transport Lab. Co., Ltd.
1 day Recreates traffic volume
on July 22, 2014
06:00 to 22:00
Drive according to supporting
recommendations
Evaluated the potential
effectiveness
Evaluated the effectiveness
in terms of actual use
Evaluated the influence
on overall traffic flow
Step 1 Step 2 Simulation
15
Traffic volume by time periodOct. 7-9, 2014 (same as at time of road traffic census)
Assumed fuel economyCongested : Step 2 (total)
Non-congested : Step 2 (daytime)
Estimate effect from total fuel consumption
for all vehicles driving over a 24-hour
0 2 4 6 8 10 12 14 16 18 20 22 (Hour)0
100
200
Am
oun
to
f d
ata
(Ste
p 2
)
300
Commuter congestion period
Daytime
Ra
te o
f im
pro
ve
me
nt [%
]
Step 1
Eastbound Westbound0
4
8
12
11.39.9
Eastbound WestboundEastbound Westbound
Step 2
0.42.2
Step 2(Daytime)
8.27.3
Effectiveness in terms of actual usability
Eastbound Westbound
4.5 5
Potential effectivenessEstimation
(over 24-hour period)
The effect is limitedsince much driving is done during congested time periods
16
Simulation scene
14
12
10
8
6
4
2
00 20 40 60 80 100
All vehicles
Without support
With support
Penetration rate of Vehicles with TSPS [%]
Ave
rage
of fu
el e
co
no
my
Imp
rove
me
nt [%
]
Even with low penetration rate of
supported vehicle,
fuel economy also improves for
vehicles without support
Approximately 13%
fuel economy improvement effect
17
During the Great East Japan Earthquake, information was provided on "what roads are usable now" obtained from vehicles
equipped with Internavi that had actually used those roads①
When earthquakes of a lower 6 or higher intensity, local severe rain, or other such disaster occurs, information on it is distributed
on web maps and car navigation systems②
Information is also provided for the actual road use data collected and organized by ITS Japan(Information provided by Honda Motor Co., Ltd., Pioneer Electronic Corporation, Toyota Motor Corporation, Nissan Motor Co., Ltd., Fujitsu Limited, Isuzu Motors Limited, and UD Trucks)
③
Information on actual road travel conditions
made public the morning after the Great East Japan Earthquake From the ITS Japan website
Contribute to disaster recovery and driver safety using floating car data
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Make optimal movement synchronizing with surroundings to avoid unnecessary disorder
in traffic flow and create smoother traffic environment not only for oneself but also for
every other driversSafe Swarm
Like a school of fish
Evolution by autonomous sensing
2002
LKAS
2003
CMBS
2014
Honda SENSING
Highway
Automated Drive
Predictable Info(by Telematics)Visible Risk
(by Onboard Sensors)
Invisible Risk(by V2X)
Accident
Create cooperative safety integrating autonomous sensing and V2X
Synchroniz
e speeds
Earlier
deceleration
Earlier
lane-change
Safe Merge Phantom Traffic Jam Prevention Hazard Prediction
19
Correlation analysis and analysis of common elements
Association analysis and decision tree analysis
Big data analysis
Anthropomorphism and agency
HMI and robotics
20
A society in which multiple types of enterprises connect, collaborate, compete, and co-create
Infrastructure cooperation
Smart city
Use-linked insurance
Home appliance
collaborationPedestrian-vehicle
communication
Retailer collaboration
Mobility
Smart gridEcosystem
creation
Public and
private sector
People and cars
Collaboration by
different types
of business
Cars and towns
Security DSRC
5G
AI
Dynamic mapping
Automated driving
Driving support
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