Presentazione della ricerca "Advanced Driver Assistance Control

Transcript

PROGRAMMA
Ore 10.30
Saluti di apertura
Giovanni Azzone, Rettore Politecnico di Milano
Ore 10.45
Discussione della ricerca:
“Sistemi predittivi di marcia”
Modera:
Federico Cheli, Dipartimento di Meccanica, Politecnico di Milano
Intervengono:
Maurizio Boiocchi, General Manager Technology Pirelli Tyre e Amministatore Delegato Pirelli Labs
Elisabetta Leo, Dipartimento di Meccanica, Politecnico di Milano
Ore 11.45
Tavola rotonda: “L’auto del futuro”
Modera:
Giampio Bracchi, Presidente Fondazione Politecnico di Milano
Intervengono:
Enrico Pisino, Head of Research & Innovation, Fiat Chrysler Automobiles
Franco Cimatti, Responsabile Impostazione Prodotto Ferrari
Guidalberto Guidi, Presidente Ducati Energia
Umberto Bertelè, Dipartimento di Ingegneria Gestionale, Politecnico di Milano
Marco Tronchetti Provera, Presidente e Amministratore Delegato Pirelli & C., Presidente Fondazione Silvio Tronchetti Provera
Ore 13.00
Conclusioni
Marco Tronchetti Provera, Presidente e Amministratore Delegato Pirelli & C., Presidente Fondazione Silvio Tronchetti Provera
Advanced Driver Assistance Control | Politecnico di Milano | 15 ottobre 2014
Federico Cheli
Dipartimento di Meccanica, Politecnico di Milano
PROJECT TARGET
ADAS systems improvement
via potential friction knowledge
2008….
… 2014
6 years sponsored
research project
PROJECT TARGET: STUDIED ADAS SYSTEMS
If I suppose to know the available
potential friction which ADAS system can
be improved and how?
LONGITUDINAL
LONGITUDINAL
i.e. Autonomous Emergency Brake (AEB) could
exploit friction knowledge:
LATERAL
i.e. Curve Speed Warning (CSW) could
improve their performances:
By World Health Organization
Implications
Developing vehicles that:
- optimize the traffic flow;
- optimize fuel consumption;
- provide safe and comfortable transportation and at the same time
- have minimal impact on the environment.
Satisfy these diverse and often conflicting requirements is a great challenge.
Solution: electromechanical subsystems that employ sensors, actuators and
feedback control.
Technological advancement in these fields had an enabling role in promoting
this trend.
The ADAS systems
Many advanced driver assistance systems (ADAS) are being developed by
automotive manufacturers to automate driving operations in order to reduce
highway accidents.
Examples include:
• collision avoidance systems: which automatically detect slower moving
preceding vehicles and provide warning and brake assist to the driver;
• adaptive cruise control (ACC) systems: which are enhanced cruise control
systems and enable preceding vehicles to be followed automatically at a safe
distance;
• lane departure warning systems;
• lane keeping systems: which automate steering on straight roads;
• vision enhancement/ night vision systems
• driver condition monitoring systems: which detect and provide warning for
driver drowsiness, as well as for obstacles and pedestrians
The ADAS systems
Safety increasment
Pollution reduction
& energy saving
ADAS
Advanced Driver Assistance
Systems
Cheap sensing, actuating,
computational capabilities
Congestion
reduction
The ADAS systems
From SBD - Automotive technology consultancy and research (www.sbd.co.uk/)
The ADAS systems
By 2020 in Europe, over 70% of vehicle models sold will be fitted
with at least one type of ADAS.
EuroNCAP decided to include testing of ADAS as part of their five
star rating.
Updates in legislation in the USA from 2018 will result in a more
aggressive growth in ADAS.
ADAS is still in its infancy for China where currently the market is
dominated with ADAS fitted on EU and USA imported vehicles: E.g
Volvo
From SBD - Automotive technology consultancy and research (www.sbd.co.uk/)
Level 0 - no-automation: control of the vehicle completely entrusted to the
driver at all times. Systems that provide safety warnings
level 1 - function-specific automation: one or more specific control functions.
The driver has overall control, but can choose to cede limited authority over a
primary control
level 2 - combined function automation: automation of at least two primary
control functions designed to work together. The driver is still responsible for
monitoring the roadway and safe operation and is expected to be available for
control at all times and on short notice.
level 3 - limited self-driving automation: the driver is enabled to cede full
control under certain traffic or environmental conditions. The driver is
expected to be available for occasional control, but with sufficiently
comfortable transition time.
level 4 - full self-driving automation: the vehicle is designed
to perform all safety-critical driving functions and monitor roadway
conditions for an entire trip.
level 0 - no-automation:
lane departure warning
forward collision warning
blind spot monitoring
level 1 - function-specific automation:
Adaptive Cruise Control
Lane keeping Assist
level 2 - combined function automation:
Super Cruise Technology
level 3 - limited self-driving automation:
Platooning (SARTRE Project)
level 4 - full self-driving automation:
Google Car,
University collaboration researches
Active safety: Autonomous vehicles
One Level MPC, with Risk Evaluation, Non Linear Vehicle Model
and Safety/Comfortable Trajectory Planner
Cooperation with
Active safety: ADAS
Camera, radar, navigation and sensors in general are stimulating a growing
number of ADAS systems aimed to improve safety:
Source: BMW, IPG Automotive GmbH
Where I am,
probable or
selected route 
turns, slope, road
signs, etc…
Source: Porsche, IPG Automotive
GmbH
Traffic
conditions,
surrounding
objects
Wheels speed,
steer angle,…
Active safety: ADAS
Are these information enough to
implement a new ADAS or an
autonomous vehicle?
Or I need something else ?
Active safety: ADAS
I need something else
!!!
ONLY including potential
friction estimation the new
ADAS could provide previews of
warning situations due to actual
driving speed.
Maurizio Boiocchi
General Manager Technology Pirelli Tyre e
Amministatore Delegato Pirelli Labs
COMPANY OVERVIEW
>
One of the most innovative and profitable companies in the sector (13,4% tyre Ebit margin in 2013)
>
A strong Brand, enhanced by F1 involvement - official tyre supplier in ‘11-’16
>
A unique positioning in the industry:
•
•
•
Growing focus on Premium segment (~ 54% of car sales in 2013)
Strong exposure to the Replacement channel (75% of revenues), less exposed to Auto sector dynamics
Balanced distribution of revenues, leading the development of Premium tyres in Emerging Market
6,15 €/bln revenues in 2013
SALES BY SEGMENT
SALES BY REGION
Car
63%
Truck
20%
Central
And
South America
36%
Moto
6%
Steelcord & others
Other
European
Country
27%
11%
1
NAFTA
11%
Asia/Pacific
8%
MEAI
8%
Italy
Russia
4%
6%
VEHICLE ELECTRONICS EVOLUTION: CAR MAKERS DEMAND
>
Investments cover all the car segments for new functionalities
to maximize differenciation and fulfill customer’s demand in the
•
•
•
safety
economy &
infotainment areas
through
•
•
•
•
>
ADAS systems
Driver Information
Fuel economy: Hybrid / electrical engine
Connectivity and Infotainment
Electronics value will increase, in Europe,
from 20% of today to up 60% in 2018 of the
total cost of a premium vehicle full opt
(F&S source).
2
PIRELLI’S ANSWERS: CYBERTMTYRE
NEW VEHICLE CONTROL LOGIC
ENGINEERING FEATURE
EXTRACTION
SENSOR
3
TYRE EVOLUTION: ELECTRONICS KICKS IN
>
Pirelli New instrumented Tyres now have a DIRECT roles in Vehicle
•
•
PREVENTIVE Safety
PERFORMANCE Driving
specifically mapped in
•
•
•
•
>
ADAS systems
Fuel economy: Hybrid / electrical engine
Connectivity and Infotainment
Driver Information
Cyber Tyre dynamically provides to the Vehicle
unique informations of grip margin and actual forces
exchanged to the ground to exploit fully preventive safety
paradigms and performance driving
4
CYBER TYRE IN A NUTSHELL
TYRE DYNAMICS
• Vertical Load
• Longitudinal & Lateral Forces
• Speed margin before hydroplaning conditions
• Grip & Grip margin before sliding
• Patch Area
• Slip Ratio
• Slip Angle
• Camber
• Tyre Aligning Moment
ROAD CONDITION
• Road surface texture
TYRE CONDITION
• Tyre Temperature & Pressure
• Tyre wearing
• Number of tyre revolutions
• Tyre ID
5
NEXT GENERATION APPLICATIONS ENABLED BY CYBER TYRE
Patch Are Control for
Maximasing Cornering
Performance
Aero-Loads control
Warning
Information
(Tyre Wearing,
Aquaplanning,
Grip margin)
Active
Suspension
Control
Performance
Enhancement
New Applications
Electronic Stability
Program
Enhanced ADAS
(Adaptive Speed
Limit)
Advanced Vehicle State
Estimator (combined
Active Controls)
Traction Control,
E-Differential
6
THE ADAS MISSING LINK: CYBERTMTYRE ‘MAXIUM AVAILABLE GRIP’
CLARKE, D. ET AL. FATAL VEHICLE-OCCUPANT
COLLISIONS: AN IN-DEPTH STUDY. 2007.
ROAD SAFETY RESEARCH REPORT NO. 75.
DEPARTMENT FOR TRANSPORT, LONDON.
• 1185 fatal vehicle occupant from 1994 to 2005.
• Over 65% of the accidents examined involved driving at
excessive speed
Speeding accidents were examined, and divided into
separate types, based predominantly on the levels of risktaking by drivers.
Type 3 – ignorance of speed, for example failing to realize
that wet road conditions increase the likelihood of skidding
2006 EUROPEAN PROJECT.
FINAL REPORT
“…Along with driver behavior and alertness, friction is one
of the remaining key unknowns in the algorithms of future
ADAS (Advanced Driver Assistance Systems) that
calculate the risk of collision, or safe speed. For example, if
a Collision Mitigation System assumes high friction, it will
have poor performance on snow, since it will brake too
late..”
TITLE: Sensor Data Fusion Based Estimation of
Tyre-Road Friction to Enhance Collision Avoidance
DATE: 2010
7
Elisabetta Leo
Dipartimento di Meccanica, Politecnico di Milano
PROJECT TARGET
ADAS systems improvement
2008….
… 2014
… future development
 4 partecipations to international conferences
 4 master thesis
6 years sponsored
research project
PROJECT TARGET: STUDIED ADAS SYSTEMS
If I suppose to know the available
potential friction which ADAS
system can be improved and
how?
LONGITUDINAL
LONGITUDINAL
i.e. Collision Avoidance Warning(CAW) could
exploit friction knowledge:
LATERAL
i.e. Curve Speed Warning (CSW) could
improve their performances:
PROJECT TARGET
ONLY including potential friction
estimation the new ADAS could
provide previews of warning situations
due to actual driving speed.
The CT Friction IDentification
algorithm has been used to verify the
possibility to increase the performance
PROJECT MAIN RESULTS
Three steps has been followed to reach
the target for both CSW e CAW
• Logics
improvement
via CT potential
friction
knowledge
• Numerical
verification
• On-board tests.
LONGITUDINAL
Collision Avoidance Warning (CAW)
CAW: how it works
For instance CAW systems
work as follows:
- Phase 1: warning is
provided to the driver;
V
- Phase 2: the obstacle is
closer and braking system
is pre-charged to assure
fast reaction;
- Phase 3: braking pressure
is applied to avoid
accident.
Radar Visibility Range
V
CAW
Phase 1
(warning)
CAW
Phase 2
(pre-charge)
CAW
Phase 3
(brake)
time
Obstacle distance
d(t)
Pre-assigned
REFERENCE potential friction
μREF
Brake pre-charge
Vehicle deceleration
Relative speed
v(t)
Driver warning
a(t)
CAW: how it works
Simulations of CAW system with pre
assigned value of μREF = 0.7:
Legend:
μREF = 0.7
1 = avoided accident
ice
0 = accident occurs
wet
stones
concr.
asphalt track
CAW: how it works
Simulations of CAW system with pre
assigned value of μREF = 0.7:
Legend:
μREF = 0.7
1 = avoided accident
 check residual distance “d”
0 = accident occurs
 check impact speed “v”
CAW: how it works
μREF = 0.2
μREF = 0.4
μREF = 0.7
Residual distance [m]
Simulations about how an CAW
system will work with different
values of μREF:
μREF
-
More cases in which accident occurs.
Higher impact speeds.
BAD EFFECTIVENESS
Compromise is needed…
BAD EFFICIENCY
-
Higher residual distances.
More cases in which accident is avoided.
μREF
μREF
Real road μP
Impact speed [km/h]
μREF
Accident
avoided index
Real road μP
μREF
Legend:
μREF = 0.7
μREF = 0.4
μREF = 0.2
CAW: benefits including CT-F.ID algorithm
Exploiting the CT-F.ID algorithm the ECU is able to refresh the value of μREF
The CT Potential Friction Identification algorithm (CT-F.ID) provides road friction μ within 5
ranges;
Are such conditions
suitable to assure ADAS
improvements?
CAW: benefits including CT-F.ID algorithm
Comparison between CAW system with
fixed value of μREF = 0.7 and CAW
exploiting friction knowledge:
residual distance
Identified
-
μCT-F.ID
More cases in which accident is avoided.
Lower impact speeds (both maximum
and average).
Still short residual distances when
accident is avoided.
BETTER EFFECTIVENESS
&
GOOD EFFICIENCY
Legend:
Avoided accidents
Low impact speed
μREF = 0.7
μREF = μCT
CAW: on-board implementation
4 instrumented tyres
RADAR
INTERNAL CAMERA
Real time PC.
CAW logic has been
implemented and
integrated with CT data
CAW: on-board implementation. Example on urban concrete surface.
NOTE: expected potential friction on dry concrete
surface = 0.6 ÷ 0.9
My vehicle speed[km/h]
Relative speed [km/h]
Safe
Warning
Danger
Relative distance [m]
Out of range
Identified potential friction [-].
Time: 4 seconds
My vehicle speed: 42km/h = 11,6m/s
Relative speed : 23km/h = 6,4m/s
Relative distance : 20m
Potential friction: 0.7  Maximum
deceleration: 7m/s2
Vmy = Vmy vehicle
Vtarget = V in front vehicle
Vmy > Vtarget
CAW ALGORITHM:
-
Vmy < Vtarget
-
No object in the
radar range
DANGER
condition
SAFE condition
One target is to reduce to zero
the relative speed between the
two vehicles.
It considers an usual human time
delay before braking (i.e. 1s) 
@42km/h = 11.6m
It considers to avoid to fully brake
at the beginning, the initial
deceleration imposed is lower
than the max.
LATERAL
Curve Speed Warning (CSW)
CSW system: how it works
Operation scheme:
1. The driver sets the path
2. The CSW logic evaluates the safety
speed on the path
3. If the vehicle speed overcomes the
safety speed a signal warns the driver
Current system’s weakness
Potential friction unknown
1st possible approach: to impose usual
potential friction conditions (i.e. µ=0.7-0.8)
2nd possible approach: to impose high
Safety margin  to impose a low potential
friction
μREF
μREF
Risk: wrong safety speed
prediction on low-µ surface.
BAD effectiveness
Risk: warning always ON.
BAD efficiency
CSW : benefits including CT-F.ID algorithm
Computed safety speed as a function of trajectory
radius and potential friction coefficient
Constant radius curve (120m)
 Safety speed changing the
potential friction.
Potential
friction
Trajectory radius [m]
Simulations about how a CSW system will work with
different values of μREF:
μREF
If critical road conditions occur (i.e.
ice, snow)  high overestimated
saefty speed
μREF = 0.7
Computed Safety
speed
Real potential friction
If μ0 =0.2  the safety
speed is 43.6% LOWER
If μ0 =1  the safety
speed is 21.8% HIGHER
BAD EFFECTIVENESS
Simulations about how a CSW system will work with
different values of μREF:
μREF
If critical road conditions occur (i.e.
ice, snow)  high overestimated
saefty speed
BAD EFFECTIVENESS
Compromise is needed…
Computed Safety
speed
BAD EFFICIENCY
μREF = 0.4
μREF
If μ0 =0.2  the safety
speed is 19.8% LOWER
Conservative approach BUT if usual
medium-high road conditions occur (i.e.
dry asphalt)  high underestimated safety
speed
If μ0 =1  the safety
speed is 78.1% HIGHER
Simulations about how a CSW system will work
with different values of μREF via CT.
μREF = CT-F.ID
Max UNDERESTIMATED
speed: 13.2%
Max OVERESTIMATED
speed: 12.1%
Lower difference between real safety
speed and the safety speed computed by
the algorithm
Both in safety and unsafety regions
BETTER EFFECTIVENESS
&
GOOD EFFICIENCY
CSW Implementation: on-board implementation
Potential friction µ0
Vertical load: Fz [N]
Vehicle position
Trajectory
CSW
Algorithms
SAFETY STATUS
Safety
speed
E-Horizon
Carthography
Vehicle speed [km/h]
Safe
Warning
Danger
Vertical load: Fz [N]
Vehicle position
Trajectory
CSW
Algorithms
SAFETY STATUS
Safety
speed
E-Horizon
Carthography
How is it possible to verify the
on-board procedure
introducing digitalized maps?
Safe
Warning
Danger
Co-simulation
CSW
Algorithms
SAFETY STATUS
Safety
speed
Potential
friction µ0
Safe
Warning
Danger
Vehicle speed
[km/h]
E-Horizon
My Vehicle position
Vehicle position
VEHICLE MODEL
2° turn
1° turn
Potential friction [-]
CSW Implementation. Example on dry asphalt
2° turn
1
0.5
0
950
1000
1050
s [m]
1100
1150
1000
1050
s [m]
1100
1150
1050
s [m]
1100
Hypothesis: potential friction constant
between my position and the next curve.
Curve Radius [m]
0
-20
-40
-60
-80
-100
950
100
Safety speed
My vehicle speed
V [km/h]
80
60
40
20
0
950
1000
1150
Safe
Warning
Danger
Simulated world – vehicle model
Real world – camera
Blue line: maximum safety speed
Red dots. Vehicle speed
CONCLUSIONS
-
The knowledge of road friction coefficient plays a key role in ADAS systems improvements,
both in terms of effectiveness and efficiency.
-
Two ADAS systems have been considered to verify their improvement introducing potential
friction knowledge: Collision Avoidance and Curve Speed Warning
-
The friction knowledge has been supposed to be identified via CT-F.ID with 5 ranges. It has
been verified to be adequate for ADAS systems performances improvements:
1) Numerical simulations of both CAW and CSW systems
behavior proved how 5 ranges of μ can improve both
system’s effectiveness and efficiency.
2) The algorithms developed togheter with CT potential
friction identification have been implemented on-board
FUTURE DEVELOPMENTS. Adaptive Speed Limiter (ASL): IDEA
TODAY  Speed Limiter
click
click
As a function of road speed limit the
driver set the maximum speed of the
vehicle with its speed limiter.
Ok, I fix
60km/h
FUTURE DEVELOPMENTS. Adaptive Speed Limiter (ASL): IDEA
TOMORROW  Adaptive Speed Limiter
Via road speed limit
+ road µ condition
infos, the system fix
my max speed
FUTURE DEVELOPMENTS. TARGETS
TARGETS:
1. To develop a simulator exploiting numerical
vehicle dynamic code.
WHY?
•
•
•
To impose offline traffic conditions.
To test the HumanMachineInterface.
To verify different drivers behaviour
with same test scenario.
• To impose a fixed virtual path
exploiting google maps or other
navigation system.
 TEST CONDITION REPETEABILITY.
TARGET 1
2. To equip the PIRELLI vehicle with ADAPTIVE
SPEED LIMITER logic
TARGET 2

Presentazione della ricerca "Advanced Driver Assistance Control

Transcript

Documenti analoghi

Special Session SIG E3 Program - Transport Economic Regulation

English version - Stefano Pellegrini

on the dynamic response of long

MAPPING THE NEIGHBORHOOD The Multiple

medical rhinoplasty: practical course on filler and

Registration of Functional Data: Aligning Inner Carotid Artery

press release in china once again

Audio driver for window 7 of realtek

Technical Datasheet