potential friction knowledge benefits on adas pcs-acc ... · the vehicle before collision and...

Potential friction knowledge benefits on ADAS PCS-ACC systems:

logic, performances and simulated critical scenarios.

Date: 2012, Sept. 18

Elisabetta Leo Marco E.Pezzola

Soluzioni Ingegneria

Federico Mancosu

AUTHORS:

Federico Cheli

Politecnico di Milano

18/09/2012 N°2

ADAS braking system world

ADDING: • Radar/lidar/cameras • V2V communication • V2I communication • Tyre-road condition • Vehicle dynamic • ….

TARGET: impact mitigation

MONITORING driver: • steering input • braking/throttle • internal cameras

TARGET: to investigate the probability of an accident

TARGET: to avoid accident

SAFETY

RESEARCH TARGET: to verify benefits of tyre-road condition knowledge to avoid accident

POTENTIAL FRICTION IDENTIFIER v3.2

PFI3.2

18/09/2012 N°3

Constant speed Accelerating/braking

Longitudinal maneuvers

Lateral maneuvers

Potential friction identification

TARGET: to identify the potential friction before reaching it. As before as possible!

Higher longitudinal slip

Hig

he

r la

tera

l slip

18/09/2012 N°4

Index

1. ADAS braking system logic description (including potential friction - 0 - infos).

2. Evaluation of 0 knowledge benefits (VIA CarMaker simulations): critical scenarios’ definition.

3. “ 0 identification algorithm” (longitudinal transient): experimental tests and results.

4. Implementation of the “ 0 identification algorithm” in CarMaker & Real-Time analysis.

5. Conclusions.

18/09/2012 N°5

Index





5. Conclusions.

18/09/2012 N°6

6

CONTROL MODE definition

1) SAFE 2) WARNING 3) DANGER

REAL or SIMULATED WORLD

Index 1

Index 2

Desired acceleration

% throttle

% Brake

SAFETY INDEXES

COMPUTATION

CONTROL LOGIC

µ: potential friction (direct or indirect mode)

Vehicle speed (i.e. via CAN bus)

Relative speed and distance between vehicles: radar

ADAS braking system logic description

18/09/2012 N°7

Relative speed Relative distance

INDEX 1: “1/TTC” INDEX 2: “Ad_D”

Nomenclature TTC = Time to collision Ad_D = Adimensional Distance.

Description: Time to reach the in-front-vehicle assuming no driver actions

Margin between required distance to stop the vehicle before collision and actual relative distance

Function of: Relative distance Relative speed

Relative distance Relative speed Potential friction Human’s time response to activate brake Vehicle dynamics

Safety if TTC: HIGH 1/TTC: LOW Ad_D: HIGH


18/09/2012 N°8

f(Ti

me

To

Co

llisi

on

)

f (distance margin)”

SAFE comfort mode (ACC)

Low accelerations

WARNING high deceleration

allowed

WARNING high deceleration

allowed

DANGER maximum

deceleration

0 importance: • 0 Ad_D . • To fix the maximum allowed deceleration value. • To compute the desired distance in SAFE region.


18/09/2012 N°9

0

20

40

60

80

100

020

4060

80100

120

-10

-8

-6

-4

-2

0

-10

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

0 = 1 rel distance rel speed

rel distance rel speed

DESIRED DISTANCE in SAFE region

IMPOSED ACCELERATION - example

Rel

ativ

e d

ista

nce

[m

]

SAFE

W

AR

NIN

G

DA

NG

ER

0 10 20 30 40 50 60 70 800

20

40

60

80

100

120

140

Actual Speed (km/h)

Desired D

ista

nce (

m)

mu=0.2

mu=0.4

mu=0.6

mu=0.8

mu=1


18/09/2012 N°10

Index





5. Conclusions.

18/09/2012 N°11

11

VS VP

μs = μp

V0s = V0p

VS

Low Friction Road Normal Road

VP

μs < μp V0s = V0p

Vehicle P emergency brake


Vehicle S emergency brake in traffic

μs = μp V0p= 0

VS

D Preview Radar

≠ friction = initial speed

= friction = initial speed

= friction ≠ initial speed

Evaluation of 0 knowledge benefits

Low Friction Road

18/09/2012 N°12

12

VS VP

Low Friction Road

μs = μp

V0s = V0p

VS


VP

μs < μp V0s = V0p




μs = μp V0p= 0

VS

D Preview Radar





18/09/2012 N°13

13

D2

D1

D1

VS VP

µ=0.4

D2

VS VP

µ=0.4

D2 ≈ 30m

D1 ≈ 15m Previous car REAL speed profile

ALGORITHM DESIRED speed profile

REAL speed profile

DESIRED = REAL speed profile

Previous car REAL speed profile

0 knowledge DOESN’T IMPROVE

SAFETY


18/09/2012 N°14

14

VS VP

Low Friction Road

μs = μp

V0s = V0p

VS


VP

μs < μp V0s = V0p




μs = μp V0p= 0

VS

D Preview Radar





18/09/2012 N°15

15

Speed Profile

D1

VS VP

µ=0.4

µ=1

Speed Profile

D3

D2

V V

µ=0.4

D2 ≈ 30m

D1 ≈ 15m Previous car REAL speed profile

ALGORITHM DESIRED speed profile

REAL speed profile

DESIRED = REAL speed profile

Previous car REAL speed profile


0 knowledge IMPROVES

SAFETY

18/09/2012 N°17

17

VS VP

Low Friction Road

μs = μp

V0s = V0p

VS


VP

μs < μp V0s = V0p




μs = μp V0p= 0

VS

D Preview Radar





18/09/2012 N°18

18

VS

µ=0.2

VS

µ=0.2 D Preview Radar

D Preview Radar

ALGORITHM DESIRED ACCELERATION

REAL CCELERATION

0 knowledge IMPROVES

SAFETY


18/09/2012 N°19

23 24 25 26 27 28 29 30 31-10

-5

0

Time (s)

Vehic

le S

accele

ration [

m/s

2]

unknown DESIRED

unknown REAL

23 24 25 26 27 28 29 30 310

20

40

60

80

Time (s)

Rela

tive D

ista

nce [

m]

UNKNOWN

19

VS


ALGORITHM DESIRED ACCELERATION

REAL CCELERATION

1

1. Radar out of range: relative distance > 60m

2. SAFE region (if 0 is unknown) no deceleration imposed 2 3. WARNING region algorithm desired deceleration HIGHER than real

3 4

4. DANGER region algorithm desired deceleration HIGHER than real

CRASH OCCURS


18/09/2012 N°20

20

1. Radar out of range: relative distance > 60m.

2. NO SAFE REGION.

3. WARNING region as far as the radar see the vehicle, deceleration occurs.

4. DANGER region

VS


23 24 25 26 27 28 29 30 31-10

-5

0

Time (s)

Vehic

le S

accele

ration [

m/s

2]

23 24 25 26 27 28 29 30 310

20

40

60

80

Time (s)

Rela

tive D

ista

nce [

m]

unknown DESIRED

unknown REAL

KNOWN

UNKNOWN

KNOWN

1 3 4

CRASH DOESN’T OCCURS


18/09/2012 N°21

Index





5. Conclusions.

18/09/2012 N°22

Constant speed Accelerating/braking

Longitudinal maneuvers

Lateral maneuvers

TARGET: to identify the potential friction before reaching it. As before as possible!

Higher longitudinal slip

Hig

he

r la

tera

l slip

0 identification algorithm

18/09/2012 N°23

LONGITUDINAL + TRANSIENT

2. LOW 0.2< 0 ≤0.4

1st target: 0 MACRO levels resolution

1. LOWEST LEVEL: 0 ≤ 0.2

2. LOW LEVEL 0.2 < 0 ≤ 0.4

3. NOT LOW LEVEL: 0.4 < 0

2nd target: to increase resolution with additional levels

3. MEDIUM ZONE: 0.4 < 0 ≤ 0.9

4. HIGH ZONE: 0.9 < 0

1. LOWEST LEVEL: 0 ≤ 0.2

2. LOW LEVEL 0.2 < 0 ≤ 0.4

1. LOWEST

0 ≤ 0.2

3. NOT LOW 0.4 < 0

2. LOW 0.2< 0 ≤0.4

1. LOWEST

0 ≤ 0.2

3. MEDIUM 0.4< 0 ≤ 0.9

4. HIGH 0.9 < 0


18/09/2012 N°24

Vizzola Ticino proving ground

Reference test Track Texture/wet-dry Reference 0

T.T.1 (GRB) Icy-smooth, wet 0.15

T.T.2 (GRA) Icy-smooth, dry 0.65

T.T.3 (CEA) Concrete, dry 0.85

T.T.3 (V2A) Asphalt, dry 1.1

1. LOWEST

0 ≤ 0.2

3. NOT LOW

0.4 < 0


18/09/2012 N°25

1. LOWEST

0 ≤ 0.2

3. NOT LOW

0.4 < 0


1. LOWEST

0 ≤ 0.2

3. NOT LOW

0.4 < 0

When 0 is accessible, over 97% of correct estimation!


18/09/2012 N°26


Reference test Track Texture/wet-dry Reference 0

T.T.1 (GRB) Icy-smooth, wet 0.15

T.T.2 (GRA) Icy-smooth, dry 0.65

T.T.3 (CEA) Concrete, dry 0.85

T.T.3 (V2A) Asphalt, dry 1.1

«…. When 0 is accessible ?...»

1.50 m/s2

0.75 m/s2

0.25

1.30

m/s2

m/s2


18/09/2012 N°27


0 200 400 600 800 1000 1200 14000

20

40

60

80

100

[s]

[km

/h]

SUBURBAN ROAD

0 200 400 600 800 1000 1200 1400-4

-2

0

2

4

[s]

[m/s

2]

18/09/2012 N°28

-3 -2 -1 0 1 2 30

5

10

15

20

Acc class [m/s2]

Occ

urre

ncie

s at

eac

h cl

ass

[%]

WETHER CONDITION WHILE TESTS: DRY

-3 -2 -1 0 1 2 30

20

40

60

80

100

120

Acc class [m/s2]

Cum

ulat

ive

prob

abili

ty [%

]

Highway

Suburban

Urban


Acceleration classes [m/s2 ]: (- 3 : 0.1 : +3)

Occurrences [%] for each acceleration class

Accelerating (>0)

Braking (<0)

18/09/2012 N°29

-3 -2 -1 0 1 2 30

5

10

15

20

Acc class [m/s2]

Occ

urre

ncie

s at

eac

h cl

ass

[%]

WETHER CONDITION WHILE TESTS: DRY

-3 -2 -1 0 1 2 30

20

40

60

80

100

120

Acc class [m/s2]

Cum

ulat

ive

prob

abili

ty [%

]

Highway

Suburban

Urban


FREE

R

OLL

ING

[%] [%] [%]

Highway 5.2 89.9 4.9

Suburban 7.1 88.0 4.9

Urban 15.5 72.0 12.5

Availability

10.1

12.0

28.0

BR

AK

ING

AC

CEL

ERA

TIN

G

18/09/2012 N°30

Index





5. Conclusions.

18/09/2012 N°31

DSpace control desk visualizator for REAL TIME analysis

Emulator for OFF LINE analysis

DataLogger via AutoBox dspace

DATA processing via simulink code


Input variable & imposed 0

Implementation of the 0 identification algorithm

CAN state variables

x, y, speed

On road test recording track

and speed profile

OFF

LIN

E R

EAL

TIM

E

18/09/2012 N°32





CAN state variables

x, y, speed OFF

LIN

E R

EAL

TIM

E





and speed profile

18/09/2012 N°34


Potential friction level for Left Wheel

Potential friction level for Right Wheel

Vehicle speed

Vehicle acceleration

Example test:

18/09/2012 N°35


Example test:

18/09/2012 N°36





CAN state variables

x, y, speed OFF

LIN

E R

EAL

TIM

E





and speed profile

18/09/2012 N°37


18/09/2012 N°38

Index





5. Conclusions.

18/09/2012 N°39

Conclusions

• 0 - based, second generation ADAS braking system logic has been introduced, highlighting efficiency improvement on driving safety.

• Results on 0 identification algorithm (PFI3.2) have been summarized and method reliability has been estimated via experimental tests performed on Pirelli proving ground on reference potential friction tracks.

• Implementation of the 0 identification algorithm (PFI3.2) in both the CarMaker Simulink for off-line analysis and in Real Time analysis for data deliverance to beta-version of new generation ADAS system.

18/09/2012 N°40

Thanks for your attention!

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