2015 Artificial Intelligence Techniques at Engineering Seminar - Chapter 2 - Part 4: Intelligent Transportation Systems

Artificial Intelligence Techniques applied to Engineering

Part 2. Genetic Fuzzy Systems Enrique Onieva Caracuel

@EnriqueOnieva

1.Fuzzy Logic

2.Genetic Algorithms

3.Genetic Fuzzy Systems

4.Applications to Intelligent Transportation Systems: My Experience

Técnicas de Inteligencia Artificial aplicadas a la Ingeniería @EnriqueOnieva 2014/2015 2

Intelligent Transportation Systems

Intelligent Transportation Systems integrate information and communication technologies with transportation of passengers and goods

Mobility

Safety

Productivity

Energy consumption

Capacity

1


Intelligent Transportation Systems

Common services

Information Systems

Route planning

Air transport

Maritime transport

Road transport Intelligent infrastructure

Intelligent vehicles Active assistances

Pasive assistances

2

Autonomous Driving?


Motivation

Fuzzy Logic

IF the vehicle is derived through the right, steer to the left

IF the vehicle is derived through the left, steer to the right

IF the vehicle is slow, press the throttle

IF the vehicle is fast, press the brake

1

Control System Driver


Motivation 2


AUTOPIA Program 1 1998

2012


AUTOPIA Program

Throttle Pedal signals commuted Orders communicated by an

Analog Card

Brake Intervention on the ABS Electro-hydraulic system

Motor Deposit 3 valves: Limiter, Proportional,

Nothing-All

Orders communicated by a CAN controller

2 WLAN

Antenna

Power Supply

GPS Receiver

Computer

IMU

GPS Antenna

CAN-USB converter

Auxiliary Battery

CAN Module

Electro-hydraulic

system


Speed Control for Cities

Inputs

Speed error(km/h)

Acceleration (km/h/s)

Outputs

Throttle [0,1] Throttle [0,0.4]

Brake [0,1] Brake [0,0.2]

1

Comfort acceleration ≤ 2.5 m/s2

Ac+ Ac0 Ac-

EV+ B02 B01 B01

EV0 T00 t01 T01

EV- T01 T02 t04

E. Onieva, et al., Throttle and Brake Pedals Automation for Populated Areas, Robotica, vol. 28, n. 4, pp 509-516.

Speed Error (km/h)

Acceleration (km/h/s)

Negative Negative Zero Positive

Negative Zero Positive

T00 T01 T02 T04

B00 B01 B02


Speed Control for Cities

First gear (speed > 16 km/h) Error measured after transitory

state (5 s) Bigger error at 15 km/h

(1st to 2nd gear)

Similar results (speed≤ 20 km/h) Better results at 25 km/h

2

E. Onieva, et al., Throttle and Brake Pedals Automation for Populated Areas, Robotica, vol. 28, n. 4, pp 509-516.

Human System

10km/h ±0.63 ±0.71

15km/h ±0.88 ±0.98

20km/h ±0.72 ±0.84

25km/h ±1.22 ±0.9

Speed Target

Speed Target

Speed Target

Speed Target

Speed Target

Time (s)

Spee

d (

km/h

)


Speed Control on-line Learning

1. Rules’ consequents modification in real time Speed error

Acceleration

Rule activation

9 cased reward

Positive or Negative error

Acceleration and comfort acceleration

Acceleration decreases when error 0

1

E. Onieva et al., On-Line Learning of a Fuzzy Controller for a Precise Vehicle Cruise Control System, Expert Systems with Applications. 40 (4) , pp. 1046-1053.



1. Rules’ consequents modification in real time

2


Spee

d (

km/h

)

Time (s)



2. Trapezoids’ modification

After a certain time (100 seconds)

Input values histogram analysis

A trapezoid is added if it is low-covered

A trapezoid is narrowed if covers several frequent values

3



Speed Control on-line Learning 4 Test with 40 vehicles in TORCS

Different dynamics

Different behaviors

Simple initial controller 2x2 membership functions

All the singletons = 0 (do nothing)




1. Speed change test

2. Fixed speed test (15 km/h)

3. Fixed speed test (5 km/h)

5



Information capture

Information Processing

Simplification Extension

Steering control by Genetic Algorithms 1

E. Onieva, et al., Genetic fuzzy-based steering wheel controller using a mass-produced car, Int. J. of Innovative Computing, Information and Control. vol. 8, n. 5B, pp. 77-94. E. Onieva, et al., Automatic lateral control for unmanned vehicles via genetic algorithms. Applied Soft Computing Journal. 11 - 1, pp. 1303 - 1309, 2011

Lateral Error Angular Error

Stee

rin

g

Lateral Error Angular Error

Stee

rin

g

Lateral Error

Angular Error

Reference Line


Steering control by Genetic Algorithms

Membership functions Representation

Rule base representation Integer coding

Length = Number of rules

21 Singletons in [-1,1]

2

LAT/ ANG

θ

{Ve

ryLeft}

Left

No

Righ

t

{Ve

ryRigth

}

Θ M M M M M

{VeryLeft} M C C C C C

Left M C C C C C

No M C C C C C

Right M C C C C C

{VeryRigth} M C C C C C


Left NO Right VeryLeft VeryRight Left NO Right

R10, R9, R8, R7, … R1 L1, L2, L3, L4, … L10 NO

Right (Clockwise) Left



Genetic fuzzy system in 2 stages Membership function

optimization Real coding BLX-𝛼 crossover

Rule base optimization Integer coding One point crossover

Steady state Genetic Algorithm Binary Tournament Uniform Mutation Worse individual replacement

3




Objective function

Mean squared error (MSE)

Highest jump in the control surface (Dist)

Fitness Function (Min): 0.75·MSE + 0.25·Dist

4




5


Controllers Speeds

Labels Rule Base Average Maximum

3x3 Marginal 12.8 22.4

3x3 Central 14.6 22.1

3x3 Total 13.5 24

5x5 Marginal 14.9 22.1

5x5 Central 14.8 28.6

5x5 Total 14.8 27.9

Lateral error Angular error Honorable Mention to

best student work

ESTYLF 2008

East (m)

No

rth

(m

)


Intersection decision by genetic algorithms

Decision making in non-cooperative intersections

Non yielding always strategy

Safe and efficient maneuver Slightly accelerate to pass

before the manual one

Slightly brake to yield

Without stopping

1

E. Onieva, et al., Genetic Optimization of a Vehicle Fuzzy Decision System for Intersections, Expert Systems with Applications. 39 (18) , pp. 13148-13157

Accidents at intersections

Intelligent Trasnportation Systems

Intersections

Manual Manual Autonomous

Autonomous Autonomous Autonomous

Coordination

Accidents

Roads



1. Check if the vehicle is going to cross and by where Fuzzy rule based system 3 inputs Manually adjusted

2. Decide the autonomous vehicle’s speed to finish the maneuver Without risk As soon as possible Fuzzy rule based system 4 inputs

Coded with {2,3,4} membership functions 81 Granularities

2 types of outputs Relative / Absolute Speed

162 controllers adjusted by a Genetic Algorithm

3. Move the pedals to reach the desires speed Vehicle’s longitudinal dynamics model Flat surface

2


Manual

Autonomous

Time Sp

eed



Evaluation in a variable number of scenarios Nsc=1+19·(g/G)

2 Executions Free (EF) (Keep SA)

What happens if speed does not vary?

Controlled (EC) Does the fuzzy system avoid the

collision

3 possible results No collision Lateral collision Frontal collision

3


No Collision Lateral Collision Frontal Collision

Keep Speed

up Slow Down



Partial fitness depending on:

Result in free execution

Result in controlled execution

How much has been the speed varied

Fitness function Minimize the sum of partial fitnesses

4


Description Meaning Partial fitness

No collision (EF=EC=NO) |∫SAc-∫SA

l|

A lateral collision is avoided (EF=LA & EC=NO) •|∫SA

c-∫SAl|, if speeds up

•2.500, if brakes down

A frontal collision is avoided (EF=FR & EC=NO) •|∫SA

c-∫SAl|, if brakes down

•2.500, if speeds up

Collision is not avoided (EF≠NO & EC ≠ NO) 5.000

Collision is caused (EF=NO & EC ≠ NO) 10.000



Safety vs Number of rules

Safety > 90%

Some relatives are worse that a ‘do nothing’ system

Absolute ones are safer

ABS4423, ABS3433, ABS3344 y ABS2442 100%

Is the safety dependent on the type (absolute / relative) of controller?

5


Relative Controllers

Absolute Controllers

Number of Rules



Granularities correlation

Most systems are near the diagonal

54% vs 46% of structures are safer with a specific model

Safe structures are both when relative and absolute output

6


Safety for Relative FRBS

Safe

ty f

or

Ab

solu

te F

RB

S

Structures with higher safety in Absolute

mode (46%) Structures with higher safety in Relative mode

(54%)



A ‘stop always’ policy safety 100 %

No efficient, neither intelligent

Fitness function measures the efficiency of the systems

Lineal relationship

Safety comes with efficiency

7


Relative Controllers Absolute Controllers

Safety

Fitn

ess

Fun

ctio

n



Both vehicles start at same speed Free execution Frontal collision System must brake

All of them do ABS4242 brake less REL3344 speeds up once the risk disappear

Autonomous one starts slightly faster Free execution Lateral collision System must speed up

All of them avoid the collision REL3344 does it by speeding up

Autonomous one starts much faster Free execution No collision System must maintain speed

All of them avoid the collision REL3344 varies less the speed

8


Time (s)

Spee

d

(km

/h)

Dis

tan

ce

(m)

Time (s)

Spee

d

(km

/h)

Dis

tan

ce

(m)

Time (s)

Spee

d

(km

/h)

Dis

tan

ce

(m)


Videos

Learning to steer in an autonomous vehicle

Self-Archive

Autonomous Driving Citroën C3 Pluriel

https://www.youtube.com/watch?v=qm-nh7_fJvY

Grand Cooperative Driving Challenge (GCDC) - Technische Universiteit Eindhoven

https://www.youtube.com/watch?v=BprHHm5j_hA

Other Applications

Composition: https://www.youtube.com/user/GrupoAUTOPIA/videos

Autonomous Driving @ High Speed

https://www.youtube.com/watch?v=1zoTg_Pnxbg

Videos/defensaPequeño.wmv

Videos/Conducción Autónoma Citroën C3 Pluriel _ Autonomous Driving Citroën C3 Pluriel(360p_H.264-AAC).mp4

Videos/Grand Cooperative Driving Challenge (GCDC) - Technische Universiteit Eindhoven(360p_H.264-AAC).mp4

Videos/v2 - Aplicaciones_Corto.wmv

Videos/Conducción autónoma a Alta Velocidad _ Autonomous Driving @ High Speed(360p_H.264-AAC).mp4


2009 Simulated Car Racing Championship 1

Gears Change current gear Rule system (RPM)

Use reverse gear Angle + Deviation

Steer

Centered vehicle Laser Sensors

Reverse gear Angle

Go back to track Angle + Deviation

Pedals Adequate speed Speed error

ABS / TCL Filters Speed-Wheels

Objective Desires Speed Fuzzy System

Learning

Off track

Decision system Borders crashs

Long straights

Opponents

Overtaking

Rule System Avoid collisions

Emergency braking

E. Onieva, et al., A Fuzzy Based Driving Architecture for Non-player Characters in a Car Racing Game, Soft Computing vol. 15, n. 8, pp. 1617-1629, 2011.


2009 Simulated Car Racing Championship

Gear Control

Change current gear according with RPM

[1ª-3ª] ↑ if RPM>9000

[4ª-5ª] ↑ if RPM>9500

[2ª-4ª] ↓ if RPM<3000

[5ª-6ª] ↓ if RPM<3500

Reverse gear?

Continue race

2




Pedals control

Throttle and brake

Pedal [-1,1]

Speed and wheels’ speed based filters

[ABS Brake]

[TCS Throttle]

Special case: Reverse Gear

Pedal = 0.25

3


Speed – Target (km/h)

Ped

al



Steer control

Reverse gear

Off-track

Inside track

4


Stee

r

Angle (rad)

Angle (rad)

Stee

r

Deviation (m)



Objective speed IF FRONT is High 200 km/h

IF FRONT is Medium 175 km/h

IF FRONT is Low Y MAX10 is High 150 km/h

IF FRONT is Low Y MAX10 is Medium 125 km/h

IF FRONT is Low Y MAX10 is Low Y MAX20 is High 100 km/h

IF FRONT is Low Y MAX10 is Low Y MAX20 is Medium 75 km/h

IF FRONT is Low Y MAX10 is Low Y MAX20 is Low 50 km/h

Non-Fuzzy rule:

IF FRONT = 100 300 km/h

5


Fro

nt

Max

10

Max

20

Low Medium High

Low Medium High

Low Medium High

Front = P0



Opponents

Modify the steer to overtake Sensors at {±90º} SI (measure/speed)<Tolerance (steer+=Increment)

Modify the steer to avoid collisions Sensors at {±30º} SI (measure<10) (steer±=0.25)

Emergency breaking Sensors at {±20º} SI (measure<10) (VELobj *=0.8)

6




3 International Conferences

Rules

3 unknown tracks

Classification phase race alone: 200 seconds

Race among the 8 classified. 10 races, 10 laps, different starting

F1 punctuation scheme:

Fastest lap +2

Less damage +2

Final score Median over 10 races

7



2009 Simulated Car Racing Championship 8

CEC GECCO CIG FINAL

Proposal 22 32 29 83

Cobostar 28.5 16.5 30 75

Champ2008 20 23 12.5 55.5

Perez &Saez 16 11 12.5 36.5

Best student work award ESTYLF 2010




Similar architecture Punctual modifications in certain modules

Removing of the fuzzy system in charge of determining the desired speed

Optimized Steer and target speed: Generational Genetic

Algorithm

Controller evaluation in 4 tracks

Maximize the sum of distances coverted

1

E. Onieva, et al., An evolutionary tuned driving system for virtual car racing games: The AUTOPIA driver, Int. Journal of Intelligent Systems, vol. 27, n. 3, pp. 217–241, 2012.



Real optimization

10 component vector

Steer control

Target speed

BLX-𝛼 Crossover

Uniform mutation

2




Winner in 2010

System to beat at 2011, 2012 y 2013

Not beaten until now

3


Proposal Muñoz Mr. Racer Polimi

GECCO_1 12 5.5 9 6

GECCO_2 12 8 4 4.5

GECCO_3 10 9 3 5.5

WCCI_1 10 10 4 5

WCCI_2 8 10 3 5

WCCI_3 6 8 2 6

CIG_1 8 4 3 10

CIG_2 12 2 4 6

CIG_3 5 6 8 8

Proposal Muñoz Mr. Racer Polimi

GECCO 34 17 16 6

WCCI 24 28 9 16

CIG 25 12 15 24

TOTAL 83 57 40 46


Racing overtakes

Opponents that oppose to be overtaken are implemented.

They try to reach the position of the overtaker

3 types: Limited

Slow

Complete

Opponent must be overtaken

1

E. Onieva, et al., Overtaking Opponents with Blocking Strategies Using Fuzzy Logic, IEEE Conference on Computational Intelligence in Games, 2010

Limited

Slow

Complete


Racing overtakes

Fuzzy Rule Based System 4 inputs:

Longitudinal distance (Dx) Lateral distance (Dy) Lateral deviation (DL) Time to Collision (TtC)

2 outputs Required lateral position Pedal (Emergency braking)

Manually tuned rule base 600 potential rules 3·8·5·5 Labels

Common sense If the maneuver is finished go back to the center Grouping If lateral distance is long, do not move

81 rules in the final rule base

2



Racing overtakes

They were tested: The proposal Controllers included in TORCS

Against: Slow at 12 different speeds Limited at 12 different speeds Complete at 12 different speeds

It is measured: % of finished maneuvers % of maneuvers without frontal damage

(system’s fault) % of maneuvers without lateral damage

(opponent’s fault)

3

Proposal Berniw Bt Inferno Lliaw Olethros Simplix Tita

%S 100 34.4 21.9 37.5 37.5 31.3 25 37.5

%BfD 90.6 75 87.5 78.1 78.1 100 100 81.3

%LfD 87.5 62.5 96.9 65.6 65.6 93.8 100 65.6


2º Best Work IEEE-CIG 2010


Videos

Highlight from the Simulated Car Racing Competition at CEC-2009 - Driver by Onieva and Pelta

https://www.youtube.com/watch?v=k5FgzAmJdzs

2010 Simulated Car Racing Championship - First Leg @ GECCO-2010

https://www.youtube.com/watch?v=SXDJMXpiRs0

Videos/Highlight from the Simulated Car Racing Competition at CEC-2009 - Driver by Onieva and Pelta(360p_H.264-AAC).mp4

Videos/2010 Simulated Car Racing Championship - First Leg @ GECCO-2010(360p_H.264-AAC).mp4


We collect traffic data from the

California Department of Transportation

About 15 km long 14 (actually more) loops detectors

6 loops detectors which give us flow, speed and density

8 loops detectors which give us only flow

Congestion Prediction 1

Objective: When congestion is going

to occur here ?

X. Zhang, E. Onieva, et al., Hierarchical Fuzzy Rule Based System Optimized with Genetic Algorithms for Short Term Traffic Congestion Prediction. Transportation Research Part C: Emerging Technologies. 43, pp. 127-142. 2014


Congestion Prediction

We group the information in 14 possible input variables 3 flows in the main highway F1 F2 F3

3 densities in the main highway D1 D2 D3

3 speeds in the main highway S1 S2 S3

2 input flows from the entrances iF1 iF2

2 output flows from the exits oF1 oF2

2




We define a hierarchical fuzzy system structure to predict congestions at desired

Example: 4 input variables with 3 membership functions per variable

3


The same example with N input variables : 3N rules in the non-hierarchical system 9·(N-1) rules in the hierarchical system

14 variables: 4.782.969 Vs 117 Rules



The systems is optimized by a Genetic Algorithm:

3-part coding

Input variables’ order Variable selection

Membership Functions

Rules’ consequents

2 operator groups:

Permutation

Real Coding

4




3 experiments:

97% 5 minutes ahead 9 variables



5



Assignment

Write an abstract of your thesis work (max. 250 words)

Look for 2-3 works that applies fuzzy logic to your thesis’ topic. Write a brief summary (max. 100 words/each)

Look for 2-3 works that applies genetic algorithms to your thesis’ topic . Write a brief summary (max. 100 words/each)

Look for 2-3 works that applies genetic fuzzy system to your thesis’ topic . Write a brief summary (max. 100 words/each)

Thank you very much

Any Question?