School of Systems, Engineering, University of Reading

rkala.99k.orgApril, 2013

Motion Planning for Multiple Autonomous Vehicles

Rahul Kala

Fuzzy LogicPresentation of paper: R. Kala, K. Warwick (2015). Reactive Planning of Autonomous Vehicles for Traffic Scenarios. Electronics 4, 739-762

Motion Planning for Multiple Autonomous Vehicles

Why Fuzzy Logic?• Computational Time• Work with partially known environments

Issues• Completeness• Optimality

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Motion Planning for Multiple Autonomous Vehicles

Key Contributions• Design of a Fuzzy Inference System for the

problem. • Design of a decision making module for

deciding the feasibility of overtaking purely based on the vehicle distances and speeds.

• Design of an evolutionary technique for optimization of such a fuzzy system.

• Using the designed fuzzy system enabling vehicles to travel through a crossing by introducing a virtual barricade.

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Motion Planning for Multiple Autonomous Vehicles

Fuzzy Inference System• Codify the immediate scenario to a few inputs. • Decide the actions to be taken and hence design the

outputs. • Think of various scenarios and the associated

inputs/outputs and generalize them as rules

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Design Methodology

Formulate Inputs

Formulate Outputs

Steering

Speed

Design Rules

Motion Planning for Multiple Autonomous Vehicles

Inputs

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Inputs

Continuous Valued

Angle deviation from road

Distance from left boundary/ obstacle

Distance from right boundary/ obstacle

Distance from front vehicle/ boundary/ obstacle

Side: distance of vehicle in wrong side

Discrete Valued/ Strategy Inputs

Turn to avoid obstacle/ overtake vehicle in front

Requested turn: turn to enable another vehicle to overtake

Motion Planning for Multiple Autonomous Vehicles

Some Inputs

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γi

θi

Obstacle

ObstacleLeft Distances

Right Distances

Front Distances

Deviation = γi – θiMinimal of twin distance inputs is

taken

Motion Planning for Multiple Autonomous Vehicles

Turn to avoid obstacle

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If front distance from left corner is less than front distance from right corner, turn right; and vice versaHeuristic holds from most small obstacles/general scenarios

Motion Planning for Multiple Autonomous Vehicles

Overtaking

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Check if the front vehicle (1) is

slower and

needs to be

overtaken by the vehicle being

planned (2)

Check if road is wide enough to

safely accommodate both

(1) and (2)

Check if overtaki

ng is feasible

with any

other vehicle (3) on

the road while (2)

overtakes (1)

Initiate the overtake(2)

decides the side of

overtake

(2) steers on

decided side

(1) steers on opposite

side

Check wheth

er cooperation of (3)

is required or not

In case yes, (3)

steers depending upon the

scenario

Motion Planning for Multiple Autonomous Vehicles

Feasibility criterion with 3 vehicles• Assumption: Road not wide enough for

multiple (>3) vehicles to lie side-by-side• The vehicles are projected to travel straight

subsequently (else would require to adjust for overtaking)

• Condition 1: (1), (2) and (3) can simultaneously lie side by side along the road,

OR• Condition 2: (2) can complete overtake of (1)

within the time (3) does not lie in the overtaking zone

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Motion Planning for Multiple Autonomous Vehicles

Cooperation• Whether (3) needs to steer in a particular

direction to enable overtake?

• Cooperation required in case the vehicles need to align to fit within road width (condition 1)

• (3) moves opposite to the location of (1) and (2)

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Motion Planning for Multiple Autonomous Vehicles

Vehicle Following• In case of infeasibility, the vehicle would follow

the vehicle in front

• If any oncoming vehicle/any other vehicle causing overtake infeasibility passes, overtake initiates

• Vehicle can drive in the wrong side and does not consider the vehicle in front (both inputs disabled)

• When (2) is ahead of (1), the inputs are enabled again

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Motion Planning for Multiple Autonomous Vehicles

Crossing • Add virtual barricade as boundaries on road not

used in navigation• Make the vehicle go by designed fuzzy planned

– overtaking disabled• In absence of traffic lights first come first serve

sequence followed

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Motion Planning for Multiple Autonomous Vehicles

Crossing

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Left Boundary

Right Boundary

Barricades

Motion Planning for Multiple Autonomous Vehicles

Evolution of fuzzy planner• Optimization using Genetic Algorithm• Complete design using Genetic Algorithm would be

computationally expensive• Initial fuzzy planner designed by human based on sample

scenarios• Altering optimization of rules and membership functions

for a few cycles• Rule optimization can increment/decrement any

antecedent/consequent by a unity• Membership function optimization can move any

membership function parameters within a narrow region.• Fitness Function: Minimize time, minimize collisions,

minimize time in wrong side, minimize safety distance breach

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Motion Planning for Multiple Autonomous Vehicles

Evolution of fuzzy planner

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F ← Human Designed Fuzzy Planner

While no of cycles are not met

F ← Tune Rules (F)

F ← Tune Membership Function Parameters (F)

Fitness Evaluation

Simulation

Map

Return F

End

Motion Planning for Multiple Autonomous Vehicles

Results – Single Vehicle

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Motion Planning for Multiple Autonomous Vehicles

Results – Vehicle Avoidance

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Motion Planning for Multiple Autonomous Vehicles

Results – Multi Vehicle

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Motion Planning for Multiple Autonomous Vehicles

Results – Multi Vehicle

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Motion Planning for Multiple Autonomous Vehicles

Results - Crossing

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Motion Planning for Multiple Autonomous Vehicles

Analysis

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1 9 17 25 33 41 49 57 65 730

1

2

3

4

5

6

7

8

9

10

Left Turn 1Left Turn 2Right Turn 1Right Turn 2StraightLeft Turn with Obstacle

Time

Spee

d

1 10 19 28 37 46 55 64 73 820

2

4

6

8

10

Left Turn 1Left Turn 2Right Turn 1Right Turn 2Straight

Time

Spee

d

Single vehicle scenarios

Two vehicle scenarios

Motion Planning for Multiple Autonomous Vehicles

Analysis

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1 8 15 22 29 36 43 50 57 64 71 78 85 92 990

1

2

3

4

5

6

7

8

9

10

With OvertakeWithout Overtake

Time

Spee

d

Overtaking scenarios

Motion Planning for Multiple Autonomous Vehicles

Angle Deviation from Road

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1 11 21 31 41 51 61 71-30

-20

-10

0

10

20

30

Left TurnRight TurnStraightLeft Turn with Obstacle

Time

Ang

le

1 9 17 25 33 41 49 57 65 73 81-30

-20

-10

0

10

20

30

Left TurnRight TurnStraight

Time

Ang

le

Single vehicle scenariosTwo vehicle scenarios

Motion Planning for Multiple Autonomous Vehicles

Angle Deviation from Road

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1 8 15 22 29 36 43 50 57 64 71 78 85 92 99-30

-20

-10

0

10

20

30

With OvertakeWithout Overtake

Time

Ang

le

Overtaking scenarios

Motion Planning for Multiple Autonomous Vehicles rkala.99k.org

Thank You

• Acknowledgements:• Commonwealth Scholarship Commission in the United Kingdom • British Council