motion planning for multiple autonomous vehicles: chapter 5a - fuzzy logic
DESCRIPTION
This series of presentations cover my thesis titled "Motion Planning for Multiple Autonomous Vehicles". The presentations are intended for general audience without much prior knowledge of the subject, and not specifically focused upon experts of the field. The thesis website contains links to table of contents, complete text, videos, presentations and other things; available at: http://rkala.in/autonomousvehiclesvideos.htmlTRANSCRIPT
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