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CIS 2011 rkala.99k.org1st September, 2011

Planning of MultipleAutonomous Vehicles usingRRTRahul Kala, Kevin Warwick

Publication of paper: R. Kala, K. Warwick (2011) Planning of Multiple Autonomous Vehicles using

RRT, Proceedings of the 10th IEEE International Conference on Cybernetic Intelligent Systems , Docklands, London, pp.20-25.

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CIS-2011, 1st September, 2011 2

Autonomous Vehicles

Safety

EfficientDriving

JamAvoidance

Coordination

Comfort

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Software Architecture

SensorEnvironment

understanding

Sensor

fusionLocalizationPlanning

Control Motion

Map Mission

Environment

3

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Software Architecture

4

Coordination

Vehicle 1

Vehicle 2

Vehicle 3

.

Vehicle n

Inter-Vehicle

Communication

CoordinationObstacle Discovery

Localization

Collision Avoidance

Travel Plan

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Junior (Stanford) Model

5

Planning

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Why not speed lanes?

6

Highly diversesizes

Excess road

widths with

diverse speeds

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Why not speed lanes?

7

Single lanes

Complex obstacle

framework

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Talos (MIT) Model

8

Key Takes:

Planner/Controller

integrated

Possibility of multiplevehicles

Source:Y.

Kuwata,S.

Karaman,J

.Teo,

E.

Frazzoli,

J.P.

How,

G.

Fiore,

Real-TimeMotio

nPlanning

With

ApplicationstoAutonomousUrbanDriving,

IEEETran

s.ControlSyst.Technol.,vo

l.17,no.5,

pp.11

05-1118,

2009.

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PLANNING ALGORITHM

9

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Layers of Planning

10

Route Planning

RoadSegmentation

Road SegmentPlanningAbstraction

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Overlapping Segment Planning

11

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Coordinate Axis System

12

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RRT

13

Source and

direction

Goal

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Curve Generation

14

max,min vkv

)(2)()( ddtdik

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Coordination

Principle: Prioritization

Rule: Higher priority for vehicle earlier in

segment Communication: Trajectory followed

stored as hash maps

Speed reduced in steps of till feasibleplan found

15

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Results

16

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RRT Generated

17

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Vehicle Following

18

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Overtaking

19

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Vehicle Avoidance

20

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Analysis

21

0

100

200

300

400

500

600

700

800

10 20 30 40 50 60 70 80 90 100 110 120 130

Numberofnodesintree

Step Size

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Analysis

22

0

500

1000

1500

2000

2500

10 20 30 40 50 60 70 80 90 100 110 120 130

Totalnumberof

iterations

Step Size

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Why RRT?

* Assuming high resolution maps, traffic scenario, complex obstacle framework.

Reduction of resolution not possible

23

Property Algorithm Order*

Completeness Graph > RRT > Evolutionary > Behavioral

Ability to generate paths

with large number ofobstacles

Graph > Behavioral > RRT > Evolutionary

Path length/ Optimality Evolutionary > Behavioral > RRT > Graph

Computational Cost Behavioral < RRT < Evolutionary < Graph

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Conclusions

24

Problem

Planningmultiplevehicles

Different timesof emergence

Traffic Scenario

Constraints

ComputationalTime

ComplexObstacleFramework

Solution

RRT Road Axis

System

CurveSmoothening

Priority

Open Issues

Optimality Being un-

struck

Cooperation Non-

autonomousvehicles

Uncertainties Curves

RRT-Connect

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THANK YOU

Acknowledgements:

Commonwealth Scholarship Commission

in the United Kingdom

British Council

25