Motion Planning for Multiple Autonomous Vehicles: Chapter 7a - Semi Autonomous ITS

School of Systems, Engineering, University of Reading

rkala.99k.orgApril, 2013

Motion Planning for Multiple Autonomous Vehicles

Rahul Kala

Semi-Autonomous Intelligent Transportation SystemPresentation of paper: R. Kala, K. Warwick (2015) Intelligent

Transportation System with Diverse Semi-Autonomous Vehicles, International Journal of Computational Intelligent Systems, 8(5): 886-899.


Key Contributions• The approach presents an integrated study of

an intelligent transportation system covering all the various concepts which are separately studied in the literature.

• The study proposes architecture of the transportation systems of the future covering both decentralized vehicle control and a centralized management control.

• The approach is designed for diverse semi-autonomous vehicles operating in a scalable environment, which is the likely future of the transportation system.

• The approach is a positive step towards creation of a traffic simulation tool for diverse and unorganized traffic.

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Assumption• All semi-autonomous vehicles, or all can

communicate• All vehicles can be tracked• There might still be some human driven

vehicles• Vehicles have very diverse speeds

Key idea• Explore all the possibilities with such an

assumption• Enable vehicles cooperatively reach their

destination in the best way• Make transportation rules dynamic

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Proposed Intelligent Transportation System Architecture

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Central Information System

Vehicle Route Planning

Vehicle Control

Vehicle MonitoringTraffic Signal

Module

Speed Lane Module

Vehicle Motion Planning

Lane Booking Road

Booking

Scenario Specification

Map/Initial conditions

Traffic at roads

Speed of vehicles at lanes

Position/ Speed

Speed Limit

Booking Specifications

Signal state

Traffic Info.

Lane change, Follow, Stop/Start, Turn

Booked?Booked?


Traffic Lightning System • Since all vehicles are tracked, lights can change as soon

as all vehicles at a particular side are clear

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• Minimize maximum waiting timeAim:

• Change lights to allow the most waiting vehicle (and all other vehicles in that side) to pass by.Concept 1

• A vehicle may not simply drive through without waiting for a light change (if some other vehicle at some other side is waiting)

Concept 2

• No vehicle left in the current side, threshold number of vehicles have passed by, threshold time has passed by.

Change of light happens if


Traffic Lighting System

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1

2

35

4 6

1

2 3

Order of change of traffic lights. Numbers denote the order of appearance in the crossing scenario for the

present vehicles.



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Comparisons

Proposed System

Cyclic Light

Changes

Light change

after time threshold (current system)



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3 135 267 399 531 663 795 927 10591191132314550

100

200

300

400

500

600

700

Number of Vehicles

Ave

rage

tim

e of

tra

vers

al

Varying traffic density



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3 147 291 435 579 723 867 10111155129914430

100

200

300

400

500

600

Number of Vehicles

Ave

rage

Tim

e of

Tra

vers

al

Varying traffic density with traffic from one side blocked



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1 6 11 16 21 26 31 36 41 46 51 56 610

100

200

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500

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800

900

Proposed System

System with definite time of change

System with cyclic changes

time_th

Ave

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tim

e of

tra

vers

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Varying maximum time per light change for dense traffic



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Varying maximum time per light change for light traffic

1 29 57 85 1131411691972252532813093373650

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200

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500

600

time_th

Ave

rage

Tra

vers

al T

ime


Traffic Lightning System - Design Choice

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Make frequent light changes for dense traffic (towards first come first serve system, limits maximum waiting time)

Make infrequent light changes for dense traffic (smaller average traversal time, smaller time wasted in transition between changes)


Speed Lane• Since the vehicles are tracked and under

communication, speed distribution between lanes can be made dynamic

• Assumption: Speeds uniformly distributed in the speed band (between slowest and fastest current vehicle on the road)

• Concept 1: Divide the speed band by weights and distribute between the lanes

• Concept 2: Higher speed vehicle can jump to lower speed lane (for overtaking) but not vice versa rkala.99k.org


Speed Lanes

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Lowest speed capability vehicle

Highest speed capability vehicle

Speed Band(all other vehicles assumed to be uniformly distributed in this band)

Speeds

Speed limit of individual

lanes

Weighted speed division


Speed Lanes

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0

100

200

300

400

500

600

700

With speed lanesWithout speed lanes

w

Ave

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Comparisons for densely occupied scenario

Comparisons with a system with no speed lane – any vehicle can go anywhere


Speed Lanes

rkala.99k.orgComparisons for lightly occupied scenario

Speed lanes are a bad idea when traffic density is low, all diverse vehicles having access to both lanes can quickly criss-cross and overtake, not having to follow a vehicle as the other lane is reserved for high speed vehicles only

0

100

200

300

400

500

600

With speed lanes

Without speed lanes

w

Ave

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tra

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ime


Speed Lanes

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0100200300400500600700800900

1000

With dy-namic speed lanes

With fixed speed lanes

Speed Upper Bound

Ave

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Comparisons by increasing diversity – test the algorithm adaptability


Speed Lanes

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Comparisons by increasing diversity – test the algorithm adaptability

0

100

200

300

400

500

With dy-namic speed lanes

With fixed speed lanes

Speed Lower Bound

Ave

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ime


Route Planning • How to choose between a shorter/congested

route or a longer/not congested route?

• Use a standard graph search on road network graph

• Re-plan at every crossing to cope with changing traffic

• Cost function: length + penalty x traffic density

• For near roads use Current traffic density• For further roads use predicted traffic densityrkala.99k.org


Rout Planning

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Entry Point

of vehicle

s

Exit Point

of vehicle

s

Straight Road

Longer Diversion

Shorter Diversion

Vehicles first use straight road, on congestion of which shorter diversion is

also used, on congestion of which, longer diversion is also used.


Route Planning

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240

260

280

300

320

340

360

380

Proposed systemDistance minimiza-tion

α

Ave

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Comparisons with distance minimization graph search (only straight road used)

by varying penalty


Route Planning• Lower penalty = Distance minimization graph

search• Lower penalty = Search which attempts to

equate density on all roads

For the considered map:• Lower penalty makes straight road congested,

poor performance• Higher penalty encourages vehicles to take

diversions even though main road may not be too congested, poor performance

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Booking• Reserve a road or lane for privileged set of

vehicles• Key issue: How many vehicles to be booked?

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Booking

Road Booking

A road/ section of road booked

E.g. VIP road for a concert

Lane Booking

One of the lanes

booked

E.g. Olympic Vehicles

Lane

Emergency vehicle may

book lanes as they go


Booking

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Booked road (only for privileged/booked

vehicles

Diversion for all other vehicles


Booking

Booking more vehicles makes it take longer for the booked vehicles and shorter for

the other vehiclesrkala.99k.org

0100200300400500600700800

Non-booked vehicles

Booked vehicles

No vehicle booked

Percent of vehicles booked

Ave

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Road Booking


Booking

Booking more vehicles makes it take longer for the booked vehicles and shorter for

the other vehicles rkala.99k.org

0

50

100

150

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300

Non-booked vehicles

Booked ve-hicles

All vehicles booked

No vehicle booked

Percent booked vehicles

Ave

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Lane Booking

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

Thank You

• Acknowledgements:

• Commonwealth Scholarship Commission in the United Kingdom

• British Council

Documents

Motion Planning for Multiple Autonomous Vehicles: Chapter 7a - Semi Autonomous ITS