trajectory planning in a crossroads for a fleet of ... · a crossroads o. mehani context problem...

TrajectoryPlanning ina Crossroads

O. Mehani

Context

Problemformalisation

Reservations

Simulation

Conclusion

References

Trajectory Planning in a Crossroadsfor a Fleet of Driverless Vehicles

Olivier [email protected]

La Route AutomatiseeA -Mines Paris/INRIA Rocquencourt- Joint Research Unit

February 14, 2007 – Eurocast 2007


O. Mehani

Context


Reservations

Simulation

Conclusion

References

Plan

1 Context

2 Problem formalisation

3 Solution proposal : reservation system

4 Simulation and results

5 Conclusion and future works

6 References and discussion


O. Mehani

Context


Reservations

Simulation

Conclusion

References

Automated transportation serviceGoing to point B

A Cybercar-based transportation system with the goal to gofrom point A to point B as efficiently as possible :

quickest way.

;

shortest way

;

no collision

;

no deadlock.


O. Mehani

Context


Reservations

Simulation

Conclusion

References



quickest way ;

shortest way.

;

no collision

;

no deadlock.


O. Mehani

Context


Reservations

Simulation

Conclusion

References



quickest way ;

shortest way ;

no collision !

;

no deadlock.


O. Mehani

Context


Reservations

Simulation

Conclusion

References



quickest way ;

shortest way ;

no collision ;

no deadlock.


O. Mehani

Context


Reservations

Simulation

Conclusion

References

A layered approachThe problem is split into 3 levels

Three levels to reduce the complexity :

the macroscopic level a path is computed to reach the goalchoosing from the network a set of edges (i.e.roads) to use.

;

the mesoscopic level a trajectory is determined taking intoaccount the controllability constraint of thevehicle

;

the microscopic level the trajectory is followed while ensuring nounforeseen collision occurs.


O. Mehani

Context


Reservations

Simulation

Conclusion

References



the macroscopic level a path is computed to reach the goalchoosing from the network a set of edges (i.e.roads) to use ;

the mesoscopic level a trajectory is determined taking intoaccount the controllability constraint of thevehicle.

;



O. Mehani

Context


Reservations

Simulation

Conclusion

References



the macroscopic level a path is computed to reach the goalchoosing from the network a set of edges (i.e.roads) to use ;

the mesoscopic level a trajectory is determined taking intoaccount the controllability constraint of thevehicle ;



O. Mehani

Context


Reservations

Simulation

Conclusion

References

A layered approachThe mesoscopic level

The mesoscopic level is in charge of generating trajectories onthe road as commands for the microscopic level to match theorders of the macroscopic one.

A trajectory is the combination of a two-dimensional trace onthe road and timing information.

We focus on this level.


O. Mehani

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Reservations

Simulation

Conclusion

References

The mesoscopic levelRequirements

The mesoscopic level generates instructions for the microscopiclevel.

There are requirements on the type of instructions given for thesystem to work correctly :

quickest way ;

no collision ;

no deadlock.

;

respect of the controllability constraints (speed, steeringpossibilities, etc.) of the vehicle.


O. Mehani

Context


Reservations

Simulation

Conclusion

References

The mesoscopic levelRequirements

The mesoscopic level generates instructions for the microscopiclevel.

There are requirements on the type of instructions given for thesystem to work correctly :

quickest way ;

no collision ;

no deadlock ;

respect of the controllability constraints (speed, steeringpossibilities, etc.) of the vehicle.


O. Mehani

Context


Reservations

Simulation

Conclusion

References

Problem formalisationA simple crossroads

We focus on a regularcrossroads : 2 two-laned roadsintersecting at a right angle.

The crossroads being fixed it ispossible to determine a priori 2Dtraces for the vehicles to follow.

These traces are generatedusing clothoids in order toensure the attainability of themovement to the steeringvehicles.

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O. Mehani

Context


Reservations

Simulation

Conclusion

References

Problem formalisationA simple crossroads

We focus on a regularcrossroads : 2 two-laned roadsintersecting at a right angle.

The crossroads being fixed it ispossible to determine a priori 2Dtraces for the vehicles to follow.

These traces are generatedusing clothoids in order toensure the attainability of themovement to the steeringvehicles. ��

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��

��

��

��

��

��

��


O. Mehani

Context


Reservations

Simulation

Conclusion

References

A reservation systemPrevious works

Dresner and Stone proposed a reservation-based multiagentsystem [1, 2] : the vehicles reserve a number of squares on thecrossroads.

Depending on the granularity of the crossroads, this mayrepresent a large number of tiles to reserve.

We want to reserve only the relevant parts of the road i.e. thosewhere collisions can happen.


O. Mehani

Context


Reservations

Simulation

Conclusion

References

A reservation systemIdentifying the resource to reserve

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��

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We already know where thevehicles are to pass.

Thus, we know where the tracesintersect, which is where thecollision risk is present.

These critical points will be theresource to share among thevehicles by using a reservationsystem.


O. Mehani

Context


Reservations

Simulation

Conclusion

References

A reservation systemAlgorithm

1 A vehicle arrives close to the crossroads and requests thecrossroads geometry from the superviser (i.e. the 2D tracesand critical points).

;

2 According to its speed, it builds a reservation request whichis sent back to the superviser

;

3 The superviser decides whether the request is acceptable ornot

:

the reservation is refused → the vehicle slows down to stopbefore the first critical point while continuing to try andobtain a reservation ;the reservation is accepted → the vehicle remains at aconstant speed or tries to place new reservations at higherspeeds.


O. Mehani

Context


Reservations

Simulation

Conclusion

References


1 A vehicle arrives close to the crossroads and requests thecrossroads geometry from the superviser (i.e. the 2D tracesand critical points) ;

2 According to its speed, it builds a reservation request whichis sent back to the superviser.

;3 The superviser decides whether the request is acceptable or

not

:



O. Mehani

Context


Reservations

Simulation

Conclusion

References



2 According to its speed, it builds a reservation request whichis sent back to the superviser ;

3 The superviser decides whether the request is acceptable ornot.

:



O. Mehani

Context


Reservations

Simulation

Conclusion

References



2 According to its speed, it builds a reservation request whichis sent back to the superviser ;

3 The superviser decides whether the request is acceptable ornot :



O. Mehani

Context


Reservations

Simulation

Conclusion

References

A reservation systemArchitecture

Two types of actors :

a superviser (i.e. infrastructure) which knows the geometry(traces and critical points) of the crossroads and keepstrack of the reservations ;

communicant vehicles running software agents able to buildand place reservations to the superviser.

Information contained in a reservation item :

the critical point ;

the time when the reservation begins ;

the time when the reservation stops.


O. Mehani

Context


Reservations

Simulation

Conclusion

References

Simulation of the algorithmSimplifying assumptions

A screenshot of the simulator.

Several simplifying assumptionsare made in the simulator :

perfect communication.

;

perfect microscopic level

;

homogeneous traffic (i.e.CyberCars only).


O. Mehani

Context


Reservations

Simulation

Conclusion

References




perfect communication ;

perfect microscopic level.

;



O. Mehani

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Reservations

Simulation

Conclusion

References




perfect communication ;

perfect microscopic level ;



O. Mehani

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Reservations

Simulation

Conclusion

References

Simulation of the algorithmQuick example of the simulator

A vehicle arrives andplaces its reservation.


O. Mehani

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A second vehicule arrives butits reservation is not acceptable.


O. Mehani

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The second vehicle has slowed down andnow can place its reservation.


O. Mehani

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The vehicle can go on with its journey,or even accelerate.


O. Mehani

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Simulation

Conclusion

References

Simulation of the algorithm1

Compared results

Time (s) Collisions Vehiclesmin max avg

None 5.28 10.80 6.21 458 422Polling 5.28 92.02 47.37 0 108Reservations 5.28 16.82 9.79 0 134

1100s simulation with 0.02s discrete timestep


O. Mehani

Context


Reservations

Simulation

Conclusion

References

Conclusion

This reservation-based approach gives encouraging results.

But :

parameters need to be adjusted

;

many assumptions which were made at first now have to beremoved

;

the algorithm only takes care of the collision handling in thecrossroads (i.e. not just before or after)

;

the deadlock-freedom of the algorithm still has to beformally proven.


O. Mehani

Context


Reservations

Simulation

Conclusion

References

Conclusion


But :

parameters need to be adjusted.

;

many assumptions which were made at first now have to beremoved

;


;



O. Mehani

Context


Reservations

Simulation

Conclusion

References

Conclusion


But :

parameters need to be adjusted ;

many assumptions which were made at first now have to beremoved.

;


;



O. Mehani

Context


Reservations

Simulation

Conclusion

References

Conclusion


But :


many assumptions which were made at first now have to beremoved ;

the algorithm only takes care of the collision handling in thecrossroads (i.e. not just before or after).

;



O. Mehani

Context


Reservations

Simulation

Conclusion

References

Conclusion


But :


many assumptions which were made at first now have to beremoved ;

the algorithm only takes care of the collision handling in thecrossroads (i.e. not just before or after) ;



O. Mehani

Context


Reservations

Simulation

Conclusion

References

Thanks

Kurt Dresner and Peter Stone.

Multiagent traffic management : A reservation-based intersectioncontrol mechanism.

In The Third International Joint Conference on AutonomousAgents and Multiagent Systems, pages 530–537, New York, NewYork, USA, July 2004.

Kurt Dresner and Peter Stone.

Multiagent traffic management : An improved intersection controlmechanism.

In The Fourth International Joint Conference on AutonomousAgents and Multiagent Systems, pages 471–477, Utrecht, TheNetherlands, July 2005.

Questions ?

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