PGRC 2011 rkala.99k.org3 May 2023

Spline-Based Multi-Level Planning for Autonomous VehiclesRahul Kala

The paper was extended and published as: R. Kala, K. Warwick (2013) Multi-Level Planning for Semi-Autonomous Vehicles in Traffic Scenarios based on Separation Maximization, Journal of Intelligent and Robotic Systems, 2013,DOI:10.1007/s10846-013-9817-7 

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Autonomous Vehicles

Safety

Efficient Driving

Jam Avoidance

Coordination

Comfort

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Conventional Model

Planning

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Why not speed lanes?

Highly Diverse Sizes

Highly Diverse Speeds

Coordination

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Why not speed lanes?

Single lanes

And if highly crowded

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Why not speed lanes?“Our model assumes that vehicles travel only along lanes or on certain lane-change path. In California, the practice of “lane-splitting” is legal — motorcycles are free to travel in between cars in adjacent lanes. This occurs in the I-80 dataset, and presents a challenge for our method, which must try to find a path around such obstacles and force each vehicle to precisely follow a single lane.” –Sewall et al. (2011)

J. Sewall, J. van den Berg, M. C. Lin, D. Manocha, D, “Virtualized Traffic: Reconstructing Traffic Flows from Discrete Spatiotemporal Data”, IEEE Transaction on Visualization Computer Graphics, 17(1), 26-37 (2011).

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Why not conventional Path Planning?

• Pre-known/same time of emergence• Wide spaces around• High mobility/Low Speeds

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From Literature

Source: R. Kala, et al., Robotic path planning in static environment using hierarchical multi-neuron heuristic search and probability based fitness, Neurocomputing (2011), doi:10.1016/j.neucom.2011.03.006

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From Literature

Source: R. Kala, et al., Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning, Artificial Intelligence Review, Vol. 33, No. 4, pp 275-306

Map

Level 1

Level 2

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MULTI-LEVEL PLANNING

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Results

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Results – Single Vehicle Scenarios

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Results – 2 Vehicle Scenarios

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Results – Multi- Vehicle Scenarios

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Results - Overtaking

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Results – Path Following

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Solution

Road Selection

Pathway Selection

Pathway Distribution

Trajectory Generation

Vehicle to be planned

Road/Crossing MapPath

Pathway

Distributed Pathway

Trajectory

ReplanAll Vehicle Pathways

All Vehicle Trajectories

Controller

Replan

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Road Selection

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Separation Maximization

Separation

PathwaysHypothesis from: J. R. Alvarez-Sanchez, F. de la Paz Lopez, J. M. C. Troncoso, D. de Santos Sierra, “Reactive navigation in real environments using partial center of area method”, Robotic and Autonomous Systems, 58(12), 1231-1237 (2010).

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Pathway Selection

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Pathway SelectionDijkstra’s algorithm cost

• ds(Pajk(m2)) = ds(Paj

k(m1)) + || end(Pajk(m2)) –

end(Pajk(m1)) ||

• min_width(Pajk(m2)) = min(width(Paj

k(m2)), min_width(Paj

k(m1)),wmax)• cost(Paj

k(m2)) = ds(Pajk(m2)) + α min_width(Paj

k(m2))

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Coordination and Re-planningRi is said to have a higher priority compared to Rr

if

• Ri and Rr are driving in same direction of road and Ri lies ahead of Rr.

Or• Ri and Rr are driving in opposite directions of

road and point of collision lies in left side of complete road.

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Pathway Distribution

Separation

Pathways

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Pathway Distribution

Vehicle 1(Speed=5)

Vehicle 2(Speed=5)

Overtake

Pre-preparation

Vehicle 3(Speed=15)

Pathway Distribution

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Pathway Distribution• Prepare yourself early for distribution change

- Pre-preparation

• Late change of distribution - Post-preparation

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Coordination and Re-planningRi has a higher priority if

• It lies ahead of Rr with Ri and Rr going in same direction

Or • Rr and Ri have different directions.

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Trajectory Generation

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Trajectory Generation

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Trajectory Generation

Vehicle 1(Speed=5)

Vehicle 3(Speed=15)

Vehicle 2(Speed=5)

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THANK YOU