1. Spline-Based Multi-Level Planning for Autonomous Vehicles
Rahul 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: /s  
01 December 2018

2. Autonomous Vehicles Safety Efficient Driving Comfort Jam Avoidance
Coordination
Comfort

3. Conventional Model
Planning

4. Why not speed lanes? Coordination Highly Diverse Speeds
Highly Diverse Sizes

5. Why not speed lanes?
Single lanes
And if highly crowded

6. 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), (2011).

7. Why not conventional Path Planning?
Pre-known/same time of emergence
Wide spaces around
High mobility/Low Speeds

8. 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: /j.neucom

9. From Literature
Map
Level 1
Level 2
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

10. Multi-Level Planning

11. Results

12. Results – Single Vehicle Scenarios

13. Results – 2 Vehicle Scenarios

14. Results – Multi- Vehicle Scenarios

15. Results - Overtaking

16. Results – Path Following

17. Solution Vehicle to be planned Road/Crossing Map Road Selection Path
Pathway Selection
Pathway Distribution
Trajectory Generation
Vehicle to be planned
Road/Crossing Map
Path
Pathway
Distributed Pathway
Trajectory
Replan
All Vehicle Pathways
All Vehicle Trajectories
Controller

18. Road Selection

19. Separation Maximization
Pathways
Hypothesis 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), (2010).

20. Pathway Selection

21. Pathway Selection Dijkstra’s algorithm cost
ds(Pajk(m2)) = ds(Pajk(m1)) + || end(Pajk(m2)) – end(Pajk(m1)) ||
min_width(Pajk(m2)) = min(width(Pajk(m2)), min_width(Pajk(m1)),wmax)
cost(Pajk(m2)) = ds(Pajk(m2)) + α min_width(Pajk(m2))

22. Coordination and Re-planning
Ri 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.

23. Pathway Distribution
Separation
Pathways

24. Pathway Distribution Pathway Distribution Vehicle 2 (Speed=5)
Overtake
Vehicle 3
(Speed=15)
Pre-preparation

25. Pathway Distribution Prepare yourself early for distribution change
- Pre-preparation
Late change of distribution
- Post-preparation

26. Coordination and Re-planning
Ri 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.

27. Trajectory Generation

28. Trajectory Generation

29. Trajectory Generation
Vehicle 2
(Speed=5)
Vehicle 1
(Speed=5)
Vehicle 3
(Speed=15)

30. Thank You