Spline -Based Multi-Level Planning for Autonomous Vehicles

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:10.1007/s10846-013-9817-7 

2. Autonomous Vehicles

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), 26-37 (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:10.1016/j.neucom.2011.03.006

9. Map Level 1 Level 2 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

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

18. Road Selection

19. Separation Maximization Separation 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), 1231-1237 (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 Riis said to have a higher priority compared to Rr if • Riand Rr are driving in same direction of road and Ri lies ahead of Rr. Or • Riand 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) Vehicle 1 (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 • Rrand 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