Nowadays self driving cars rely on many different sensors to percept the surrounding environment. The most relevant one is the LiDAR, which is exploited for mapping, localization, obstacle detection and so on. All those application can use clustering techniques in order to achieve better results. There exist several state of the art algorithm for clustering, for example K-Means, Hierarchical and DBSCAN, however none of them are compliant to real time requirements. This latter aspect is fundamental for autonomous vehicles, which can be seen as real time systems that need to respect deadlines. Our solution is an optimized clustering algorithm, which exploits parallelism and a clever point selection to massively drop computation times. This solution allows us to perform clustering online while fulfilling our real time needs.

1. Bartoli Luca
San Francisco - 15 May 2019
DBSCAL Clustering
Bartoli Luca - 228618@studenti.unimore.it
Modena - 15/05/2019
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2. Bartoli Luca
San Francisco - 15 May 2019
Environment perception
2
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?
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?
● Principal case in autonomous driving
● Sensors help us to percept the environment near
the car

3. Bartoli Luca
San Francisco - 15 May 2019
Environment perception
3
LIDAR
RADAR
CAMERA
LiDAR
Radar
Camera

4. Bartoli Luca
San Francisco - 15 May 2019
Field of view
4
Data based on the common sensors present on the market
LiDAR Radar Camera

5. Bartoli Luca
San Francisco - 15 May 2019
Distance
5
Data based on the common sensors present on the market
LiDAR Radar Camera

6. Bartoli Luca
San Francisco - 15 May 2019
Night time
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Data based on the common sensors present on the market
LiDAR Radar Camera

7. Bartoli Luca
San Francisco - 15 May 2019
Weather conditions
7
Data based on the common sensors present on the market
LiDAR Radar Camera

8. Bartoli Luca
San Francisco - 15 May 2019
HiPeRT Prototype
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9. Bartoli Luca
San Francisco - 15 May 2019
Real time conditions
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10. Bartoli Luca
San Francisco - 15 May 2019
Real time conditions
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< 40

11. Bartoli Luca
San Francisco - 15 May 2019
Clustering
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12. Bartoli Luca
San Francisco - 15 May 2019
Timing performance
12
Dataset with 40’000
AVG points for spin

13. Bartoli Luca
San Francisco - 15 May 2019
Timing performance
13
Dataset with 40’000
AVG points for spin
< 40

14. Bartoli Luca
San Francisco - 15 May 2019
Clustering algorithms
1. K-Means Clustering
2. Hierarchical Clustering
3. DBSCAN
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15. Bartoli Luca
San Francisco - 15 May 2019
K-Means Clustering
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● k-means clustering aims to partition n observations into k clusters.

16. Bartoli Luca
San Francisco - 15 May 2019
K-Means Clustering
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● k-means clustering aims to partition n
observations into k clusters.
● Need to know the number of clusters!

17. Bartoli Luca
San Francisco - 15 May 2019
Hierarchical Clustering
● Each observation starts in its own cluster, and pairs of clusters are merged as
one moves up the hierarchy (Agglomerative)
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18. Bartoli Luca
San Francisco - 15 May 2019
Hierarchical Clustering
● Each observation starts in its own cluster, and pairs of clusters are merged as
one moves up the hierarchy (Agglomerative)
● o(n3
)
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19. Bartoli Luca
San Francisco - 15 May 2019
DBSCAN
● Given a set of points in some space, it groups together points that are closely
packed together.
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20. Bartoli Luca
San Francisco - 15 May 2019
DBSCAN
● Given a set of points in some space, it groups together points that are closely
packed together.
● o(n2
)
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21. Bartoli Luca
San Francisco - 15 May 2019
Clustering summary
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k-means Need to know the number of clusters
Hierarchical Clustering o(n3
)
DBSCAN o(n2
)

22. Bartoli Luca
San Francisco - 15 May 2019
DBSCAN improvement
DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
DBSCAL (Density-Based Spatial Clustering of Applications with LiDAR)
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23. Bartoli Luca
San Francisco - 15 May 2019
DBSCAL
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Compute neighbours for each point
Calculate the cluster

24. Bartoli Luca
San Francisco - 15 May 2019
Compute neighbours
1. Launch a thread for each points
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25. Bartoli Luca
San Francisco - 15 May 2019
Compute neighbours
1. Launch a thread for each points
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26. Bartoli Luca
San Francisco - 15 May 2019
Compute neighbours
1. Launch a thread for each points
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Neighbors search improvement

27. Bartoli Luca
San Francisco - 15 May 2019
Compute neighbours
1. Launch a thread for each points
2. Select MAX 20 nearest points for each
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28. Bartoli Luca
San Francisco - 15 May 2019
Compute neighbours
1. Launch a thread for each points
2. Select MAX 20 nearest points for each
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29. Bartoli Luca
San Francisco - 15 May 2019
Compute neighbours
1. Launch a thread for each points
2. Select MAX 20 nearest points for each
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radius

30. Bartoli Luca
San Francisco - 15 May 2019
Compute neighbours
1. Launch a thread for each points
2. Select MAX 20 nearest points for each
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radius

31. Bartoli Luca
San Francisco - 15 May 2019
Compute neighbours
1. Launch a thread for each points
2. Select MAX 20 nearest points for each
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radius
Less search and memory allocation/transfer

32. Bartoli Luca
San Francisco - 15 May 2019
Compute neighbours
1. Launch a thread for each points
2. Select MAX 20 nearest points for each
3. Not check all points
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33. Bartoli Luca
San Francisco - 15 May 2019
Compute neighbours
1. Launch a thread for each points
2. Select MAX 20 nearest points for each
3. Not check all points
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34. Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
● Semi-order cloud by horizontal angle
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LIDAR

35. Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
● Semi-order cloud by horizontal angle
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LIDAR

36. Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
● Semi-order cloud by horizontal angle
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LIDAR

37. Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
● Semi-order cloud by horizontal angle
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LIDAR LIDAR

38. Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
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LIDAR LIDAR
● Semi-order cloud by horizontal angle

39. Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
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LIDAR LIDAR
● Semi-order cloud by horizontal angle

40. Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
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LIDAR LIDAR
● Semi-order cloud by horizontal angle

41. Bartoli Luca
San Francisco - 15 May 2019
Lidar pointcloud
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LIDAR LIDAR
● Semi-order cloud by horizontal angle

42. Bartoli Luca
San Francisco - 15 May 2019
Calculate the cluster
● Recursive visit for each neighbours points on CPU
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43. Bartoli Luca
San Francisco - 15 May 2019
Summary
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Compute neighbours for each point
Calculate the cluster
1. Launch a thread for each points (ACC)
2. Select MAX 20 nearest points for each
3. Not check all points
1. Recursive visit for each neighbours points (CPU)

44. Bartoli Luca
San Francisco - 15 May 2019
Timing performance
44
Dataset with 40’000
AVG points for spin

45. Bartoli Luca
San Francisco - 15 May 2019
Tracking 3D
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46. Bartoli Luca
San Francisco - 15 May 2019
Classification
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47. Bartoli Luca
San Francisco - 15 May 2019
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Demo

48. Bartoli Luca
San Francisco - 15 May 2019
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Luca Bartoli - 228618@studenti.unimore.it
Thanks for your attentions
https://hipert.unimore.it/

49. Bartoli Luca
San Francisco - 15 May 2019
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50. Bartoli Luca
San Francisco - 15 May 2019
Environment perception
50
RADAR
CAMERAParameters LIDAR RADAR CAMERA
Range high medium medium
Accuracy high medium low
Night time high high low
Rain, snow medium high low
Distance medium high low
Data based on the common sensors present on the market