Functional Discretization of Space Using Gaussian Processes for Road Intersection Crossing

Transcription

1 Functional Discretization of Space Using Gaussian Processes for Road Intersection Crossing M A T H I E U B A R B I E R 1,2, C H R I S T I A N L A U G I E R 1, O L I V I E R S I M O N I N 1, J A V I E R I B A Ñ E Z - G U Z M Á N 2 1 I N R I A R H Ô N E - A L P E S, C H R O M A T E A M, F R A N C E 2 R E N A U L T S A S, F R A N C E

2 Motivations Intersections are the most dangerous situation of the road network 3384 deaths in France in 2014 Even complex for autonomous vehicles What does an Autonomous vehicle need to understand? Different behaviors and actions regarding the context Dynamic: pedestrians, other cars Static : layout, road signs Perception maps Decision making Actuators 11/1/2016 2

3 Problem definition Multiple overlapping areas Functional discretization Dynamic behaviours Velocity profiles 11/1/2016 3

4 Problem definition How to represent in map a space discretization taking into account dynamic behaviours? Challenges: Learning with various behaviours Scalable and adaptable to any layout Representation within map standard 11/1/2016 4

5 Modeling trajectories : Gaussian processes Trajectories models [Tay and Laugier, 2007] Used to learn velocities profile approaching a stop intersection [Armand et al., 2013] From motion pattern to context [Liu et al., 2015] 11/1/2016 5

6 Discretization framework Determine overlapping areas Data set of trajectories Learning process Predict trajectories pattern Merge and store Determine approaching areas 11/1/2016 6

7 Discretization framework Determine overlapping areas Data set of trajectories Learning process Predict trajectories pattern Merge and store Determine approaching areas 11/1/2016 7

8 Data set of trajectories : Timestamp when the measure has been taken, with the moment when the car is 50m away from the entrance A measure that contains t i =5s,x=1.05,y i =68,h i =pi/3 t 0 =0s,x 0 =2,y 0 =50,h 0 =pi/2 11/1/2016 8

9 Pre-processing for learning step 1 Several trajectories with different duration Solution=>Temporal normalization 11/1/2016 9

10 Pre-processing for learning step 2 Clustering 12 clusters for each possible direction Each trajectory is assigned by looking at its first and last observation 11/1/

11 Discretization framework Determine overlapping areas Data set of trajectories Learning process Predict trajectories pattern Merge and store Determine approaching areas 11/1/

12 Learning process using GP are supposed independent A Gaussian process is a collection of random variables, any finite number of which have a joint Gaussian distribution, [Rasmussen, 2006] GP aims to recover from the data set How: Squared exponential covariance function: 11/1/

13 Learning process Minimized Data Hyper-parameters proposition Log marginal likelihood Hyper-parameters 11/1/

14 Discretization framework Determine overlapping areas Data set of trajectories Learning process Predict trajectories pattern Merge and store Determine approaching areas 11/1/

15 Trajectories pattern from prediction Summation Learned trajectory 11/1/

16 Discretization framework Determine overlapping areas Data set of trajectories Learning process Predict trajectories pattern Merge and store Determine approaching areas 11/1/

17 Determine overlapping areas Traj i P(overlap) Zone processing Condition Traj j 11/1/

18 Discretization framework Determine overlapping areas Data set of trajectories Learning process Predict trajectories pattern Merge and store Determine approaching areas 11/1/

19 Determine approaching areas Condition stop Traj i Condition slow1 Zone merging Zone processing Condition slow2 11/1/

20 Experimentation 11/1/

21 Experimentation Path Simulator: Scaner Use in automotive industry Intersection layout Velocity profiles 11/1/

22 Discretization framework Data set of trajectories Learning process Predict trajectories pattern Determine overlapping areas Determine approaching areas Merge and store 11/1/

23 Experimentation: Simulation lower its speed Continue Stop pass get to top speed 5 11/1/

24 Experimentation : Real data A X-shaped intersection Experimental platform Xsens (IMU+GPS)to record trajectory Video Camera for context 11/1/

25 Results: Real data Simulation & Real-Data results are the same 5 Observation from the front camera Elements availables for the reasoning 11/1/

26 Conclusion Formulation of a discretization of space for decision making Applied to intersections Validation with experimentations Future work Improvement on the thresholds determination Trade off between Simulated and real-data in the dataset 11/1/

27 References [Rasmussen, 2006] Rasmussen, C. E. (2006). Gaussian processes for machine learning. MIT Press. [Darpa, 2007] Darpa (2007). Urban Challenge Route Network Definition File (RNDF) and Mission Data File (MDF) Formats. [Tay and Laugier, 2007] Tay, C. and Laugier, C. (2007). Modelling smooth paths using gaussian processes. In Proc. of the Int. Conf. on Field and Service Robotics, Chamonix, France. voir basilic : [Aoude et al., 2012] Aoude, G., Desaraju, V., Stephens, L., and How, J.(2012). Driver behavior classification at intersections and validation on large naturalistic data set. Intelligent Transportation Systems, IEEE Transactions on, 13(2): [Armand et al., 2013] Armand, A., Filliat, D., and Ibanez-Guzman, J. (2013). Modelling stop intersection approaches using gaussian processes. In ITSC, page xx, Netherlands. [Bender et al., 2014] Bender, P., Ziegler, J., and Stiller, C. (2014). Lanelets: Efficient map representation for autonomous driving. In Intelligent Vehicles Symposium Proceedings, 2014 IEEE, pages [Liu et al., 2015] Liu, W., Kim, S.-W., and Ang, M. H. (2015). Probabilistic road context inference for autonomous vehicles. In 2015 IEEE International Conference on Robotics and Automation (ICRA), pages [national interministériel de la sécurité routiere, 2015] national interministériel de la sécurité routiere, O. (2015). Bilan de l accidentalité de l année Technical report, ONISR. 11/1/

28 To delete Support: Maps Maps as prior information, Different representation and information RNDF [Darpa, 2007], Lanelet [Bender et al., 2014] Crowd sourced maps New information: sematic and dynamic 11/1/

29 Motivations From sensors =>Dynamic: pedestrians, other cars From maps =>Static : layout, road signs 11/1/