2023-05835 - Extension of vehicle behavior clustering to pedestrians and cyclists
Contract type : Internship
Level of qualifications required : Master's or equivalent
Fonction : Internship Research
ContextThe team-project ASTRA is continuation of the project RITS, which were a multidisciplinary project working on robotics for intelligent transportation systems. ASTRA is a partner team with VALEO to research and develop technologies for automated vehicles, from perception to motion planning and decision making.
AssignmentContext:
One of the challenges when testing automated vehicles in simulation is to propose a realistic environment with road users that are independent from the simulation itself and interact with each other. To built such agents two approaches can be taken: one based on knowledge about the environment itself or another based on the data obtained through observation, or both.
But first the data obtained needs to be processed before being used in a data- driven approach. For the observation in intersections made on 1, one needs to cluster the trajectories, with different sizes, into maneuvers (as seen in figure ref{fig:1}), to then classify each behavior existent in a maneuver according to its longitudinal velocity and acceleration (figures ref{fig:2} and ref{fig:3}). For vehicles, this was done applying the k-means algorithm twice, once for the position trace of the trajectory and another, on each detected cluster, on the longitudinal velocity and acceleration.
The distance measure used, since the data points do not have the same size, was the dynamic time warp 2, with a custom initialization function for the kmeans algorithm. There are multiple ways to do it, from using the longest common sub-sequence (LCSS) 3 to using a trained descriptor 4. Currently, the DTW distance measure is one of the best methods (if not the best) to align time series. Then, to define the number of cluster that exists in the data, the gap statistic was used 5, 6.
References:
1 J. Bock, R. Krajewski, T. Moers, S. Runde, L. Vater, and L. Eckstein, “The ind dataset: A drone dataset of naturalistic road user trajectories at german intersections,” in 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2020, pp. 1929–1934.
2 C. A. Ratanamahatana and E. Keogh, “Everything you know about dynamic time warping is wrong,” in Third workshop on mining temporal and sequential data, vol. 32. Citeseer, 2004.
3 M. Y. Choong, L. Angeline, R. K. Yin Chin, K. Beng Yeo, and K. T. Kin Teo, “Modeling of vehicle trajectory clustering based on lcss for traffic pattern extraction,” in 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS). IEEE, 2017, pp. 74–79.
4 S. Choi, J. Kim, and H. Yeo, “Trajgail: Generating urban vehicle trajectories using generative adversarial imitation learning,” Transporta- tion Research Part C: Emerging Technologies, vol. 128, p. 103091, 2021. Online. Available: https: // www. sciencedirect.com/science/article/pii/ S0968090X21001121
5 R. G. Ribeiro and R. Rios, “Temporal gap statistic: A new internal index to validate time series clustering,” Chaos, Solitons & Fractals, vol. 142, p. 110326, 2021. Online. Available: https: // www. sciencedirect.com/science/ article/pii/S0960077920307219
6 R. Tibshirani, G. Walther, and T. Hastie, “Estimating the number of clusters in a data set via the gap statistic,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 63, no. 2, pp. 411–423, 2001. Online. Available: https: // rss.onlinel
Main activitiesWe are looking for one motivated M1 student to accomplish the following objectives:
Theme/Domain : Algorithmics, Computer Algebra and Cryptology Statistics (Big data) (BAP E)
Town/city : Paris
There you can provide a "broad outline" of the collaborator you are looking for what you consider to be necessary and sufficient, and which may combine :
This section enables the more formal list of skills to be completed and 'lightened' (reduced) :
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