TY - GEN
T1 - Generation of reference trajectories for safe trajectory planning
AU - Chaulwar, Amit
AU - Botsch, Michael
AU - Utschick, Wolfgang
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Many variants of a sampling-based motion planning algorithm, namely Rapidly-exploring Random Tree, use biased-sampling for faster convergence. One of such recently proposed variant, the Hybrid-Augmented CL-RRT+, uses a predicted predefined template trajectory with a machine learning algorithm as a reference for the biased sampling. Because of the finite number of template trajectories, the convergence time is short only in scenarios where the final trajectory is close to predicted template trajectory. Therefore, a generative model using variational autoencoder for generating many reference trajectories and a 3D-ConvNet regressor for predicting those reference trajectories for critical vehicle traffic-scenarios is proposed in this work. Using this framework, two different safe trajectory planning algorithms, namely GATE and GATE-ARRT+, are presented in this paper. Finally, the simulation results demonstrate the effectiveness of these algorithms for the trajectory planning task in different types of critical vehicle traffic-scenarios.
AB - Many variants of a sampling-based motion planning algorithm, namely Rapidly-exploring Random Tree, use biased-sampling for faster convergence. One of such recently proposed variant, the Hybrid-Augmented CL-RRT+, uses a predicted predefined template trajectory with a machine learning algorithm as a reference for the biased sampling. Because of the finite number of template trajectories, the convergence time is short only in scenarios where the final trajectory is close to predicted template trajectory. Therefore, a generative model using variational autoencoder for generating many reference trajectories and a 3D-ConvNet regressor for predicting those reference trajectories for critical vehicle traffic-scenarios is proposed in this work. Using this framework, two different safe trajectory planning algorithms, namely GATE and GATE-ARRT+, are presented in this paper. Finally, the simulation results demonstrate the effectiveness of these algorithms for the trajectory planning task in different types of critical vehicle traffic-scenarios.
KW - Hybrid machine learning
KW - Safe trajectory planning
KW - Variational autoencoder
UR - http://www.scopus.com/inward/record.url?scp=85054862500&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01418-6_42
DO - 10.1007/978-3-030-01418-6_42
M3 - Conference contribution
AN - SCOPUS:85054862500
SN - 9783030014179
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 423
EP - 434
BT - Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings
A2 - Kurkova, Vera
A2 - Hammer, Barbara
A2 - Manolopoulos, Yannis
A2 - Iliadis, Lazaros
A2 - Maglogiannis, Ilias
PB - Springer Verlag
T2 - 27th International Conference on Artificial Neural Networks, ICANN 2018
Y2 - 4 October 2018 through 7 October 2018
ER -