TY - CHAP A1 - Nadarajan, Parthasarathy A1 - Botsch, Michael A1 - Sardina, Sebastian T1 - Predicted-occupancy grids for vehicle safety applications based on autoencoders and the Random Forest algorithm T2 - 2017 International Joint Conference on Neural Networks (IJCNN) UR - https://doi.org/10.1109/IJCNN.2017.7965995 KW - Roads KW - Feature extraction KW - Radio frequency KW - Geometry KW - Computational modeling KW - Prediction algorithms KW - Machine learning algorithms Y1 - 2017 UR - https://doi.org/10.1109/IJCNN.2017.7965995 SN - 978-1-5090-6182-2 SN - 2161-4407 SP - 1244 EP - 1251 PB - IEEE CY - Piscataway ER - TY - INPR A1 - Nadarajan, Parthasarathy A1 - Botsch, Michael A1 - Sardina, Sebastian T1 - Predicted-occupancy grids for vehicle safety applications based on autoencoders and the Random Forest algorithm UR - https://doi.org/10.48550/arXiv.2512.12901 Y1 - 2017 UR - https://doi.org/10.48550/arXiv.2512.12901 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Nadarajan, Parthasarathy A1 - Botsch, Michael A1 - Sardina, Sebastian T1 - Continuous Probabilistic Motion Prediction based on Latent Space Interpolation T2 - 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC57777.2023.10422685 Y1 - 2024 UR - https://doi.org/10.1109/ITSC57777.2023.10422685 SN - 979-8-3503-9946-2 SP - 3796 EP - 3803 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Nadarajan, Parthasarathy A1 - Botsch, Michael A1 - Sardina, Sebastian T1 - Machine Learning Architectures for the Estimation of Predicted Occupancy Grids in Road Traffic JF - Journal of Advances in Information Technology N2 - This paper introduces a novel machine learning architecture for an efficient estimation of the probabilistic space-time representation of complex traffic scenarios. A detailed representation of the future traffic scenario is of significant importance for autonomous driving and for all active safety systems. In order to predict the future spacetime representation of the traffic scenario, first the type of traffic scenario is identified and then the machine learning algorithm maps the current state of the scenario to possible future states. The input to the machine learning algorithms is the current state representation of a traffic scenario, termed as the Augmented Occupancy Grid (AOG). The output is the probabilistic space-time representation which includes uncertainties regarding the behaviour of the traffic participants and is termed as the Predicted Occupancy Grid (POG). The novel architecture consists of two Stacked Denoising Autoencoders (SDAs) and a set of Random Forests. It is then compared with the other two existing architectures that comprise of SDAs and DeconvNet. The architectures are validated with the help of simulations and the comparisons are made both in terms of accuracy and computational time. Also, a brief overview on the applications of POGs in the field of active safety is presented. UR - https://doi.org/10.12720/jait.9.1.1-9 KW - Predicted Occupancy Grid KW - Active vehicle safety KW - Autoencoders KW - Random Forest KW - DeconvNet Y1 - 2018 UR - https://doi.org/10.12720/jait.9.1.1-9 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-24653 SN - 1798-2340 VL - 9 IS - 1 SP - 1 EP - 9 PB - Engineering and Technology Publishing CY - Rowland Heights ER - TY - INPR A1 - Nadarajan, Parthasarathy A1 - Botsch, Michael A1 - Sardina, Sebastian T1 - Machine Learning Architectures for the Estimation of Predicted Occupancy Grids in Road Traffic UR - https://doi.org/10.48550/arXiv.2512.12907 Y1 - 2018 UR - https://doi.org/10.48550/arXiv.2512.12907 PB - arXiv CY - Ithaca ER -