Michael Botsch
Orcid: 0000-0002-0900-1697
According to our database1,
Michael Botsch
authored at least 48 papers
between 2007 and 2023.
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Bibliography
2023
IEEE Trans. Intell. Veh., May, 2023
Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics.
Sensors, 2023
Metric Learning Based Class Specific Experts for Open-Set Recognition of Traffic Participants in Urban Areas Using Infrastructure Sensors.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2023
Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2023
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023
Proceedings of the Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2023
2022
Sensors, 2022
Leibniz Trans. Embed. Syst., 2022
Proceedings of the 2022 IEEE Intelligent Vehicles Symposium, 2022
A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction.
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2022
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2022
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2022
2021
High Precision Outdoor and Indoor Reference State Estimation for Testing Autonomous Vehicles.
Sensors, 2021
Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2021
Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2021
Open-Set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2021
Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space.
Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference, 2021
Interpretable Early Prediction of Lane Changes Using a Constrained Neural Network Architecture.
Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference, 2021
2020
An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2020
Proceedings of the IEEE Intelligent Vehicles Symposium, 2020
Proceedings of the IEEE Intelligent Vehicles Symposium, 2020
Towards Feature Validation in Time to Lane Change Classification using Deep Neural Networks.
Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems, 2020
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2020, 2020
2019
Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance.
Proceedings of the 2019 IEEE Intelligent Vehicles Symposium, 2019
Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification.
Proceedings of the 2019 IEEE Intelligent Vehicles Symposium, 2019
Interpretable Feature Generation using Deep Neural Networks and its Application to Lane Change Detection.
Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, 2019
Proceedings of the 2019 International Conference on Indoor Positioning and Indoor Navigation, 2019
Efficient Hybrid Machine Learning Algorithm for Trajectory Planning in Critical Traffic-Scenarios.
Proceedings of the 4th International Conference on Intelligent Transportation Engineering, 2019
2018
An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization.
Proceedings of the 21st International Conference on Intelligent Transportation Systems, 2018
Real-Time Crash Severity Estimation with Machine Learning and 2D Mass-Spring-Damper Model.
Proceedings of the 21st International Conference on Intelligent Transportation Systems, 2018
Wireless Communication System for the Validation of Autonomous Driving Functions on Full-Scale Vehicles.
Proceedings of the 2018 IEEE International Conference on Vehicular Electronics and Safety, 2018
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018
2017
A Machine Learning Approach for the Segmentation of Driving Maneuvers and its Application in Autonomous Parking.
J. Artif. Intell. Soft Comput. Res., 2017
A machine learning based biased-sampling approach for planning safe trajectories in complex, dynamic traffic-scenarios.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2017
Predicted-occupancy grids for vehicle safety applications based on autoencoders and the Random Forest algorithm.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017
2016
Probability estimation for Predicted-Occupancy Grids in vehicle safety applications based on machine learning.
Proceedings of the 2016 IEEE Intelligent Vehicles Symposium, 2016
A statistical learning approach for estimating the reliability of crash severity predictions.
Proceedings of the 19th IEEE International Conference on Intelligent Transportation Systems, 2016
A Hybrid Machine Learning Approach for Planning Safe Trajectories in Complex Traffic-Scenarios.
Proceedings of the 15th IEEE International Conference on Machine Learning and Applications, 2016
2015
Supervised Learning via Optimal Control Labeling for Criticality Classification in Vehicle Active Safety.
Proceedings of the IEEE 18th International Conference on Intelligent Transportation Systems, 2015
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015
2010
Complexity reduction using the Random Forest classifier in a collision detection algorithm.
Proceedings of the IEEE Intelligent Vehicles Symposium (IV), 2010
Situation aspect modelling and classification using the Scenario Based Random Forest algorithm for convoy merging situations.
Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, 2010
2008
Construction of interpretable Radial Basis Function classifiers based on the Random Forest kernel.
Proceedings of the International Joint Conference on Neural Networks, 2008
2007
Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2007