TY - JOUR A1 - Sánchez Morales, Eduardo A1 - Dauth, Julian A1 - Huber, Bertold A1 - García Higuera, Andrés A1 - Botsch, Michael T1 - High precision outdoor and indoor reference state estimation for testing autonomous vehicles JF - Sensors N2 - A current trend in automotive research is autonomous driving. For the proper testing and validation of automated driving functions a reference vehicle state is required. Global Navigation Satellite Systems (GNSS) are useful in the automation of the vehicles because of their practicality and accuracy. However, there are situations where the satellite signal is absent or unusable. This research work presents a methodology that addresses those situations, thus largely reducing the dependency of Inertial Navigation Systems (INSs) on the SatNav. The proposed methodology includes (1) a standstill recognition based on machine learning, (2) a detailed mathematical description of the horizontation of inertial measurements, (3) sensor fusion by means of statistical filtering, (4) an outlier detection for correction data, (5) a drift detector, and (6) a novel LiDAR-based Positioning Method (LbPM) for indoor navigation. The robustness and accuracy of the methodology are validated with a state-of-the-art INS with Real-Time Kinematic (RTK) correction data. The results obtained show a great improvement in the accuracy of vehicle state estimation under adverse driving conditions, such as when the correction data is corrupted, when there are extended periods with no correction data and in the case of drifting. The proposed LbPM method achieves an accuracy closely resembling that of a system with RTK. UR - https://doi.org/10.3390/s21041131 KW - machine learning KW - autonomous vehicles KW - Inertial Navigation System KW - Satellite Navigation KW - Real-Time Kinematic KW - indoor navigation KW - reference state Y1 - 2021 UR - https://doi.org/10.3390/s21041131 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10932 SN - 1424-8220 N1 - "This paper is an extended version of our paper published in : Sánchez Morales, E.; Botsch, M.; Huber, B.; García Higuera, A. High precision indoor positioning by means of LiDAR. In Proceedings of the 2019 DGON Inertial Sensors and Systems (ISS), Braunschweig, Germany, 10–11 September 2019." VL - 21 IS - 4 PB - MDPI CY - Basel ER - TY - JOUR A1 - Flores Fernández, Alberto A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - Facchi, Christian A1 - García Higuera, Andrés T1 - Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics JF - Sensors N2 - A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167 kmh (0.046 ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863 kmh (0.24 ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments. UR - https://doi.org/10.3390/s23010159 KW - On-Board Diagnostics KW - Machine Learning KW - Transformer Neural Network KW - Autonomous Driving KW - ADAS KW - Inertial Navigation Systems Y1 - 2022 UR - https://doi.org/10.3390/s23010159 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-30464 SN - 1424-8220 VL - 23 IS - 1 PB - MDPI CY - Basel ER - TY - CHAP A1 - Sánchez Morales, Eduardo A1 - Kruber, Friedrich A1 - Botsch, Michael A1 - Huber, Bertold A1 - García Higuera, Andres T1 - Accuracy Characterization of the Vehicle State Estimation from Aerial Imagery T2 - 2020 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV47402.2020.9304705 Y1 - 2021 UR - https://doi.org/10.1109/IV47402.2020.9304705 SN - 978-1-7281-6673-5 SP - 2081 EP - 2088 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - Huber, Bertold A1 - García Higuera, Andrés T1 - High precision indoor navigation for autonomous vehicles T2 - 2019 International Conference on Indoor Positioning and Indoor Navigation UR - https://doi.org/10.1109/IPIN.2019.8911780 KW - Sensors KW - Automobiles KW - State estimation KW - Autonomous vehicles KW - Testing KW - Receivers KW - Discrete Fourier transforms Y1 - 2019 UR - https://doi.org/10.1109/IPIN.2019.8911780 SN - 978-1-7281-1788-1 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Flores Fernández, Alberto A1 - Wurst, Jonas A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - Facchi, Christian A1 - García Higuera, Andrés T1 - Probabilistic Traffic Motion Labeling for Multi-Modal Vehicle Route Prediction JF - Sensors N2 - The prediction of the motion of traffic participants is a crucial aspect for the research and development of Automated Driving Systems (ADSs). Recent approaches are based on multi-modal motion prediction, which requires the assignment of a probability score to each of the multiple predicted motion hypotheses. However, there is a lack of ground truth for this probability score in the existing datasets. This implies that current Machine Learning (ML) models evaluate the multiple predictions by comparing them with the single real trajectory labeled in the dataset. In this work, a novel data-based method named Probabilistic Traffic Motion Labeling (PROMOTING) is introduced in order to (a) generate probable future routes and (b) estimate their probabilities. PROMOTING is presented with the focus on urban intersections. The generation of probable future routes is (a) based on a real traffic dataset and consists of two steps: first, a clustering of intersections with similar road topology, and second, a clustering of similar routes that are driven in each cluster from the first step. The estimation of the route probabilities is (b) based on a frequentist approach that considers how traffic participants will move in the future given their motion history. PROMOTING is evaluated with the publicly available Lyft database. The results show that PROMOTING is an appropriate approach to estimate the probabilities of the future motion of traffic participants in urban intersections. In this regard, PROMOTING can be used as a labeling approach for the generation of a labeled dataset that provides a probability score for probable future routes. Such a labeled dataset currently does not exist and would be highly valuable for ML approaches with the task of multi-modal motion prediction. The code is made open source. UR - https://doi.org/10.3390/s22124498 KW - PROMOTING KW - automated driving systems KW - autonomous vehicles KW - multi-modal KW - motion prediction KW - route prediction KW - machine learning KW - real traffic data Y1 - 2022 UR - https://doi.org/10.3390/s22124498 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-24341 SN - 1424-8220 VL - 22 IS - 12 PB - MDPI CY - Basel ER - TY - CHAP A1 - Cañas, Valentin A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - García Higuera, Andres T1 - Wireless Communication System for the Validation of Autonomous Driving Functions on Full-Scale Vehicles T2 - 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES.2018.8519492 KW - Vehicle safety KW - wireless communication KW - autonomous driving KW - Intelligent Transport Systems Y1 - 2018 UR - https://doi.org/10.1109/ICVES.2018.8519492 SN - 978-1-5386-3543-8 PB - IEEE CY - Piscataway ER - TY - INPR A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - Huber, Bertold A1 - García Higuera, Andrés T1 - High precision indoor positioning by means of LiDAR UR - https://doi.org/10.48550/arXiv.2005.06798 Y1 - 2020 UR - https://doi.org/10.48550/arXiv.2005.06798 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - Huber, Bertold A1 - García Higuera, Andrés T1 - High precision indoor positioning by means of LiDAR T2 - 2019 DGON Inertial Sensors and Systems (ISS), Proceedings UR - https://doi.org/10.1109/ISS46986.2019.8943731 KW - Laser radar KW - Receivers KW - Automotive engineering KW - Earth KW - Estimation KW - Position measurement KW - Testing Y1 - 2019 UR - https://doi.org/10.1109/ISS46986.2019.8943731 SN - 978-1-7281-1935-9 PB - IEEE CY - Piscataway ER -