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Li , Yu

Yu Li

M.Sc.

Ph.D. Student in co-operation Daimler Truck AG TP/VES VE Trucks - Mechatronische Systeme
Institute of Engineering Geodesy Stuttgart
Faculty 6: Aerospace Engineering and Geodesy

Contact

Geschwister-Scholl-Str. 24D
70174 Stuttgart
Deutschland

Subject

Environment detection with LiDAR sensors
SLAM
Multi-sensor fusion

Publications

Li, Y., & Schwieger, V. (2026). FugSeg: Fast Uncertainty-Aware Ground Segmentation for 3-D Point Cloud. IEEE Transactions on Intelligent Transportation Systems, 1–14. https://doi.org/10.1109/TITS.2026.3682176
- BibTeX
BibTeX
@article{yuli2026fugseg, abstract = {In LiDAR-based environment perception systems, ground segmentation is a key preprocessing step supporting various applications such as mapping and navigation. Although extensively studied, problems such as reflection noise and isolated ground remain challenging. To address these issues, we propose FugSeg, a fast uncertainty-aware ground segmentation method. A polar grid map is adopted as the point cloud representation to ensure generalizability across LiDAR types. Building on that, we develop a within- and cross-segment ground labeling strategy that identifies not only directly visible ground cells but also those that are isolated or occluded. During this process, an adaptive slope is introduced, which incorporates measurement uncertainties to enhance its reliability under complex terrain. Finally, to achieve point-level ground segmentation, a fine-grained ground elevation estimation method is introduced. Throughout the complete workflow, reflection noise is explicitly handled via the proposed noisy ground cells. We conduct comprehensive evaluations on four public datasets covering both structured and unstructured environments. Results show that FugSeg outperforms state-of-the-art non-learning methods, achieving the highest F1 , accuracy, and mIoU across all datasets, while maintaining the fastest runtime (135 Hz and 487 Hz for 64- and 32-layer LiDARs) using a single CPU thread, making it suitable for resource-limited systems. The code will be available at https://github.com/Leo-YuLi/FugSeg}, author = {Li, Yu and Schwieger, Volker}, doi = {10.1109/TITS.2026.3682176}, issn = {1524-9050}, journal = {IEEE Transactions on Intelligent Transportation Systems}, month = {04}, pages = {1--14}, publisher = {IEEE}, title = {FugSeg: Fast Uncertainty-Aware Ground Segmentation for 3-D Point Cloud }, url = {https://ieeexplore.ieee.org/document/11488729}, year = 2026 }
Li, Y., Metzner, M., & Schwieger, V. (2023). Driving Environment Inference from POI of Navigation Map: Fuzzy Logic and Machine Learning Approaches. Sensors, 23, Article 22. https://doi.org/10.3390/s23229156
- BibTeX
BibTeX
@article{li2023driving, abstract = {To adapt vehicle control and plan strategies in a predictive manner, it is usually desired to know the context of a driving environment. This paper aims at efficiently inferring the following five driving environments around vehicle’s vicinity: shopping zone, tourist zone, public station, motor service area, and security zone, whose existences are not necessarily mutually exclusive. To achieve that, we utilize the Point of Interest (POI) data from a navigation map as the semantic clue, and solve the inference task as a multilabel classification problem. Specifically, we first extract all relevant POI objects from a map, then transform these discrete POI objects into numerical POI features. Based on these POI features, we finally predict the occurrence of each driving environment via an inference engine. To calculate representative POI features, a statistical approach is introduced. To composite an inference engine, three inference systems are investigated: fuzzy inference system (FIS), support vector machine (SVM), and multilayer perceptron (MLP). In total, we implement 11 variants of inference engine following two inference strategies: independent and unified inference strategies, and conduct comprehensive evaluation on a manually collected dataset. The result shows that the proposed inference framework generalizes well on different inference systems, where the best overall F1 score 0.8699 is achieved by the MLP-based inference engine following the unified inference strategy, along with the fastest inference time of 0.0002 millisecond per sample. Hence, the generalization ability and efficiency of the proposed inference framework are proved.}, article-number = {9156}, author = {Li, Yu and Metzner, Martin and Schwieger, Volker}, doi = {10.3390/s23229156}, issn = {1424-8220}, journal = {Sensors}, number = 22, title = {Driving Environment Inference from POI of Navigation Map: Fuzzy Logic and Machine Learning Approaches}, url = {https://www.mdpi.com/1424-8220/23/22/9156}, volume = 23, year = 2023 }

Yu Li

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Yu Li

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