The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Potential of radar for static object classification using deep learning methods
Abstract
A semantic representation of the environment is extremely beneficial in enabling autonomous driving. The objects in the vehicle's surroundings must be recognized and assigned to a proper class. Redundancy is often required for safety-relevant application. This cannot be provided by a single sensor system. This study now demonstrates the potential of radar-based object recognition using deep learning methods.
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Self-citation
Research works printed but not linked: 1
- japkowicz, 2000, The Class Imbalance Problem: Significance and Strategies, Proceedings of the 2000 International Conference on Artificial Intelligence, 111
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Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2972974 · Cited by 1,425
Metadata
- Authors
- Lombacher, J., Hahn, M., Dickmann, J., & Wohler, C.
- Affiliations
- Daimler (Germany) (DE)
- ORCID
- Lombacher, Jakob
- Venue
- 2016 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)
- Publisher
- IEEE
- Published
- 2016-05
- Volume
- 2016
- Pages
- 1-4
- OpenAlex
- W2513079078
- Cited by
- 76
- References
- 21
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 12 · 2022: 9 · 2021: 19 · 2020: 12 · 2019: 11 · 2018: 3 · 2017: 5
- FWCI
- 11.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM) (publishedVersion)
- IEEE Conference Proceedings
- Funders
- European Commission
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Advanced SAR Imaging Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
- Keywords
- radar, autonomous driving, object recognition, deep learning, sensor redundancy, multi-sensor fusion
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