The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Automotive Radar and Lidar Systems for Next Generation Driver Assistance Functions
Open accessAbstract
Abstract. Automotive radar and lidar sensors represent key components for next generation driver assistance functions (Jones, 2001). Today, their use is limited to comfort applications in premium segment vehicles although an evolution process towards more safety-oriented functions is taking place. Radar sensors available on the market today suffer from low angular resolution and poor target detection in medium ranges (30 to 60m) over azimuth angles larger than ±30°. In contrast, Lidar sensors show large sensitivity towards environmental influences (e.g. snow, fog, dirt). Both sensor technologies today have a rather high cost level, forbidding their wide-spread usage on mass markets. A common approach to overcome individual sensor drawbacks is the employment of data fusion techniques (Bar-Shalom, 2001). Raw data fusion requires a common, standardized data interface to easily integrate a variety of asynchronous sensor data into a fusion network. Moreover, next generation sensors should be able to dynamically adopt to new situations and should have the ability to work in cooperative sensor environments. As vehicular function development today is being shifted more and more towards virtual prototyping, mathematical sensor models should be available. These models should take into account the sensor's functional principle as well as all typical measurement errors generated by the sensor.
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“A sensor fusion strategy is often employed to avoid any single point of failure [147].”
From A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2020) · cites this paper as [147]
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Citations
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Metadata
- Authors
- Rasshofer, R. H., & Gresser, K.
- Affiliations
- BMW (Germany) (DE)
- Venue
- Advances in Radio Science
- Publisher
- Copernicus GmbH
- Published
- 2005-05-12
- Volume
- 3
- Pages
- 205-209
- ISSN
- 1684-9965 (ISSN-L)
- 1684-9973
- OpenAlex
- W2129577383
- Cited by
- 252
- References
- 8
- Citations by year
- 2026: 8 · 2025: 21 · 2024: 22 · 2023: 17 · 2022: 26 · 2021: 31 · 2020: 33 · 2019: 20 · 2018: 18 · 2017: 21 · 2016: 5 · 2015: 7 · 2014: 4 · 2013: 3 · 2012: 5
- FWCI
- 7.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Advances in radio science (publishedVersion, cc-by-nc-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
- Physical Sciences › Physics and Astronomy › Instrumentation › Advanced Optical Sensing Technologies
- Keywords
- automotive radar, LiDAR, driver assistance systems, sensor fusion, sensor modeling, measurement error, sensor cost, virtual prototyping, vehicle safety
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