Automotive Radar and Lidar Systems for Next Generation Driver Assistance Functions
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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.
“A sensor fusion strategy is often employed to avoid any single point of failure [147].”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [147]
The Abstract calls data fusion a common approach to overcoming individual sensor drawbacks, and §2.2 describes combining complementary sensor strengths. Neither passage says fusion is employed to avoid a single point of failure; that specific purpose is unsupported. (LLM, conf=0.84)
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- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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