The Google Car: Driving Toward A Better Future?
Open accessAbstract
Googles dramatic ascent and subsequent domination in the past fifteen years of the technology and information industries has financially enabled Google to explore seemingly unrelated projects ranging from Google Mail to the Google Car. In particular, Google has invested a significant amount of resources in the Google Car, an integrated system that allows for the driverless operation of a vehicle. While initial reports indicate that the Google Car driverless automobile will be more safe and efficient than current vehicles, the Google Car is not without its critics. In particular, the existential threat that the car presents to several large industries, including the insurance, health care and construction industries, creates an additional challenge to the success of the Google Car well beyond the standard competitive threats from other established car manufacturers in the automobile industry, which begs the question, Can the Google Car be successful? With so many challenges above and beyond the competitive forces typically threatening long-term profitability, will the Google Car be able to create and sustain a competitive advantage for Google in the driverless car space?
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LiDAR sensors are currently being used by various companies, such as Google, BMW, and Volvo, to develop autonomous vehicles.
“LiDAR sensors are currently being used by various companies, such as Google [60], BMW, and Volvo [61], to develop autonomous vehicles.”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [60]
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- Authors
- Poczter, S. L., & Jankovic, L. M.
- Affiliations
- Cornell University (US)
- ORCID
- Poczter, Sharon L.
- Venue
- Journal of Business Case Studies (JBCS)
- Publisher
- Clute Institute
- Published
- 2013-12-31
- Volume
- 10
- Issue
- 1
- Pages
- 7
- ISSN
- 1555-3353 (ISSN-L)
- 2157-8826
- OpenAlex
- W2177814138
- Cited by
- 106
- References
- 4
- Citations by year
- 2025: 4 · 2024: 7 · 2023: 10 · 2022: 11 · 2021: 11 · 2020: 17 · 2019: 18 · 2018: 11 · 2017: 10 · 2016: 5 · 2015: 2
- FWCI
- 6.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Journal of Business Case Studies (JBCS) (publishedVersion, cc-by)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Business Strategies and Innovation
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Keywords
- autonomous vehicles, competitive advantage, existential risk, automotive industry, insurance industry, health sector, construction industry, Google, business strategy, disruptive innovation
About these checks
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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