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3 datasets found
  1. H

    Data from: Tesla Deaths

    • dataverse.harvard.edu
    • tesladeaths.com
    • +5more
    Updated Jan 10, 2025
    + more versions
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    @elonbachman; @icapulet (2025). Tesla Deaths [Dataset]. http://doi.org/10.7910/DVN/MCNENT
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 10, 2025
    Dataset provided by
    Harvard Dataverse
    Authors
    @elonbachman; @icapulet
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Tesla Deaths is a record of Tesla accidents that involved a driver, occupant, cyclist, motorcyclist, or pedestrian death. We record information about Tesla fatalities that have been reported and as much related crash data as possible such as location of crash, names of deceased. This dataset also tallies claimed and confirmed Tesla autopilot crashes, that is instances when Autopilot was activated during a Tesla crash that resulted in death.Latest version of dataset at Tesla Deaths.{"references": ["Nataprawira, Jason, et al. "PEDESTRIAN DETECTION IN DIFFERENT LIGHTING CONDITIONS USING DEEP NEURAL NETWORKS."", "Gelperin, David. "Simplistic Models Considered Harmful.""]}Regularly updated at tesladeaths.com; version hosted on Zenodo will be updated periodically.

  2. T

    Tesla Fire

    • tesla-fire.com
    • dataverse.harvard.edu
    • +3more
    csv
    Updated Feb 19, 2024
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    I Capulet (2024). Tesla Fire [Dataset]. http://doi.org/10.5281/zenodo.5520568
    Explore at:
    csvAvailable download formats
    Dataset updated
    Feb 19, 2024
    Dataset provided by
    TSLAQ
    Authors
    I Capulet
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Time period covered
    Apr 2, 2013 - Present
    Variables measured
    fires
    Description

    A digital record of all Tesla fires - including cars and other products, e.g. Tesla MegaPacks - that are corroborated by news articles or confirmed primary sources. Latest version hosted at https://www.tesla-fire.com.

  3. c

    Driverless Futures: A Survey of Public Attitudes, 2021-2022

    • datacatalogue.cessda.eu
    • beta.ukdataservice.ac.uk
    Updated Mar 2, 2025
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    Stilgoe, J (2025). Driverless Futures: A Survey of Public Attitudes, 2021-2022 [Dataset]. http://doi.org/10.5255/UKDA-SN-857630
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    Dataset updated
    Mar 2, 2025
    Dataset provided by
    UCL
    Authors
    Stilgoe, J
    Time period covered
    Nov 1, 2021 - Mar 30, 2022
    Area covered
    United Kingdom, United States
    Variables measured
    Individual
    Measurement technique
    Sampling methodologyRespondents to the survey were recruited via Qualtrics, the same company that hosts the web platform in which we set up the questionnaire. Qualtrics provides respondents through a selection of industry partners who curate survey panels. Such panels comprise members of the public who have signed up to take part in surveys, usually in exchange for modest compensation in the form of vouchers that can be redeemed for cash or in high street or online shops. These companies go to considerable lengths to maximise the numbers of people they have on their panels, and their diversity in terms of socio-demographic and consumption characteristics. While any resulting sample from their database cannot be thought of as a strict probability sample of the general public, the efforts at maximising variability (known in survey research as ‘indirect approximation’) go some way towards addressing worries about biases that may be present in the sample as a result of the way people are recruited.During the sampling process, nested hard quotas were applied to try to ensure that we obtained a roughly even split of males and females within each age bracket, and a distribution of age that reflects that of the UK population. There were some imbalances in the gender splits across age bands in the sample: fewer younger men than women, and more older men than women. We calculated a weight to adjust these proportions to more closely match their population counterparts, and applied it to all of the results reported in this document, apart from the demographic variables documented in this section. Population statistics for gender are not available for the ‘other’ category, so in our weighting variable we assigned those cases a weight of 1, and adjusted the proportions of males and females accordingly. The mean weighting value was 1.1178, median 0.9897, minimum 0.5895, maximum 2.0403.Participants were assigned to the different modules in a quasi-random way, employing a least fill strategy to try to ensure that each module contained respondents with a range of socio-demographic characteristics, but also ensuring that the module that they were given was appropriate given their stated travel habits (e.g. questions asking for a cyclist’s perspective were only asked to those who stated that they do cycle).Data CleaningOnline survey participants are adept at completing surveys rapidly. After early pilots we agreed with Qualtrics to apply a threshold completion time of 15 minutes.Qualtrics apply their own data scrubbing to the data. We then applied quality controls to the data andexcluded responses with:• Nonsense responses to the free-text questions• Excessive straightlining (i.e. giving exactly the same answer to each question) on the larger batteries• Consistently speeding on a selection of the survey pages - this was measured by identifying respondents in the lowest deciles for time taken on each of four different pages• Implausible travel modes: respondents who say they used all of the travel modes more than once a week.We excluded 901 respondents (16% of the original sample) through these procedures.AnalysisThe majority of analyses reported here are simple univariate statistics displayed in bar charts, showing the percentages of respondents who gave particular answers. In some places we make reference to correlation statistics to illustrate how answers to pairs of questions are (or are not) related. Those reported in the main text are Pearson correlation coefficients, which are well-known and easily understood: they have a possible range of -1 (indicting a perfect negative linear association – such as would be illustrated with points lying directly on a straight line of best fit in a scatterplot) through 0 (indicating no linear association) to +1 (a perfect positive linear association). For these calculations we exclude ‘don’t know’ responses and treat the question response options as representing continuous, interval-level scales. In a strict sense, the answer options form only ordinal scales, so we would caution against interpreting the Pearson statistics as representing the associations very precisely – they nevertheless provide an accessible indication of how respondents’ answers do (or don’t) vary systematically.Comparative US and 'Expert' surveysUS surveyA similar survey was conducted in the USA with data collection in February and March 2022. Survey text was essentially the same with the following exceptions:• Where appropriate, language was amended to use standard American English terminology: for example, the references to zebra crossings were replaced by 'unsignalled crosswalks'• The survey includes four modules rather than five, eliminating the module using pictures of typical UK street scenes with pedestrians and a cyclist.• A sub-sample of respondents from Maricopa County, Arizona was obtained. The total USsample of 1,890 included 152 of these Maricopa residents.• This Maricopa sub-sample were asked a few questions specifically about the self-drivingvehicles currently deployed in part of the countyExpert' SurveyWe invited developers, other stakeholders and interested observers to complete a shortened version of the survey, also on the Qualtrics platform. Respondents were invited either by direct email contact or by an invitation posted on two different Reddit chat groups. In this report we refer to our respondents as 'experts', reflecting the fact that most have had much more involvement in the technology than our public respondents (see Appendix 12 for details on how this was measured).Although 113 people started the expert survey, a number exited the survey before completing it. The survey was explicitly divided into a short survey of core questions, followed by an invitation to continue to supplementary questions.
    Description

    A set of surveys of public attitudes to issues around self-driving vehicles. Our major sample is from the UK public, with a smaller US sample and a small group of expert respondents for comparison.

    Background The prospect of self-driving vehicles on our roads has attracted considerable public attention, and private and government investment. As vehicles have started to be tested, it has become clear that their interactions with other road users and broader social implications are complex and potentially controversial. The need for governance is becoming clearer. Questions of how safe the technology needs to be, who is likely to benefit and who should be making decisions are becoming ever more important.

    At the end of 2021, we surveyed a sample of 4,860 members of the British public to capture their opinions on self-driving vehicles. The survey was part of Driverless Futures? (driverless-futures.com), a project funded by the UK Economic and Social Research Council, with researchers from University College London, UWE Bristol and City, University of London. Our questions were derived from a set of more than 50 expert interviews and a programme of public dialogue that identified key issues for governance of the technology.

    Most surveys of public attitudes towards self-driving vehicles have addressed respondents as potential users or consumers of the technology. Our survey is different. We address our respondents as citizens, to ask them how they wish to see the future of mobility.

    Our respondents all answered most of the survey questions before being divided into five groups for modules on specific topics relating to self-driving vehicles. On some matters our respondents return a clear consensus; on others, opinions are diverse. The range of sentiments include excitement and scepticism about the benefits, the safety, and the wider impacts of introducing self-driving vehicles.

    We have also fielded this survey in the US (N=1,890) (data collection in February and March 2022) and deployed a shortened version for a convenience sample of 'experts' (N=80).

    In the middle of the afternoon on May 7th, 2016, near Williston, Florida, Joshua Brown joined the long list of fatalities on the world's roads. However, his death was different. He was his car's only occupant but, as far as we know, he was not driving. His car was in 'Autopilot' mode. The technology in his Tesla Model S that was designed to keep him safe failed to see a white truck that was crossing his carriageway against the bright white sky behind it. Brown's Tesla hit the trailer at 74mph, after which it left the road and hit a post. Had the car veered left instead of right, crossing onto the opposite carriageway, the world's first fatal self-driving car crash could have caused a higher death toll and even greater controversy.

    Self-driving cars promise to be one of the most disruptive technologies of the early 21st Century. Enthusiasts for the technology think that it could solve problems such as access to transport for disabled people, traffic jams and hundreds of thousands of deaths on the road each year, most of which are cause by human error. Some companies say they will sell self-driving cars as early as 2018. Governments in the UK and elsewhere see huge potential in securing economic growth and new high-tech jobs for their populations. The UK's Industrial Strategy has prioritised self-driving cars and increased investment in the machine learning technologies that will allow computers to replace humans behind the wheel. Morgan Stanley, an investment bank, forecasts a multi-trillion dollar global market with billions of extra dollars in productivity gains in a 'New Auto Industry Paradigm'. The consultancy firm KPMG calls self-driving cars 'The Next Revolution'.

    The typical approach to a new technology is for society to understand its effects only in hindsight. For self-driving cars, this would be a bad idea. Policymakers, innovators and the public risk sleepwalking into a future in which technology worsens inequality and loses public trust. The history of the car in the 20th Century shows us that, while technologies can have enormous benefits, they can also cause harm and lock society into new ways of living that then prove hard to change. For self-driving cars, the question is whether we can develop a more alert approach to the technology as it is emerging, before it becomes part of our everyday lives. Rather than innovation being 'driverless', we should look for ways in which innovators and policymakers can take responsibility for the futures they help create.

    To maximise the public benefits of self-driving cars, we should scrutinise innovations and policies that are currently underway. The engineering of our future transport systems is too important to be left to engineers alone. There is a need for democratic discussion of the opportunities and uncertainties of self-driving cars. Rather than guessing at the hopes and fears of consumers...

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Share
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Email
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Close
Cite
@elonbachman; @icapulet (2025). Tesla Deaths [Dataset]. http://doi.org/10.7910/DVN/MCNENT

Data from: Tesla Deaths

Related Article
Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Jan 10, 2025
Dataset provided by
Harvard Dataverse
Authors
@elonbachman; @icapulet
License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically

Description

Tesla Deaths is a record of Tesla accidents that involved a driver, occupant, cyclist, motorcyclist, or pedestrian death. We record information about Tesla fatalities that have been reported and as much related crash data as possible such as location of crash, names of deceased. This dataset also tallies claimed and confirmed Tesla autopilot crashes, that is instances when Autopilot was activated during a Tesla crash that resulted in death.Latest version of dataset at Tesla Deaths.{"references": ["Nataprawira, Jason, et al. "PEDESTRIAN DETECTION IN DIFFERENT LIGHTING CONDITIONS USING DEEP NEURAL NETWORKS."", "Gelperin, David. "Simplistic Models Considered Harmful.""]}Regularly updated at tesladeaths.com; version hosted on Zenodo will be updated periodically.