4 datasets found
  1. Z

    Data from: Tesla Deaths

    • data.niaid.nih.gov
    • tesladeaths.com
    • +6more
    Updated Mar 10, 2025
    + more versions
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    @elonbachman (2025). Tesla Deaths [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_3378951
    Explore at:
    Dataset updated
    Mar 10, 2025
    Dataset provided by
    @icapulet
    @elonbachman
    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

    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 https://www.tesladeaths.com.

  2. T

    Tesla Fire

    • tesla-fire.com
    • search.dataone.org
    • +2more
    csv
    Updated Feb 19, 2024
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    I Capulet (2024). Tesla Fire [Dataset]. http://doi.org/10.5281/zenodo.5520568
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    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. U

    Development and evaluation of vehicle to pedestrian [V2P] safety...

    • dataverse-staging.rdmc.unc.edu
    pdf
    Updated Nov 20, 2023
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    Mary Cummings; Mary Cummings; Lixiao Huang; Lixiao Huang; Michael Clamann; Michael Clamann; Songpo Li; Songpo Li (2023). Development and evaluation of vehicle to pedestrian [V2P] safety interventions [R7] [Dataset]. http://doi.org/10.15139/S3/JU7U1Z
    Explore at:
    pdf(417853), pdf(940256), pdf(1402772)Available download formats
    Dataset updated
    Nov 20, 2023
    Dataset provided by
    UNC Dataverse
    Authors
    Mary Cummings; Mary Cummings; Lixiao Huang; Lixiao Huang; Michael Clamann; Michael Clamann; Songpo Li; Songpo Li
    License

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

    Description

    Globally, pedestrian deaths account for almost a quarter of all traffic related deaths and are also increasing (World Health Organization, 2018). In the US, pedestrian fatalities now account for approximately 16% of all motor vehicle crash-related deaths (Retting, 2018), with an 81% increase in injuries to distracted pedestrians since 2005 (Nasar & Troye, 2013). These increasing injury and fatality rates are concerning given that cars, in theory, have more safety devices on them today than ever before. Moreover, with increasing worldwide focus on autonomous self-driving vehicles, it is not clear that such advanced technology can account for vulnerable users such as pedestrians. It is also not clear how much pedestrian risk will be increased with the arrival of more automated vehicles and what could be done to mitigate such risks when these cars are more commonplace. This research effort, the first to conduct a controlled experiment of crossing pedestrians in a field setting with smartphone-based alerts, demonstrated that in a group of 30 participants given smartphone aural and visual alerts of varying reliability while engaging in distracted walking, only 2% exhibited a tendency towards unsafe crossings, while 18% tended towards risky crossings. These results parallel similar observational studies. Non-US-born participants, representing half the test population, were statistically more likely to engage in risky crossing behavior despite developing accurate trust models of the alert reliability. This was particularly true for non-US-born participants with higher than average neuroticism personality scores. These results suggest national origin plays an important role in the use of technological interventions meant to promote positive behaviors and solutions effective in one setting may not generalize to other nations. Moreover, technology-focused interventions are currently not producing effective solutions, especially across different nationalities. While the subject pool was small in this study and more research is needed in a larger population, this research suggests design criteria might be elucidated from such use of machine learning classification methods in concert with controlled experiments. In this experiment, whether people stopped at or before approximately two feet from the road’s edge predicted safer crossings. Such a threshold could be critical for the designers of autonomous cars who need to prioritize the tracking of multiple entities in congested environments. Those pedestrians that move, for example, inside two feet with constant or increasing velocity or acceleration can become high priority entities to track. More research is needed to determine such thresholds, including variations due to nationality, road and sidewalk design, and proximity to particularly vulnerable populations, i.e., high school and college campuses with higher numbers of people like to engage in distracted walking. However, given that cars like those from Tesla and Waymo already collect this information at levels researchers never could, allowing non-partisan researchers to access this data and develop safety-based models to be shared across all manufacturers would help prevent future fatalities.

  4. E

    Electric Truck Market Report

    • promarketreports.com
    doc, pdf, ppt
    Updated Jan 21, 2025
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    Pro Market Reports (2025). Electric Truck Market Report [Dataset]. https://www.promarketreports.com/reports/electric-truck-market-1093
    Explore at:
    pdf, ppt, docAvailable download formats
    Dataset updated
    Jan 21, 2025
    Dataset authored and provided by
    Pro Market Reports
    License

    https://www.promarketreports.com/privacy-policyhttps://www.promarketreports.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global electric truck market is projected to reach a market size of $13.63 billion by 2033, expanding at a CAGR of 13.95% from 2025 to 2033. The growth of the market can be attributed to the increasing adoption of electric vehicles, stringent government regulations aimed at reducing carbon emissions, and ongoing advancements in battery technology. Key market drivers include the rising demand for environmentally sustainable transportation solutions, fuel efficiency, and government subsidies and incentives. However, the high initial cost of electric trucks and limited charging infrastructure may act as market restraints. The market is segmented by propulsion type (BEV, PHEV, FCEV), vehicle type (light duty, medium duty, heavy duty), end user (last-mile delivery, long haul transportation, refuse services, field services, distribution services), range (up to 200 miles, above 200 miles), battery capacity (less than 50kWh, 50-250 kWh, above 250 kWh), and region (North America, South America, Europe, Middle East & Africa, Asia Pacific). Key industry players include AB Volvo, Daimler AG, PACCAR Inc., Volkswagen AG, BYD Company Limited, Tesla, Nikola Corporation, Rivian, Volvo Trucks, Scania, and Ford Motor Company, among others. These companies are focusing on product innovation, strategic partnerships, and geographical expansion to gain a competitive edge in the market. North America is expected to hold a significant market share during the forecast period due to the early adoption of electric vehicles in the region. Asia Pacific is anticipated to witness the fastest growth rate, driven by the increasing demand for electric vehicles in China and India. Ongoing technological advancements and government initiatives are expected to fuel the growth of the electric truck market in the coming years. Recent developments include: July 2024: The Volvo Group reported strong profitability in the second quarter of 2024 as demand in many areas continued to decline from the high levels of 2023. After accounting for exchange rate fluctuations, net sales came to SEK 140.2 billion, which was the same as the previous year. With a margin of 13.9% (15.4), the adjusted operating income came to SEK 19.4 billion (21.9). Margin was unfavorably impacted by lower volumes and our increased R&D expenditures, but it was positively impacted by the price hikes we carried out last year. When currency was taken into account, our service business expanded by 5%. The service industry brought in SEK 130.3 billion in sales during a rolling 12-month period, according to President and CEO Martin Lundstedt.. Key drivers for this market are: ROAD ACCIDENT DEATHS, INCREASE IN SALE OF EV AND LUXURY VEHICLES; DRIVER IMPACT ANALYSIS. Potential restraints include: HIGH COST AS COMPARED TO OTHER STEERING SYSTEM, RESTRAINT IMPACT ANALYSIS.

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Click to copy link
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@elonbachman (2025). Tesla Deaths [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_3378951

Data from: Tesla Deaths

Related Article
Explore at:
Dataset updated
Mar 10, 2025
Dataset provided by
@icapulet
@elonbachman
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

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 https://www.tesladeaths.com.

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