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Tesla Deaths (Updated 2023)

An In-depth Look into Driver, Occupant, and Pedestrian Deaths

@kaggle.thedevastator_tesla_accident_fatalities_analysis_and_statistic

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About this Dataset

Tesla Deaths (Updated 2023)

Tesla Deaths

An In-depth Look into Driver, Occupant, and Pedestrian Deaths

By [source]

About this dataset

This dataset reveals an in-depth analysis of tragic Tesla vehicle accidents that have resulted in the death of a driver, occupant, cyclist, or pedestrian. It contains an extensive amount of information related to the fatal incidents including the date and location of each crash, model type involved and if Autopilot was enabled at the time. Every case is given its own unique identifier for easy reference and thorough review. Now is your chance to dive deep into these records to truly understand what happened during those tragic events and how we can prevent them from happening again

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How to use the dataset

This dataset provides a comprehensive overview of the Tesla vehicle accidents that have resulted in fatalities. It includes details on the date and location of each incident, model involved, crash description, fatalities, and Autopilot usage. This dataset can be used to analyze the frequency and locations of these fatal accidents as well as gain valuable insights into potential safety risks associated with driving/operating Tesla vehicles.

To begin your analysis with this dataset, start by reading through the information contained in each column: Case # (unique identifier for each case), Year (year of incident), Date (date of incident), Country (country where the accident occurred), State (state where the accident occurred), Description (description of crash), Model (model of Tesla vehicle involved) Source(source). All columns are mandatory for analysis.

Once you have familiarized yourself with this data set, consider looking at how many fatal accidents there have been over time by creating line graphs to show trends over years or states. You may also decide to review incidents based on geographic location or model type to determine which locations or model types may require further investigation and testing in terms of Tesla's safety features.
Additionally consider using descriptive analytics such as means and medians to determine if certain models are more prone to accidents than others compared against one another; while also exploring if Autopilot feature usage has any correlation to higher rates/ numbers involving fatalities .

Using this data set can help increase awareness about potential safety risk related issues associated with driving/ operating a Tesla vehicle allowing individuals involved production side decisions or investing decisions have a better understanding when entering such fields . We do recommend however that when conducting your analysis , it’s important understand proper ways for handling missing data points so that users can get an accurate picture related current issues surrounding vehicular mistakes involving teslas vehicles

Research Ideas

  • Estimating the safety risk of Autopilot feature usage in different countries and states. By analyzing the differences in fatalities between Tesla vehicles operating with and without Autopilot, researchers can infer risks associated with Autopilot use.
  • Examining the relation between driver / occupant fatalities and Tesla vehicle models over time. Through observation of trends in model-specific fatalities across years, engineers may be able to identify vulnerabilities or safety features that should be improved upon in the next version of a car model.
  • Creating predictive models to assess crash probability per country or state based on uncontrollable factors such as road environment or traffic conditions by analyzing large numbers of reported accidents for which there were no fatalities but had similar characteristics (time of day, weather conditions, speed limit etc). Technological developments such as self-driving cars could potentially benefit from this type of predictive evaluation method to enhance their safety by improving preventive measures ahead of accidents occurring

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Tesla Deaths - Deaths (3).csv

Column name Description
Case # Unique identifier for each case. (String)
Year Year of the accident. (Integer)
Date Date of the accident. (Date)
Country Country where the accident occurred. (String)
State State where the accident occurred. (String)
Description Description of the accident. (String)
Tesla driver Whether the Tesla driver was killed in the accident. (Boolean)
Tesla occupant Whether a Tesla occupant was killed in the accident. (Boolean)
Model Model of the Tesla vehicle involved in the accident. (String)
Source Source of the data. (String)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Tesla Deaths Deaths

@kaggle.thedevastator_tesla_accident_fatalities_analysis_and_statistic.tesla_deaths_deaths
  • 67.53 kB
  • 309 rows
  • 24 columns
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CREATE TABLE tesla_deaths_deaths (
  "case" DOUBLE  -- Case #,
  "year" DOUBLE,
  "date" TIMESTAMP,
  "n__country" VARCHAR  -- Country,
  "n__state" VARCHAR  -- State,
  "n__description" VARCHAR  -- Description,
  "n__deaths" DOUBLE  -- Deaths,
  "n__tesla_driver" VARCHAR  -- Tesla Driver,
  "n__tesla_occupant" VARCHAR  -- Tesla Occupant,
  "n__other_vehicle" VARCHAR  -- Other Vehicle,
  "n__cyclists_peds" VARCHAR  -- Cyclists/ Peds,
  "n__tsla_cycl_peds" VARCHAR  -- TSLA+cycl / Peds,
  "n__model" VARCHAR  -- Model,
  "n__autopilot_claimed" VARCHAR  -- Autopilot Claimed,
  "n__verified_tesla_autopilot_deaths" VARCHAR  -- Verified Tesla Autopilot Deaths,
  "n__verified_tesla_autopilot_deaths_all_deaths_reported_d74c0182" VARCHAR  -- Verified Tesla Autopilot Deaths + All Deaths Reported To NHTSA SGO,
  "unnamed_16" VARCHAR  -- Unnamed: 16,
  "unnamed_17" VARCHAR  -- Unnamed: 17,
  "n__source" VARCHAR  -- Source,
  "n__note" VARCHAR  -- Note,
  "n__deceased_1" VARCHAR  -- Deceased 1,
  "n__deceased_2" VARCHAR  -- Deceased 2,
  "n__deceased_3" VARCHAR  -- Deceased 3,
  "n__deceased_4" VARCHAR  -- Deceased 4
);

Tesla Deaths Deaths 3

@kaggle.thedevastator_tesla_accident_fatalities_analysis_and_statistic.tesla_deaths_deaths_3
  • 44.25 kB
  • 254 rows
  • 21 columns
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CREATE TABLE tesla_deaths_deaths_3 (
  "case" BIGINT  -- Case #,
  "year" BIGINT,
  "date" TIMESTAMP,
  "n__country" VARCHAR  -- Country,
  "n__state" VARCHAR  -- State,
  "n__description" VARCHAR  -- Description,
  "n__deaths" BIGINT  -- Deaths,
  "n__tesla_driver" VARCHAR  -- Tesla Driver,
  "n__tesla_occupant" VARCHAR  -- Tesla Occupant,
  "n__other_vehicle" VARCHAR  -- Other Vehicle,
  "n__cyclists_peds" VARCHAR  -- Cyclists/ Peds,
  "n__tsla_cycl_peds" VARCHAR  -- TSLA+cycl / Peds,
  "n__model" VARCHAR  -- Model,
  "n__autopilot_claimed" VARCHAR  -- AutoPilot Claimed,
  "n__verified_tesla_autopilot_death" VARCHAR  -- Verified Tesla Autopilot Death,
  "n__source" VARCHAR  -- Source,
  "n__note" VARCHAR  -- Note,
  "n__deceased_1" VARCHAR  -- Deceased 1,
  "n__deceased_2" VARCHAR  -- Deceased 2,
  "n__deceased_3" VARCHAR  -- Deceased 3,
  "n__deceased_4" VARCHAR  -- Deceased 4
);

Tesla Deaths Miles

@kaggle.thedevastator_tesla_accident_fatalities_analysis_and_statistic.tesla_deaths_miles
  • 10.83 kB
  • 33 rows
  • 13 columns
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CREATE TABLE tesla_deaths_miles (
  "unnamed_0" VARCHAR  -- Unnamed: 0,
  "miles_driven" VARCHAR,
  "unnamed_2" VARCHAR  -- Unnamed: 2,
  "unnamed_3" VARCHAR  -- Unnamed: 3,
  "unnamed_4" VARCHAR  -- Unnamed: 4,
  "unnamed_5" VARCHAR  -- Unnamed: 5,
  "unnamed_6" VARCHAR  -- Unnamed: 6,
  "unnamed_7" VARCHAR  -- Unnamed: 7,
  "cumulative_deaths" VARCHAR  -- Cumulative Deaths*,
  "unnamed_9" VARCHAR  -- Unnamed: 9,
  "unnamed_10" VARCHAR  -- Unnamed: 10,
  "miles_per_death" VARCHAR,
  "unnamed_12" VARCHAR  -- Unnamed: 12
);

Tesla Deaths Musk Claims

@kaggle.thedevastator_tesla_accident_fatalities_analysis_and_statistic.tesla_deaths_musk_claims
  • 4.7 kB
  • 5 rows
  • 5 columns
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CREATE TABLE tesla_deaths_musk_claims (
  "unnamed_0" VARCHAR  -- Unnamed: 0,
  "unnamed_1" VARCHAR  -- Unnamed: 1,
  "unnamed_2" VARCHAR  -- Unnamed: 2,
  "unnamed_3" VARCHAR  -- Unnamed: 3,
  "unnamed_4" VARCHAR  -- Unnamed: 4
);

Tesla Deaths Sudden Acceleration

@kaggle.thedevastator_tesla_accident_fatalities_analysis_and_statistic.tesla_deaths_sudden_acceleration
  • 74.62 kB
  • 122 rows
  • 7 columns
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CREATE TABLE tesla_deaths_sudden_acceleration (
  "vin" VARCHAR,
  "model" VARCHAR,
  "report_date" VARCHAR,
  "incident_date" VARCHAR,
  "location" VARCHAR,
  "nhtsa_complaint" BIGINT,
  "details" VARCHAR
);

Tesla Deaths Vehicleyears

@kaggle.thedevastator_tesla_accident_fatalities_analysis_and_statistic.tesla_deaths_vehicleyears
  • 55.68 kB
  • 121 rows
  • 70 columns
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CREATE TABLE tesla_deaths_vehicleyears (
  "n_1" VARCHAR  -- 1,
  "unnamed_1" TIMESTAMP  -- Unnamed: 1,
  "unnamed_2" VARCHAR  -- Unnamed: 2,
  "n_1_1" VARCHAR  -- 1.1,
  "n_2" VARCHAR  -- 2,
  "n_3" VARCHAR  -- 3,
  "n_4" VARCHAR  -- 4,
  "n_5" VARCHAR  -- 5,
  "n_6" VARCHAR  -- 6,
  "n_7" VARCHAR  -- 7,
  "n_8" VARCHAR  -- 8,
  "n_9" VARCHAR  -- 9,
  "n_10" VARCHAR  -- 10,
  "n_11" VARCHAR  -- 11,
  "n_12" VARCHAR  -- 12,
  "n_13" VARCHAR  -- 13,
  "n_14" VARCHAR  -- 14,
  "n_15" VARCHAR  -- 15,
  "n_16" VARCHAR  -- 16,
  "n_17" VARCHAR  -- 17,
  "n_18" VARCHAR  -- 18,
  "n_19" VARCHAR  -- 19,
  "n_20" VARCHAR  -- 20,
  "n_21" VARCHAR  -- 21,
  "n_22" VARCHAR  -- 22,
  "n_23" VARCHAR  -- 23,
  "n_24" VARCHAR  -- 24,
  "n_25" VARCHAR  -- 25,
  "n_26" VARCHAR  -- 26,
  "n_27" VARCHAR  -- 27,
  "n_28" VARCHAR  -- 28,
  "n_29" VARCHAR  -- 29,
  "n_30" VARCHAR  -- 30,
  "n_31" VARCHAR  -- 31,
  "unnamed_34" VARCHAR  -- Unnamed: 34,
  "unnamed_35" VARCHAR  -- Unnamed: 35,
  "unnamed_36" VARCHAR  -- Unnamed: 36,
  "unnamed_37" VARCHAR  -- Unnamed: 37,
  "unnamed_38" VARCHAR  -- Unnamed: 38,
  "unnamed_39" VARCHAR  -- Unnamed: 39,
  "unnamed_40" VARCHAR  -- Unnamed: 40,
  "unnamed_41" VARCHAR  -- Unnamed: 41,
  "unnamed_42" VARCHAR  -- Unnamed: 42,
  "unnamed_43" VARCHAR  -- Unnamed: 43,
  "unnamed_44" VARCHAR  -- Unnamed: 44,
  "unnamed_45" VARCHAR  -- Unnamed: 45,
  "unnamed_46" VARCHAR  -- Unnamed: 46,
  "unnamed_47" VARCHAR  -- Unnamed: 47,
  "unnamed_48" VARCHAR  -- Unnamed: 48,
  "unnamed_49" VARCHAR  -- Unnamed: 49,
  "unnamed_50" VARCHAR  -- Unnamed: 50,
  "unnamed_51" VARCHAR  -- Unnamed: 51,
  "unnamed_52" VARCHAR  -- Unnamed: 52,
  "unnamed_53" VARCHAR  -- Unnamed: 53,
  "unnamed_54" VARCHAR  -- Unnamed: 54,
  "unnamed_55" VARCHAR  -- Unnamed: 55,
  "unnamed_56" VARCHAR  -- Unnamed: 56,
  "unnamed_57" VARCHAR  -- Unnamed: 57,
  "unnamed_58" VARCHAR  -- Unnamed: 58,
  "unnamed_59" VARCHAR  -- Unnamed: 59,
  "unnamed_60" VARCHAR  -- Unnamed: 60,
  "unnamed_61" VARCHAR  -- Unnamed: 61,
  "unnamed_62" VARCHAR  -- Unnamed: 62,
  "unnamed_63" VARCHAR  -- Unnamed: 63,
  "unnamed_64" VARCHAR  -- Unnamed: 64,
  "unnamed_65" VARCHAR  -- Unnamed: 65,
  "unnamed_66" VARCHAR  -- Unnamed: 66,
  "unnamed_67" VARCHAR  -- Unnamed: 67,
  "unnamed_68" VARCHAR  -- Unnamed: 68,
  "unnamed_69" VARCHAR  -- Unnamed: 69
);

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