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.

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 ...

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 and citizens, we should instead ask people what they really think.

In 2017, the House of Lords science and technology committee concluded, "There is a clear need for further Government-commissioned social and economic research to weigh the potential human and financial implications of CAV (Connected and Autonomous Vehicles)." But, while investment in self-driving cars currently totals around $80 billion, there is almost no social science exploring public views about what self-driving cars could mean for the future of transport. This proposal is for the world's first major social science project to bring the public voice into the debate on the future of self-driving cars.