![\"\"](\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2023\/10\/1.png\")
<\/a>A positive virtuous circle also exists, as the cars run on self-driving models would give more data that could be fed back into the model to provide better self-driving capability for the cars. As more and more Tesla cars are driven on the road, Tesla designs a network of “fleet learning,” which refers to the fact that the whole population of Tesla cars could learn from all individual’s newly acquired data, to enhance its data collection and analytic process [2]. All Tesla models could be updated remotely through an internet connection just like the small phones, a feature they named “over-the-air” software updates [3]. Thus, any newly improved self-driving models could be implemented on a mass scale within a short period, giving chances for more iterations of improvements. <\/p>\n\n\n\n
Of course, the self-driving model fed on big data is far from perfect. The data collected from faulty sensors could be noisy or untrue. Customer feedback from self-driving could also be subjected to biases within the drivers. The large amount of data generated from each car (which includes large-size video data) could pose significant burdens to data cleaning and data analysis that require high computational power not only on the cloud server but also within individual cars. <\/p>\n\n\n\n
Big data for safety and a customized experience<\/h2>\n\n\n\n
Tesla models come standard with a set of safety avoidance features that they called “active safety features” [4]. Tesla cars, with the help of AI\/ML models, are able to use their various sensors to identify road hazards, such as icy roads, and notify the drivers about the underlying dangers. Potential collisions predicted by the cars’ advanced custom-made computers would also be forecasted to the drivers for immediate action to mitigate risks [5]. In the emergency case when the drivers are unable to respond in time to avoid collisions, Tesla cars would perform actions automatically to avoid such incidents with features such as “Automatic Emergency Braking,” “Lane Departure Avoidance,” and “Emergency Lane Departure Avoidance” [4].<\/p>\n\n\n\n
All these intelligent safety features require accurate models or algorithms that would not only identify the risks from various forms of data in real-time with high precision and accuracy but also derive the best mitigation plans accordingly. Tesla officially said that they collected data “from millions of Tesla vehicles to learn how collisions happen– and how we (Tesla) can help prevent them in the future” [3]. <\/p>\n\n\n\n
However, similar problems of using big data for ML prediction also exist. Such predictions would have to be in the balance of being conservative vs. being liberal to still be functional, given the context of safety.<\/p>\n\n\n\n
Big data for customized driving experience<\/h2>\n\n\n\n
According to a 2021 report, Tesla has the highest customer satisfaction rate out of most car makers [6]. Tesla enhances its big data strategy to create personalized experiences geared toward each individual customer. With the use of various external or internal sensors, each Tesla vehicle is able to provide a customized driving experience such as the songs played upon starting the car, the mirror positions, air-conditioning volumes, navigation paths designed for the specific time of day, etc. Each car is able to learn from its owners and tailor to the owners’ needs. The dynamic personalization feature of Tesla cars is even able to distinguish different drivers in the car and thus provide a customized experience accordingly [7]. Finally, Tesla’s analytic team obtained data such as text from online forums to find demands and complaints about their vehicles as bases for future improvements [8]. <\/p>\n\n\n\n
Citations<\/h2>\n\n\n\n
[1] Brian Wang, \u201cTesla Full Self Driving, Autopilot and the Driver Assist Market | NextBigFuture.Com,\u201d June 26, 2023, https:\/\/www.nextbigfuture.com\/2023\/06\/tesla-full-self-driving-autopilot-and-the-driver-assist-market.html<\/a>.<\/p>\n\n\n\n [2] Bipin Karki, \u201cBig Data and Analytics in Tesla Inc.,\u201d January 18, 2020, https:\/\/www.linkedin.com\/pulse\/big-data-analytics-tesla-inc-bipin-karki\/<\/a>.<\/p>\n\n\n\n [3] \u201cSafety,\u201d Tesla, accessed October 17, 2023, https:\/\/www.tesla.com\/safety<\/a>.<\/p>\n\n\n\n [4] \u201cModel 3 Earns 5-Star Safety Rating from Euro NCAP,\u201d Tesla, accessed October 17, 2023, https:\/\/www.tesla.com\/blog\/model-3-earns-5-star-safety-rating-euro-ncap<\/a>.<\/p>\n\n\n\n [5] Vaishnavi Yada (Amira), \u201cHow Tesla Is Using AI and Big Data Analytics in Their Self Driving Cars?,\u201d Dare To Be Better<\/em> (blog), February 16, 2023, https:\/\/medium.com\/dare-to-be-better\/how-tesla-is-using-ai-and-big-data-analytics-in-their-self-driving-cars-7072e410c1b8<\/a>.<\/p>\n\n\n\n [6] Jeremy Korzeniewski, \u201cTesla Leads and Infiniti Bleeds in Consumer Reports\u2019 Satisfaction Survey,\u201d Autoblog, accessed October 17, 2023, https:\/\/www.autoblog.com\/2021\/02\/08\/car-truck-owner-satisfaction-survey-consumer-reports\/<\/a>.<\/p>\n\n\n\n [7] Blake Morgan, \u201c3 Ways Tesla Creates A Personalized Customer Experience,\u201d Forbes, accessed October 17, 2023, https:\/\/www.forbes.com\/sites\/blakemorgan\/2021\/05\/10\/3-ways-tesla-creates-a-personalized-customer-experience\/<\/a>.<\/p>\n\n\n\n [8] Vikram Singh, \u201cTesla: A Data Driven Future,\u201d accessed October 17, 2023, https:\/\/d3.harvard.edu\/platform-digit\/submission\/tesla-a-data-driven-future\/<\/a>.<\/p>\n\n\n\n