{"id":35218,"date":"2018-11-13T20:18:07","date_gmt":"2018-11-14T01:18:07","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/tesla-fully-autonomous-driving-has-never-been-closer\/"},"modified":"2018-11-13T20:18:07","modified_gmt":"2018-11-14T01:18:07","slug":"tesla-fully-autonomous-driving-has-never-been-closer","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/tesla-fully-autonomous-driving-has-never-been-closer\/","title":{"rendered":"Tesla \u2013 Fully Autonomous Driving Has Never Been Closer"},"content":{"rendered":"

Tesla Inc, formerly Tesla Motors, founded and joined by Elon Musk in early 2003, reached a market valuation of more than $65 billion in mid 2017, after only 14 years in operation. It\u2019s innovation in the electric vehicle field allowed it to catch car manufacturing giants General Motors, Ford Motor Company, and Fiat Chrysler, napping at the wheel. Early investments in rechargeable lithium-ion batteries had finally paid off. Now, Tesla looks to the future for the next breakthrough that will shake up the car industry \u2014 self-driving cars.<\/p>\n

Tesla first showcased it\u2019s proprietary Tesla Autopilot software and hardware on October 9th, 2014 [1]. The Autopilot hardware included forward radar, forward and backward looking cameras, and 12 ultrasonic sensors. Early versions of the software and hardware had limited the Autopilot\u2019s use to semi-autonomous driving and parking capabilities. Four years later and Tesla now boasts the largest deployment of robots in the world, with 250,000 currently on the road [2].<\/p>\n

The Tesla fleet operates as a network connected through cloud computing and neural net algorithms: when one car learns something the whole fleet learns it. Data from the fleet is used to create maps showing locations of hazards across roads, increases in traffic speeds, changes in weather conditions, and identifications of lane markings, all while finding the optimal route path and making present-time driving decisions. The complex driving environments that cars operate in along with the unpredictable actions of all other drivers on the road can lead to billions of unique situations that simply could not be accounted for through traditional hard-coding. Tesla\u2019s machine learning algorithms, coupled with the vast amount of data it\u2019s able to feed into these algorithms, allows its software to overcome these challenges.<\/p>\n

\"\"<\/a>
Figure 1: Tesla Autopilot<\/figcaption><\/figure>\n

Tesla’s Strategy<\/strong><\/p>\n

When human lives are at stake, it\u2019s important to get autonomous driving right. To do this, Tesla does not only need the right software, but also the right hardware. Since 2014, Tesla cars have doubled the number of sensors available for each model, installing 8 surround cameras giving 360 degree views, 12 updated ultrasonic sensors, and a forward facing radar [3]. To account for the additional data required to be processed as a result of these sensors, Tesla has heavily invested in developing its own hardware. Tesla brought the design of it\u2019s AI chip in house and is designing it to act as a \u201cneural net accelerator\u201d, delivering up to 10x increase in processing capability [4].<\/p>\n

\"\"<\/a>
Figure 2: Tesla Hardware<\/figcaption><\/figure>\n

In addition, Tesla is also expanding its number of cars on the road through the release of its first mass-market car, the Model 3. Tesla expects to double its number of cars out on the road in the next two years [5]. In 2017 Google found a \u201clogarithmic relationship between performance on vision tasks and the amount of training data available\u201d [6]. Tesla\u2019s mass-market car strategy will effectively double the available data it can use to train its algorithms, setting it up for long term success. In addition, there is continued investment in developing the Tesla Semi and the Tesla Pickup, allowing Tesla to gather information on all major segments of the driving market. Finally, Tesla looks poised to continue investing in its machine learning algorithms through the hiring of Andrej Karpathy, a leading expert in computer vision and deep learning.<\/p>\n

\"\"<\/a>
Figure 3: Tesla Semi<\/figcaption><\/figure>\n

Recommendations<\/strong><\/p>\n

Recently, Tesla has gathered criticism for its refusal to invest in LIDAR systems. LIDAR, unlike typical radar, uses pulses of light to map out the surrounding environment. Many experts theorize that fully autonomous driving is impossible without LIDAR systems [7]. Long term, Tesla should consider investing in this technology as it has been proven to be 25% more accurate than typical radar systems. Second, Tesla should consider a collaboration in designing its AI chips versus bringing development in-house. Tesla has limited experience in processor hardware development and collaborating with a dedicated company such as NVIDIA or AMD can lead to a more powerful processor than one Tesla will build on its own.<\/p>\n

Finally, the biggest hurdle towards autonomous driving might not be the technology itself, but rather the regulations limiting the use of this technology. Critics of Tesla are saying that it\u2019s pushing autonomous driving too fast, sacrificing the safety of its passengers to do so. Fatal accidents because of this could lead to stricter regulations that could impact autonomous driving for decades to come. The recent March 23rd death of Walter Huang while using autopilot mode shows that Tesla needs to re-evaluate its autonomous deployment strategy and have stricter controls and tests in place [8].<\/p>\n

Open Question<\/strong><\/p>\n

With the inevitable rise of autonomous driving, many questions remain regarding on whom responsibility should fall on when accidents occur. Should manufacturers be held responsible for providing a vehicle that was unsafe? Or is it the responsibility of the driver to remain alert at all times during operation of the vehicle?<\/p>\n

Word Count [785]<\/p>\n

References<\/strong><\/p>\n

    \n
  1. Thompson, C. (2017). How Tesla emerged from the brink of bankruptcy to become America’s coolest car company<\/i>. [online] Business Insider. Available at: https:\/\/www.businessinsider.com\/most-important-moments-tesla-history-2017-2#october-9-2014-elon-musk-unveils-teslas-semi-autonomous-self-driving-system-called-autopilot-16 [Accessed 12 Nov. 2018].<\/span><\/span><\/li>\n
  2. Lambert, F. and Lambert, F. (2018). Tesla confirms having produced its 300,000th electric car<\/i>. [online] Electrek. Available at: https:\/\/electrek.co\/2018\/02\/14\/tesla-delivered-300000th-vehicle\/ [Accessed 12 Nov. 2018].<\/span><\/li>\n
  3. Tesla.com. (2018). Autopilot<\/i>. [online] Available at: https:\/\/www.tesla.com\/autopilot [Accessed 12 Nov. 2018].<\/span><\/li>\n
  4. Kumparak, G. (2018). Tesla is building its own AI chips for self-driving cars<\/i>. [online] TechCrunch. Available at: https:\/\/techcrunch.com\/2018\/08\/01\/tesla-is-building-its-own-ai-chips-for-self-driving-cars\/ [Accessed 12 Nov. 2018].<\/span><\/li>\n
  5. Marr, Bernard. 2018. “The Amazing Ways Tesla Is Using Artificial Intelligence and Big Data”. [online] Forbes<\/i>.\u00a0Available at:<\/span> <\/span>https:\/\/www.forbes.com\/sites\/bernardmarr\/2018\/01\/08\/the-amazing-ways-tesla-is-using-artificial-intelligence-and-big-data\/#3ddf8c134270 [Accessed 13 Nov. 2018].<\/span><\/span><\/li>\n
  6. Gupta, A. (2017). Revisiting the Unreasonable Effectiveness of Data<\/i>. [online] Google AI Blog. Available at: https:\/\/ai.googleblog.com\/2017\/07\/revisiting-unreasonable-effectiveness.html [Accessed 13 Nov. 2018].<\/span><\/li>\n
  7. Matousek, M. (2018). Tesla is wrong about one key part of its self-driving car strategy, experts say<\/i>. [online] Business Insider. Available at: https:\/\/www.businessinsider.com\/tesla-wrong-about-self-driving-strategy-needs-lidar-experts-2018-9 [Accessed 13 Nov. 2018].<\/span><\/li>\n
  8. Osborne, M. (2018). Tesla car was on autopilot prior to fatal crash in California, company says<\/i>. [online] ABC News. Available at: https:\/\/abcnews.go.com\/US\/tesla-car-autopilot-prior-fatal-crash-california-company\/story?id=54142891 [Accessed 13 Nov. 2018].<\/span><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Tesla's competitive advantage in self-driving cars.<\/p>\n","protected":false},"author":11824,"featured_media":36661,"comment_status":"open","ping_status":"closed","template":"","categories":[346],"class_list":["post-35218","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-machine-learning","hck-taxonomy-organization-tesla","hck-taxonomy-industry-manufacturing","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nTesla \u2013 Fully Autonomous Driving Has Never Been Closer - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/tesla-fully-autonomous-driving-has-never-been-closer\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Tesla \u2013 Fully Autonomous Driving Has Never Been Closer - 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