{"id":29037,"date":"2018-11-12T17:25:04","date_gmt":"2018-11-12T22:25:04","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/the-limits-of-autonomy\/"},"modified":"2018-11-12T17:25:04","modified_gmt":"2018-11-12T22:25:04","slug":"the-limits-of-autonomy","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-limits-of-autonomy\/","title":{"rendered":"The Limits of Autonomy"},"content":{"rendered":"

The race for self-driving cars began more than a decade ago in the California desert when 11 teams competed in the DARPA Urban Challenge [1].\u00a0<\/span>\u00a0Since then, investors have piled on and invested billions into autonomous vehicles. As of September 2018, $3.5B had been invested in autonomous tech startups [2].\u00a0<\/span>Between 2009 and 2015, Google\/Waymo alone spent $1.1B on autonomous tech [3].<\/span> Much of that has gone to incorporating deep learning, a combination of machine learning and artificial intelligence across the product development lifecycle . Despite large scale investment, Waymo and others face real obstacles before deep learning is good enough for cars to drive themselves on any road, in any conditions, without a human present, otherwise known as level 5 autonomy [4].<\/span><\/p>\n

At Waymo, it all starts with the data. The car relies on a combination of maps and sensors to gather data while algorithms run in the background and help the car maneuver (change lanes, speed up, etc.) [5].\u00a0<\/span> Waymo also constantly experiments with different machine learning models to improve performance. In 2012, Waymo relied on neural networks, whereby a computer \u201clearns to perform some task by analyzing training samples\u201d, which must be labeled by humans [6].<\/span> Surprised by the poor performance, engineers discovered that humans were incorrectly labeling the training data. Instead of trying to solve the fundamental problem that humans make mistakes, they reintroduced traditional machine learning concepts like decision trees to \u201cget the best of both worlds . [7]\u201d<\/span><\/p>\n

Deep learning models are only as good as the data provided, and Waymo needs orders of magnitude more data to continue improving. By virtually testing upwards of $8M miles per day, it can focus on interactions that may not occur too often in the real world (like a human dressed as an animal), identify where algorithms need improvements, and iterate accordingly. \u201cThe cycle that would take us weeks in the early days of the program is now in the order of minutes,\u201d according to Dolgov, a software lead at Waymo [8].<\/span><\/p>\n

Despite these advances, Waymo (and others) face a series of short-term and long-term challenges. In the short-term, autonomous vehicles need to be able to handle inclement weather, ie snow. Snow can either block or confuse the car\u2019s sensors, confusing the models and sending the car into a standstill. In response, Waymo opened a test facility in Michigan in 2016 [9]<\/span> and recently demonstrated that machine learning can \u201cfilter out snow\u201d and \u201csee just what\u2019s on the road. [10]\u201d<\/span><\/p>\n

Snow is indicative of a medium-term problem that Waymo struggles to deal with, specifically that of generalization. To reach level 5 autonomy, cars must be able to identify an infinite number of images and react accordingly. Recent research indicates that conventional deep learning is uniquely bad at this. Small changes to the same image can lead to vastly different predictions [11].<\/span> If some accidents are inherently unpredictable, and autonomous vehicles can\u2019t account for the universe of possible outcomes, then Waymo may not eliminate as many fatalities as they would like the industry to believe [12].<\/span> Waymo continues to attack this problem with brute force, hoping that tens of thousands of simulations can account for most, if not, all of these scenarios [13].<\/span><\/p>\n

In addition to simulating a variety of scenarios, Waymo may need to fundamentally augment its current approach to deep learning. First, it needs to model how human drivers, pedestrians, and cyclists will respond to the presence of self-driving cars. Current simulations assume that human drivers will act the same as they did before, potentially introducing a fatal flaw into the models. If real world inputs are different from the data Waymo trained against, autonomous vehicles may not be as safe as we expect them to be.<\/span><\/p>\n

Second, the cars need to be able to interact with others on the road. Drivers often use non-verbal cues to help other drivers and pedestrians stay safe. Waymo needs to increase its focus on \u201chuman-in-the-loop\u201d interaction and make sure that deep learning can both understand the needs of another party and visually cue accordingly. Others in the industry recognize this difficulty as well. The CEO of Argo.ai, a Waymo competitor, mentioned that \u201cWe must build algorithms that enable our autonomous vehicles to respond to a deeper understanding of the likely behavior of other road users. [14]\u201d<\/span><\/p>\n

While these are solvable problems, a number of open questions remain. How does deep learning account for ethics? If a self-driving car has to choose between an accident involving 5 bystanders versus a single passenger, how should it respond [15]?<\/span> What is the role of government? Should it set and enforce new safety standards? Waymo and others have promised large-scale deployments of autonomous vehicles over the next few years, but they have to deal with serious technical, legal, and ethical challenges before they can get there.<\/span><\/p>\n

(790 words)<\/span><\/p>\n

 <\/p>\n

Bibliograpy<\/span><\/p>\n

[1]\u00a0Voelcker, John. “Autonomous Vehicles Complete DARPA Urban Challenge.” IEEE Spectrum: Technology, Engineering, and Science News. November 01, 2007. Accessed November 12, 2018. https:\/\/spectrum.ieee.org\/transportation\/advanced-cars\/autonomous-vehicles-complete-darpa-urban-challenge.<\/span><\/p>\n

[2]\u00a0“Taking The Wheel: Autonomous Vehicle Tech Grabs Majority Of Auto Tech Deals, Dollars.” CB Insights Research. September 17, 2018. Accessed November 12, 2018. <\/span>https:\/\/www.cbinsights.com\/research\/auto-tech-startup-investment-trends\/<\/span><\/a>. <\/span><\/p>\n

[3]\u00a0Harris, Mark. “Google Has Spent Over $1.1 Billion on Self-Driving Tech.” IEEE Spectrum: Technology, Engineering, and Science News. September 15, 2017. Accessed November 12, 2018. <\/span>https:\/\/spectrum.ieee.org\/cars-that-think\/transportation\/self-driving\/google-has-spent-over-11-billion-on-selfdriving-tech<\/span><\/a>. <\/span><\/p>\n

[4]\u00a0Barab\u00e1s et al 2017 IOP Conf. Ser.: Mater. Sci. Eng. 252 012096. Accessed November 12, 2018. <\/span>http:\/\/iopscience.iop.org\/article\/10.1088\/1757-899X\/252\/1\/012096\/pdf<\/span><\/a><\/p>\n

[5]\u00a0Harris, Mark. “Google Has Spent Over $1.1 Billion on Self-Driving Tech.” IEEE Spectrum: Technology, Engineering, and Science News. September 15, 2017. Accessed November 12, 2018. <\/span>https:\/\/spectrum.ieee.org\/cars-that-think\/transportation\/self-driving\/google-has-spent-over-11-billion-on-selfdriving-tech<\/span><\/a>.<\/span><\/p>\n

[6]\u00a0Hardesty, Larry. “Explained: Neural Networks.” MIT News. April 14, 2017. Accessed November 12, 2018. <\/span>http:\/\/news.mit.edu\/2017\/explained-neural-networks-deep-learning-0414<\/span><\/a>.<\/span><\/p>\n

[7]\u00a0Hawkins, Andrew J. “Inside the Lab Where Waymo Is Building the Brains for Its Driverless Cars.” The Verge. May 09, 2018. Accessed November 12, 2018. <\/span>https:\/\/www.theverge.com\/2018\/5\/9\/17307156\/google-waymo-driverless-cars-deep-learning-neural-net-interview<\/span><\/a>.<\/span><\/p>\n

[8]\u00a0Madrigal, Alexis C. “Inside Waymo’s Secret World for Training Self-Driving Cars.” The Atlantic. August 23, 2017. Accessed November 12, 2018. <\/span>https:\/\/www.theatlantic.com\/technology\/archive\/2017\/08\/inside-waymos-secret-testing-and-simulation-facilities\/537648\/<\/span><\/a>.<\/span><\/p>\n

[9]\u00a0Krafcik, John. “Michigan Is Waymo’s Winter Wonderland \u2013 Waymo \u2013 Medium.” Medium. October 26, 2017. Accessed November 12, 2018. <\/span>https:\/\/medium.com\/waymo\/michigan-is-waymos-winter-wonderland-9b3cffbb9bab<\/span><\/a>. <\/span><\/p>\n

[10]\u00a0Team, Waymo. “Google I\/O Recap: Turning Self-driving Cars from Science Fiction into Reality with the Help of AI.” Medium. May 08, 2018. Accessed November 12, 2018. <\/span>https:\/\/medium.com\/waymo\/google-i-o-recap-turning-self-driving-cars-from-science-fiction-into-reality-with-the-help-of-ai-89dded40c63<\/a>.\u00a0<\/span><\/p>\n

[11]\u00a0Azulay, Aharon & Weiss, Yair. \u201cWhy do deep convolutional networks generalize so poorly to small image transformations?\u201d Arxiv. May, 2018. Accessed November 12, 2018. <\/span>https:\/\/arxiv.org\/pdf\/1805.12177.pdf<\/span><\/a><\/p>\n

[12]\u00a0Brandom, Russell. “Self-driving Cars Are Headed toward an AI Roadblock.” The Verge. July 03, 2018. Accessed November 12, 2018. https:\/\/www.theverge.com\/2018\/7\/3\/17530232\/self-driving-ai-winter-full-autonomy-waymo-tesla-uber<\/a>.<\/p>\n

[13]\u00a0Madrigal, Alexis C. “Inside Waymo’s Secret World for Training Self-Driving Cars.” The Atlantic. August 23, 2017. Accessed November 12, 2018. <\/span>https:\/\/www.theatlantic.com\/technology\/archive\/2017\/08\/inside-waymos-secret-testing-and-simulation-facilities\/537648\/<\/span><\/a>. <\/span><\/p>\n

[14]\u00a0Argo. “A Decade after DARPA: Our View on the State of the Art in Self-Driving Cars.” Medium. October 16, 2017. Accessed November 12, 2018. https:\/\/medium.com\/self-driven\/a-decade-after-darpa-our-view-on-the-state-of-the-art-in-self-driving-cars-3e8698e6afe8<\/a>.<\/p>\n

[15]\u00a0Markoff, John. “Should Your Driverless Car Hit a Pedestrian to Save Your Life?” The New York Times. December 21, 2017. Accessed November 12, 2018.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Don't hold your breath for autonomous vehicles. 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