{"id":10436,"date":"2016-11-08T06:52:37","date_gmt":"2016-11-08T06:52:37","guid":{"rendered":"http:\/\/revoscience.com\/en\/?p=10436"},"modified":"2016-11-08T06:52:37","modified_gmt":"2016-11-08T06:52:37","slug":"driverless-vehicle-options-now-include-scooters","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/driverless-vehicle-options-now-include-scooters\/","title":{"rendered":"Driverless-vehicle options now include scooters"},"content":{"rendered":"

Self-driving scooter demonstrated at MIT complements autonomous golf carts and city cars.<\/strong><\/em><\/span><\/p>\n

\n

\"An<\/a>
An autonomous mobility scooter and related software were designed by researchers from MIT\u2019s Computer Science and Artificial Intelligence Laboratory (CSAIL), the National University of Singapore, and the Singapore-MIT Alliance for Research and Technology (SMART).
Courtesy of the Autonomous Vehicle Team of the SMART Future of Urban Mobility Project<\/figcaption><\/figure>\n

CAMBRIDGE, Mass.<\/strong> —\u00a0At MIT\u2019s 2016 Open House last spring, more than 100 visitors took rides on an autonomous mobility scooter in a trial of software designed by researchers from MIT\u2019s Computer Science and Artificial Intelligence Laboratory (CSAIL), the National University of Singapore, and the Singapore-MIT Alliance for Research and Technology (SMART).<\/span><\/p>\n

The researchers had previously used the same sensor configuration and software in trials of autonomous\u00a0cars<\/span><\/a>\u00a0and\u00a0golf carts<\/span><\/a>, so the new trial completes the demonstration of a comprehensive autonomous mobility system. A mobility-impaired user could, in principle, use a scooter to get down the hall and through the lobby of an apartment building, take a golf cart across the building\u2019s parking lot, and pick up an autonomous car on the public roads.<\/span><\/p>\n

[pullquote]The scooter trial at MIT also demonstrated the ease with which the researchers could deploy their modular hardware and software system in a new context.\u00a0[\/pullquote]<\/p>\n

The new trial establishes that the researchers\u2019 control algorithms work indoors as well as out. \u201cWe were testing them in tighter spaces,\u201d says Scott Pendleton, a graduate student in mechanical engineering at the National University of Singapore (NUS) and a research fellow at SMART. \u201cOne of the spaces that we tested in was the Infinite Corridor of MIT, which is a very difficult localization problem, being a long corridor without very many distinctive features. You can lose your place along the corridor. But our algorithms proved to work very well in this new environment.\u201d<\/span><\/p>\n

The researchers\u2019 system includes several layers of software: low-level control algorithms that enable a vehicle to respond immediately to changes in its environment, such as a pedestrian darting across its path; route-planning algorithms; localization algorithms that the vehicle uses to determine its location on a map; map-building algorithms that it uses to construct the map in the first place; a scheduling algorithm that allocates fleet resources; and an online booking system that allows users to schedule rides.<\/span><\/p>\n

Uniformity<\/strong><\/span><\/p>\n

Using the same control algorithms for all types of vehicles \u2014 scooters, golf carts, and city cars \u2014 has several advantages. One is that it becomes much more practical to perform reliable analyses of the system\u2019s overall performance.<\/span><\/p>\n

\u201cIf you have a uniform system where all the algorithms are the same, the complexity is much lower than if you have a heterogeneous system where each vehicle does something different,\u201d says Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and one of the project\u2019s leaders. \u201cThat\u2019s useful for verifying that this multilayer complexity is correct.\u201d<\/span><\/p>\n

Furthermore, with software uniformity, information that one vehicle acquires can easily be transferred to another. Before the scooter was shipped to MIT, for instance, it was tested in Singapore, where it used maps that had been created by the autonomous golf cart.<\/span><\/p>\n

Similarly, says Marcelo Ang, an associate professor of mechanical engineering at NUS who co-leads the project with Rus, in ongoing work the researchers are equipping their vehicles with machine-learning systems, so that interactions with the environment will improve the performance of their navigation and control algorithms. \u201cOnce you have a better driver, you can easily transplant that to another vehicle,\u201d says Ang. \u201cThat\u2019s the same across different platforms.\u201d<\/span><\/p>\n

Finally, software uniformity means that the scheduling algorithm has more flexibility in its allocation of system resources. If an autonomous golf cart isn\u2019t available to take a user across a public park, a scooter could fill in; if a city car isn\u2019t available for a short trip on back roads, a golf cart might be.<\/span><\/p>\n

Changing perceptions<\/strong><\/span><\/p>\n

The scooter trial at MIT also demonstrated the ease with which the researchers could deploy their modular hardware and software system in a new context. \u201cIt\u2019s extraordinary to me, because it\u2019s a project that the team conducted in about two months,\u201d Rus says. MIT\u2019s Open House was at the end of April, and \u201cthe scooter didn\u2019t exist on February first,\u201d Rus says.<\/span><\/p>\n

The researchers described the design of the scooter system and the results of the trial in a paper they presented last week at the IEEE International Conference on Intelligent Transportation Systems. Joining Rus, Pendleton, and Ang on the paper are You Hong Eng, who leads the SMART autonomous-vehicle project, and four other researchers from both NUS and SMART.<\/span><\/p>\n

The paper also reports the results of a short user survey that the researchers conducted during the trial. Before riding the scooter, users were asked how safe they considered autonomous vehicles to be, on a scale from one to five; after their rides, they were asked the same question again. Experience with the scooter brought the average safety score up, from 3.5 to 4.6.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

At MIT\u2019s 2016 Open House last spring, more than 100 visitors took rides on an autonomous mobility scooter in a trial of software designed by researchers from MIT\u2019s Computer Science and Artificial Intelligence Laboratory (CSAIL), the National University of Singapore, and the Singapore-MIT Alliance for Research and Technology (SMART).<\/p>\n","protected":false},"author":6,"featured_media":10438,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,22,28],"tags":[],"class_list":["post-10436","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-innovation","category-other","category-techbiz"],"featured_image_urls":{"full":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",543,362,false],"thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1-150x150.jpg",150,150,true],"medium":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1-300x200.jpg",300,200,true],"medium_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",543,362,false],"large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",543,362,false],"1536x1536":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",543,362,false],"2048x2048":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",543,362,false],"ultp_layout_landscape_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",543,362,false],"ultp_layout_landscape":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",543,362,false],"ultp_layout_portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",543,362,false],"ultp_layout_square":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",543,362,false],"newspaper-x-single-post":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",543,362,false],"newspaper-x-recent-post-big":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",540,360,false],"newspaper-x-recent-post-list-image":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",95,63,false],"web-stories-poster-portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",543,362,false],"web-stories-publisher-logo":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",96,64,false],"web-stories-thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2016\/11\/MIT-Auto-Scooter-1_0-1.jpg",150,100,false]},"author_info":{"info":["Amrita Tuladhar"]},"category_info":"Innovation<\/a> Other<\/a> Tech<\/a>","tag_info":"Tech","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/10436","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/comments?post=10436"}],"version-history":[{"count":0,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/10436\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media\/10438"}],"wp:attachment":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media?parent=10436"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/categories?post=10436"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/tags?post=10436"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}