{"id":490,"date":"2020-05-09T18:43:21","date_gmt":"2020-05-09T18:43:21","guid":{"rendered":"http:\/\/mrsdprojects.ri.cmu.edu\/2020teamd\/?p=490"},"modified":"2020-12-19T03:12:18","modified_gmt":"2020-12-19T03:12:18","slug":"user-case","status":"publish","type":"post","link":"https:\/\/mrsdprojects.ri.cmu.edu\/2020teamd\/2020\/05\/09\/user-case\/","title":{"rendered":"Use Case"},"content":{"rendered":"

Ridecell, a ridesharing company with an autonomous fleet system, wants to deploy its service. It wants to add a redundant safety system into its current self-driving infrastructure.\u00a0\u00a0Ridecell\u00a0purchased the GRASP learning-based predictive software package.<\/span><\/p>\n

With the Camera and Lidar sensor suite installed on\u00a0Ridecell\u2019s\u00a0autonomous fleet and the interface GRASP team provided, the tech team found it easy to translate the sensor output to the state representation that\u00a0GRASP needs.<\/span><\/p>\n

\"\" \"\"<\/span><\/p>\n

Figure 1:\u00a0Ridecell\u2019s\u00a0autonomous car with GRASP software on a typical traffic scenario<\/span><\/p>\n

The tech team then upgraded the software stack with GRASP and went for a test drive. GRASP asked the test driver to put in the target destination as soon as it was turned on. With the city map loaded, GRASP offered a route, which was then followed by the\u00a0Ridecell\u2019s\u00a0self-driving solution. Every behavioural decision made by the\u00a0Ridecell\u2019s\u00a0system was passed into GRASP for a safety check. Based on the pre-trained reinforcement learning model, GRASP warns of any risky move attempted by the\u00a0Ridecell\u2019s\u00a0system. After the test drive, the tech team were ensured that there was an additional system which was reliably evaluating the safety of the actions taken by their original system.\u00a0Ridecell\u00a0then decided to add GRASP to its infrastructure for deployment.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Ridecell, a ridesharing company with an autonomous fleet system, wants to deploy its service. It wants to add a redundant safety system into its current self-driving infrastructure.\u00a0\u00a0Ridecell\u00a0purchased the GRASP learning-based predictive software package. With the Camera and Lidar sensor suite installed on\u00a0Ridecell\u2019s\u00a0autonomous fleet and the interface GRASP team provided, the tech team found it easy
+ Read More<\/a><\/p>\n","protected":false},"author":198,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,1],"tags":[],"class_list":["post-490","post","type-post","status-publish","format-standard","hentry","category-design","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2020teamd\/wp-json\/wp\/v2\/posts\/490","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2020teamd\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2020teamd\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2020teamd\/wp-json\/wp\/v2\/users\/198"}],"replies":[{"embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2020teamd\/wp-json\/wp\/v2\/comments?post=490"}],"version-history":[{"count":5,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2020teamd\/wp-json\/wp\/v2\/posts\/490\/revisions"}],"predecessor-version":[{"id":664,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2020teamd\/wp-json\/wp\/v2\/posts\/490\/revisions\/664"}],"wp:attachment":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2020teamd\/wp-json\/wp\/v2\/media?parent=490"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2020teamd\/wp-json\/wp\/v2\/categories?post=490"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2020teamd\/wp-json\/wp\/v2\/tags?post=490"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}