{"id":172,"date":"2019-02-21T23:11:53","date_gmt":"2019-02-21T23:11:53","guid":{"rendered":"http:\/\/mrsdprojects.ri.cmu.edu\/2018teama\/?page_id=172"},"modified":"2020-04-11T18:51:44","modified_gmt":"2020-04-11T18:51:44","slug":"system-requirements","status":"publish","type":"page","link":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teama\/system-requirements\/","title":{"rendered":"System Requirements"},"content":{"rendered":"<p><strong>Mandatory Performance Requirements<\/strong><br \/>\nThe system will&#8230;<br \/>\nM.P.1\u00a0Collect\u00a010 km\u00a0of synthetic sensor data and ground truth from the simulator.<br \/>\nM.P.2\u00a0Detect oncoming vehicles in simulator up to a distance of 100 m in with a mAP of 0.4.<br \/>\nM.P.3\u00a0Detect lane markings with an accuracy of 75% within a maximum offset of 0.5 m.<br \/>\nM.P.4\u00a0Predict oncoming vehicle&#8217;s trajectory 2 second\u00a0into the future with an RMSE of less than 3 m with respect to the ground truth trajectory.<br \/>\nM.P.5\u00a0Predict ego-trajectory 3 seconds into the future with an RMSE of less than 2 meters with respect to the ground truth trajectory.<br \/>\nM.P.6\u00a0Perform tracking and fusion with MOTA (multi-object tracking accuracy) of \u00a060% and MOTP (multi-object tracking precision) of 55%.<br \/>\nM.P.7 Visualize detections with track IDs, occupancy grids with objects and road lanes, and future trajectories of oncoming vehicles at a minimum of 5 FPS.<br \/>\nM.P.8 Perform perception and prediction in real-time at 10 FPS.<br \/>\nM.P.9 Predict the possibility of a head-on collision (small overlap) in simulator with an accuracy of 80% also in 90%\u00a0of the cases we should not detect the false positives.<br \/>\nM.P.10 Plan an optimal and feasible evasive maneuver trajectory for the ego-vehicle within 50ms.<br \/>\nM.P.11 Ego-vehicle tracks the optimal trajectory with an error of less than 1m in Carla.<br \/>\nM.P.12 Ego-vehicle camera detects the position of the other RC car within a tolerance of \u00b150cm of the ground truth position.<br \/>\nM.P.13 Detect the oncoming RC car using sensor fusion within a position tolerance of \u00b130cm.<br \/>\nM.P.14 Detect the oncoming RC car velocity using sensor fusion within a tolerance of 15% of the ground truth RC car velocity.<\/p>\n<p><strong>Mandatory Non-Functional Requirements<\/strong><br \/>\nThe system shall&#8230;<br \/>\nM.N.1\u00a0<span style=\"font-weight: 400\">Collect data (at <\/span>30 FPS<span style=\"font-weight: 400\"> for Point Grey) within a maximum drop rate of <\/span>10% <span style=\"font-weight: 400\">and visualize in Rviz<\/span><br \/>\n<span style=\"font-weight: 400\">M.N.2 \u00a0Warn driver in case of system failure<\/span><br \/>\nM.N.3 Be economically justifiable to develop the system<\/p>\n<p><strong>Desired Performance Requirements<\/strong><br \/>\nThe system will&#8230;<br \/>\nD.P.1 Detect roadside obstacles with an accuracy mAP of 0.3<br \/>\nD.P.2 Detect terrain with an accuracy (IoU) of 70%<br \/>\nD.P.3 Meet detection requirements on roads up to 10\u00b0 incline\/decline<br \/>\nD.P.4 Meet detection requirements on curved roads having a radius of curvature of up to 250 m<br \/>\nD.P.5 Predict the possibility of side-swipes with an accuracy of 60%<br \/>\nD.P.6 Meet detection and prediction requirements at speeds of up to 65 mph in the simulator<\/p>\n<p><strong>Desired Non-Functional Requirements<\/strong><br \/>\nThe system shall&#8230;<br \/>\nD.N.1 Use a secondary RC Car as an on-coming vehicle during demonstration<br \/>\nD.N.2 Meet detection and prediction requirements in low-light conditions<br \/>\nD.N.3 Meet detection and prediction requirements in weather conditions like light snow and rain<br \/>\nD.N.4 Notify police and emergency services in the event of a crash<br \/>\nD.N.5 Have a modular software architecture for easy integration of different sensors<br \/>\nD.N.6 Log potential crash data for continuous system improvements<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Mandatory Performance Requirements The system will&#8230; M.P.1\u00a0Collect\u00a010 km\u00a0of synthetic sensor data and ground truth from the simulator. M.P.2\u00a0Detect oncoming vehicles in simulator [&hellip;]<\/p>\n","protected":false},"author":178,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-172","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teama\/wp-json\/wp\/v2\/pages\/172","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teama\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teama\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teama\/wp-json\/wp\/v2\/users\/178"}],"replies":[{"embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teama\/wp-json\/wp\/v2\/comments?post=172"}],"version-history":[{"count":6,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teama\/wp-json\/wp\/v2\/pages\/172\/revisions"}],"predecessor-version":[{"id":896,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teama\/wp-json\/wp\/v2\/pages\/172\/revisions\/896"}],"wp:attachment":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teama\/wp-json\/wp\/v2\/media?parent=172"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}