{"id":191,"date":"2016-10-20T18:34:59","date_gmt":"2016-10-20T18:34:59","guid":{"rendered":"http:\/\/mrsdprojects.ri.cmu.edu\/2016teama\/?page_id=191"},"modified":"2017-05-13T05:28:53","modified_gmt":"2017-05-13T05:28:53","slug":"test-plan","status":"publish","type":"page","link":"https:\/\/mrsdprojects.ri.cmu.edu\/2016teama\/project-management\/test-plan\/","title":{"rendered":"Test plan"},"content":{"rendered":"
    \n
  1. Fall Semester<\/em><\/li>\n<\/ol>\n

    For the Fall semester, the plan was to finish the hardware part of the system, synchronize the data from stereo camera and radar, then get the data from the sensors. The test conditions including the location, the equipment and the environment required are shown in Table. 1. The complete steps and performance requirements of the FVE are specified in Table. 2.<\/p>\n

    Table 1. Fall testing conditions<\/em><\/p>\n

    Location<\/u><\/strong>: <\/strong>The streets around the CMU campus Equipment<\/u><\/strong>:<\/strong> Test vehicle, Thule roof rack, two PointGrey Grasshopper3 cameras, one Delphi ESR 2.5 radar, camera housings, radar mount<\/p>\n

    Table 2. Test Plan Details<\/p>\n\n\n\n\n\n\n\n
    Experiment:<\/strong><\/td>\nA<\/strong><\/td>\n<\/tr>\n
    Description:<\/strong><\/td>\nVerifying weatherproofing of camera enclosures and radar mount<\/td>\n<\/tr>\n
    \u00a0<\/strong><\/td>\n<\/tr>\n
    Step:<\/strong><\/td>\nStep description<\/strong><\/td>\nSuccess condition<\/strong><\/td>\n<\/tr>\n
    A.1<\/strong><\/td>\nSplash water and blow dust towards the enclosure from different directions.<\/td>\nCamera enclosures both pass IP54 rating. No dust or water enters the enclosures.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n\n
    Experiment:<\/strong><\/td>\nB<\/strong><\/td>\n<\/tr>\n
    Description:<\/strong><\/td>\nVerifying robustness of sensor rack and mounting method<\/td>\n<\/tr>\n
    Step:<\/strong><\/td>\nStep description<\/strong><\/td>\nSuccess condition<\/strong><\/td>\n<\/tr>\n
    B.1<\/strong><\/td>\nFix the rack and sensors on the test vehicle and then measure the relative position of the sensors<\/td>\nThe sensors successfully mount onto the car while the car is stationary.<\/td>\n<\/tr>\n
    B.2<\/strong><\/td>\nDrive around the school for about 20 minutes. Test will include quick turns and stops, as well as driving over cobbled roadways.<\/td>\nSensors do not fall off the car.<\/td>\n<\/tr>\n
    B.3<\/strong><\/td>\nMeasure the relative positions of the sensors after the driving test in step B.2<\/td>\nThe relative position of sensors change by less than 5 mm in any direction.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n\n
    Experiment:<\/strong><\/td>\nC<\/strong><\/td>\n<\/tr>\n
    Description:<\/strong><\/td>\nVerifying stereo vision performance in adverse weather conditions<\/td>\n<\/tr>\n
    Step:<\/strong><\/td>\nStep description<\/strong><\/td>\nSuccess condition<\/strong><\/td>\n<\/tr>\n
    C.1<\/strong><\/td>\nDrive around for 20 minutes in fog.<\/td>\nThe recorded data are not obscured and object detection and tracking is still possible up to at least 15 m. Visualize the stereo vision and shown<\/td>\n<\/tr>\n
    C.2<\/strong><\/td>\nDrive around for 20 minutes in rain.<\/td>\n<\/tr>\n
    C.3<\/strong><\/td>\nDrive around for 20 minutes in snow.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n
    Experiment:<\/strong><\/td>\nD<\/strong><\/td>\n<\/tr>\n
    Description:<\/strong><\/td>\nVerifying sensor synchronization<\/td>\n<\/tr>\n
    Step:<\/strong><\/td>\nStep description<\/strong><\/td>\nSuccess condition<\/strong><\/td>\n<\/tr>\n
    D.1<\/strong><\/td>\nPoint cameras at a stopwatch and trigger them to see if they capture images at the same time.<\/td>\nThe captured images from both cameras show the same time on the stopwatch.<\/td>\n<\/tr>\n
    D.2<\/strong><\/td>\nMount sensors onto the test vehicle and drive around for 20 minutes. Monitor different sets of data collected by the stereo camera and radar and compare the timeline of the data consistently.<\/td>\nData from stereo camera and radar should be synchronized and can be displayed in real-time (less than 100ms delay)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n
    Experiment:<\/strong><\/td>\nE<\/strong><\/td>\n<\/td>\n<\/tr>\n
    Description:<\/strong><\/td>\nTest the object detection algorithm accuracy and speed<\/td>\n<\/td>\n<\/tr>\n
    Step:<\/strong><\/td>\nStep description<\/strong><\/td>\nSuccess condition<\/strong><\/td>\n<\/tr>\n
    E.1<\/td>\nUsing the images captured by stereo cameras from the previous test as input then give a detection and classification results<\/td>\nThe algorithm shall detect and classify pedestrians and vehicles up to 70% accuracy The algorithm shall give the size of the objects with accuracy up to x% The algorithm shall give the results\u00a0 within 200ms<\/td>\n<\/tr>\n
    <\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u00a0<\/em> \u00a02. Spring Semester<\/em>\u00a0<\/u><\/strong> For spring semester, we planned to mainly verify our function requirements \u00a0on the software components. The logistics are shown below, also with initial version of the schedule shown in Table 3. More Details on the updated schedule are shown in the Test Plan in PDF format on the Document page, .<\/p>\n

    Test Locations<\/strong><\/p>\n