BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT DTSTART:20220313T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20221106T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20220924T013730Z UID:C656999E-D359-4C76-8F39-3F09CA3DC8FD DTSTART;TZID=America/Chicago:20220921T110000 DTEND;TZID=America/Chicago:20220921T120000 DESCRIPTION:The tutorial will focus on sensor and measurement systems for n ew generations of vehicles with driver-assisted/autonomous capability. Thi s is the main trend that is revolutionizing vehicles and mobility of peopl e and goods and is also making smart our cities. The economic and social i mpacts of this application field are huge. Worldwide every year 90 million vehicles are sold\, but 1.25 million people are killed due to a lack of s afety. In the US 3.1 billion gallons of fuel are wasted due to traffic con gestion. Assisted driving and autonomous driving aim at increasing safety\ , improving fuel efficiency and our lifestyle by avoiding traffic congesti on\, at ensuring mobility for elderly and disabled people (inclusivity). T he interest in this research subject is demonstrated by the huge investmen ts of companies like Google\, Intel\, Tesla\, Uber\, Ford\, and GM\, to na me just a few\, and by technology alliances\, e.g. between BMW and Intel\, planning autonomous cars for 2021. A convergence between automotive and I CT/Electronics industries is foreseen in the near future. An example of th is convergence is the 5G Automotive Association http://www.5gaa.org/\, whi ch includes all main car manufacturers\, telecom service providers\, elect ronic industries\, and measurement system providers (Keysight\, Rohde&Schw arz).\n\nThe key enabling technologies for this scenario are the sensing a nd measurement systems\, needed for accurate vehicle positioning and navig ation\, vehicle context-awareness\, obstacle detection\, and collision avo idance\, for driver assistance (enhanced vision\, driver’s attention\, a nd fatigue detection).\n\nThe lecture will be divided into multiple sectio ns.\n\nFirst\, in the Introduction\, innovation and market trends in the f ield of sensor and measurement technologies applied to vehicles and smart mobility systems will be discussed\, focusing on the next generation of dr iver-assisted/autonomous vehicles.\n\nThen\, new Radar and Lidar systems\, appearing on-board vehicles beside an array of imaging cameras\, will be discussed for measurement of obstacle positions\, distance\, and relative speed. A trade-off has to be found between the power and size of active se nsing systems like Radar and Lidar and their maximum measurement range. Mo reover\, in continuous wave Radars\, the limited frequency sweep range and the limited number of TX/RX channels lead to limits for the resolution in distance\, direction of arrival\, and speed measurements. Examples of X-b and mobility surveillance Radar and mm-wave automotive Radar will be provi ded.\n\nOn the other hand\, MOEMS (micro opto-electro mechanical systems)- based scanned systems\, used to reduce the size and cost of Lidars are cau sing distortions that are worsening the accuracy of light-based measuremen ts. Distortions due to fish-eye lenses\, used to enlarge the field of view \, are decreasing the measurement performance of imaging sensors. Techniqu es to mitigate such artifacts will be discussed.\n\nPractical examples of traffic sign recognition systems\, road sign recognition\, and image mosai cking for an all-around view will be discussed. In addition\, Lidar and im aging cameras suffer from decreased measurement performance in case of har sh operating conditions (e.g. bad weather or light conditions).\n\nNew bio metric sensing and measurement systems will be also reviewed\, such as Rad ar-based contactless heart/breath-rate measurement\, smart steering wheel for skin temperature/galvanic-response measurements\, or heart-rate detect ion\, with the final aim of detecting the driver’s attention or health s tatus.\n\nConcerning onboard sensors for positioning and navigation\, rece nt advances in MEMS accelerometers and gyroscopes will be discussed. A car eful analysis will be carried out about the measurement errors they cause on position and navigation\, due to their bias and random walk output nois e.\n\nFinally\, the lecture will analyze the trend in computing platforms\ , where parallel architectures and machine learning/AI (artificial intelli gence) techniques\, will be exploited to manage in real-time many and hete rogeneous sources of measurements and to take autonomous decisions.\n\nSug gestions for future directions of interest for the I&M society\, and refer ences to recent publications on IMS journals and conferences\, in the fiel d of automated and connected vehicles\, will be provided as a conclusion.\ n\nSpeaker(s): SERGIO\, \n\nVirtual: https://events.vtools.ieee.org/m/3212 58 LOCATION:Virtual: https://events.vtools.ieee.org/m/321258 ORGANIZER:ztaqvi@gmail.com SEQUENCE:3 SUMMARY:Measurement Performance of Sensor Systems Towards Autonomous Vehicl es: GBS INSTRUMENTATION AND MEASUREMENT WEEK\, EVENT #2 of 3 URL;VALUE=URI:https://events.vtools.ieee.org/m/321258 X-ALT-DESC:Description: <br /><p>The tutorial will focus on sensor and meas urement systems for new generations of vehicles with driver-assisted/auton omous capability. This is the main trend that is revolutionizing vehicles and mobility of people and goods and is also making smart our cities. The economic and social impacts of this application field are huge. Worldwide every year 90 million vehicles are sold\, but 1.25 million people are kill ed due to a lack of safety. In the US 3.1 billion gallons of fuel are wast ed due to traffic congestion. Assisted driving and autonomous driving aim at increasing safety\, improving fuel efficiency and our lifestyle by avoi ding traffic congestion\, at ensuring mobility for elderly and disabled pe ople (inclusivity). The interest in this research subject is demonstrated by the huge investments of companies like Google\, Intel\, Tesla\, Uber\, Ford\, and GM\, to name just a few\, and by technology alliances\, e.g. be tween BMW and Intel\, planning autonomous cars for 2021. A convergence bet ween automotive and ICT/Electronics industries is foreseen in the near fut ure. An example of this convergence is the 5G Automotive Association <a hr ef="http://www.5gaa.org/"><strong>http://www.5gaa.org/</strong></a>\, whic h includes all main car manufacturers\, telecom service providers\, electr onic industries\, and measurement system providers (Keysight\, Rohde&amp\; Schwarz).</p>\n<p>The key enabling technologies for this scenario are the sensing and measurement systems\, needed for accurate vehicle positioning and navigation\, vehicle context-awareness\, obstacle detection\, and coll ision avoidance\, for driver assistance (enhanced vision\, driver&rsquo\;s attention\, and fatigue detection).</p>\n<p>The lecture will be divided i nto multiple sections.</p>\n<p>First\, in the Introduction\, innovation an d market trends in the field of sensor and measurement technologies applie d to vehicles and smart mobility systems will be discussed\, focusing on t he next generation of driver-assisted/autonomous vehicles.</p>\n<p>Then\, new Radar and Lidar systems\, appearing on-board vehicles beside an array of imaging cameras\, will be discussed for measurement of obstacle positio ns\, distance\, and relative speed. A trade-off has to be found between th e power and size of active sensing systems like Radar and Lidar and their maximum measurement range. Moreover\, in continuous wave Radars\, the limi ted frequency sweep range and the limited number of TX/RX channels lead to limits for the resolution in distance\, direction of arrival\, and speed measurements. Examples of X-band mobility surveillance Radar and mm-wave a utomotive Radar will be provided.</p>\n<p>On the other hand\, MOEMS (micro opto-electro mechanical systems)-based scanned systems\, used to reduce t he size and cost of Lidars are causing distortions that are worsening the accuracy of light-based measurements. Distortions due to fish-eye lenses\, used to enlarge the field of view\, are decreasing the measurement perfor mance of imaging sensors. Techniques to mitigate such artifacts will be di scussed.</p>\n<p>Practical examples of traffic sign recognition systems\, road sign recognition\, and image mosaicking for an all-around view will b e discussed. In addition\, Lidar and imaging cameras suffer from decreased measurement performance in case of harsh operating conditions (e.g. bad w eather or light conditions).</p>\n<p>New biometric sensing and measurement systems will be also reviewed\, such as Radar-based contactless heart/bre ath-rate measurement\, smart steering wheel for skin temperature/galvanic- response measurements\, or heart-rate detection\, with the final aim of de tecting the driver&rsquo\;s attention or health status.</p>\n<p>Concerning onboard sensors for positioning and navigation\, recent advances in MEMS accelerometers and gyroscopes will be discussed. A careful analysis will b e carried out about the measurement errors they cause on position and navi gation\, due to their bias and random walk output noise.</p>\n<p>Finally\, the lecture will analyze the trend in computing platforms\, where paralle l architectures and machine learning/AI (artificial intelligence) techniqu es\, will be exploited to manage in real-time many and heterogeneous sourc es of measurements and to take autonomous decisions.</p>\n<p>Suggestions f or future directions of interest for the I&amp\;M society\, and references to recent publications on IMS journals and conferences\, in the field of automated and connected vehicles\, will be provided as a conclusion.</p> END:VEVENT END:VCALENDAR