BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT DTSTART:20250330T020000 TZOFFSETFROM:+0000 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:BST END:DAYLIGHT BEGIN:STANDARD DTSTART:20251026T010000 TZOFFSETFROM:+0100 TZOFFSETTO:+0000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:GMT END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250805T074137Z UID:477D0DBE-2581-4ECA-9E05-FDEDC18DBEEA DTSTART;TZID=Europe/London:20250613T110000 DTEND;TZID=Europe/London:20250613T120000 DESCRIPTION:Autonomous driving is one of the automotive industry's key mega trends\, with most car manufacturers already incorporating varying levels of autonomy into commercially available vehicles. The primary task of the sensing suite in autonomous vehicles is to provide the most reliable and d ense information about the vehicle's surroundings. To achieve the required sensing performance\, sensors must detect\, localize\, and classify a wid e range of typical objects\, such as vehicles\, pedestrians\, poles\, and guardrails. Autonomous vehicles are equipped with multiple sensors of vari ous modalities: radars\, cameras\, and lidars. However\, lidars are costly \, cameras are sensitive to illumination and weather conditions\, need to be mounted behind optically transparent surfaces\, and lack direct range a nd velocity measurement capabilities. In contrast\, radars offer robustnes s to adverse weather conditions\, are unaffected by lighting changes\, pro vide long-range\, accurate measurements\, and can be installed behind nont ransparent fascia.\n\nThe unique nature of automotive radar scenarios requ ires developing new signal-processing approaches beyond the classical mili tary radar concepts. The redefinition of vehicular radar tasks and new per formance demands offer a fertile ground for innovative signal processing t echniques.\n\nThis lecture begins by outlining the radar performance requi rements critical for active safety and autonomous driving and the associat ed challenges. It then reviews current trends in automotive radar technolo gy and highlights the advantages of radar over other sensing modalities. T he lecture explores propagation phenomena encountered in automotive radar and the radar concepts developed to address them. The radar processing cha in will be discussed\, including range and Doppler estimation\, beamformin g\, detection\, angle-of-arrival migration\, tracking\, and clustering. MI MO and cognitive radar approaches for automotive applications will be cove red\, followed by radar interference mitigation and sensor fusion discussi ons. Finally\, open research questions are presented to inspire future dev elopments in the field.\n\nCo-sponsored by: Dr Fatemeh Norouzian - Univers ity of Birmingham\, UK\n\nRoom: Room 229\, Bldg: School of Engineering\, U niversity of Birmingam\, Birmingham\, England\, United Kingdom\, B15 2TT\, Virtual: https://events.vtools.ieee.org/m/485113 LOCATION:Room: Room 229\, Bldg: School of Engineering\, University of Birmi ngam\, Birmingham\, England\, United Kingdom\, B15 2TT\, Virtual: https:// events.vtools.ieee.org/m/485113 ORGANIZER:f.norouzian@bham.ac.uk SEQUENCE:49 SUMMARY:Automotive Radars Principles URL;VALUE=URI:https://events.vtools.ieee.org/m/485113 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="text-align: justi fy\;"><span style="mso-fareast-font-family: 'Times New Roman'\; color: bla ck\;">Autonomous driving is one of the automotive industry's key megatrend s\, with most car manufacturers already incorporating varying levels of au tonomy into commercially available vehicles. The primary task of the sensi ng suite in autonomous vehicles is to provide the most reliable and dense information about the vehicle's surroundings. To achieve the required sens ing performance\, sensors must detect\, localize\, and classify a wide ran ge of typical objects\, such as vehicles\, pedestrians\, poles\, and guard rails. Autonomous vehicles are equipped with multiple sensors of various m odalities: radars\, cameras\, and lidars. However\, lidars are costly\, ca meras are sensitive to illumination and weather conditions\, need to be mo unted behind optically transparent surfaces\, and lack direct range and ve locity measurement capabilities. In contrast\, radars offer robustness to adverse weather conditions\, are unaffected by lighting changes\, provide long-range\, accurate measurements\, and can be installed behind nontransp arent fascia.</span></p>\n<p class="MsoNormal" style="text-align: justify\ ;"><span style="mso-fareast-font-family: 'Times New Roman'\; color: black\ ;">The unique nature of automotive radar scenarios requires developing new signal-processing approaches beyond the classical military radar concepts . The redefinition of vehicular radar tasks and new performance demands of fer a fertile ground for innovative signal processing techniques.</span></ p>\n<p class="MsoNormal" style="text-align: justify\; text-indent: 36.0pt\ ;"><span style="mso-fareast-font-family: 'Times New Roman'\; color: black\ ;">This lecture begins by outlining the radar performance requirements cri tical for active safety and autonomous driving and the associated challeng es. It then reviews current trends in automotive radar technology and high lights the advantages of radar over other sensing modalities. The lecture explores propagation phenomena encountered in automotive radar and the rad ar concepts developed to address them. The radar processing chain will be discussed\, including range and Doppler estimation\, beamforming\, detecti on\, angle-of-arrival migration\, tracking\, and clustering. MIMO and cogn itive radar approaches for automotive applications will be covered\, follo wed by radar interference mitigation and sensor fusion discussions. Finall y\, open research questions are presented to inspire future developments i n the field.</span></p> END:VEVENT END:VCALENDAR