BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
X-LIC-LOCATION:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20220715T000418Z
LOCATION:3004\, Level 3
DTSTART;TZID=America/Los_Angeles:20220713T105300
DTEND;TZID=America/Los_Angeles:20220713T111500
UID:dac_DAC 2022_sess113_RESEARCH375@linklings.com
SUMMARY:EcoFusion: Energy-Aware Adaptive Sensor Fusion for Efficient Auton
 omous Vehicle Perception
DESCRIPTION:Research Manuscript\n\nEcoFusion: Energy-Aware Adaptive Sensor
  Fusion for Efficient Autonomous Vehicle Perception\n\nMalawade, Mortlock,
  Al Faruque\n\nCurrently, autonomous vehicles (AVs) use multiple sensors, 
 large deep-learning models, and powerful hardware platforms to perceive th
 e environment and navigate safely. However, the high energy demands of the
 se systems can reduce vehicle range significantly. In many contexts, some 
 sensing modalities can negatively impact perception while increasing energ
 y consumption. In this work, we propose EcoFusion: a context- and energy-a
 ware sensor fusion approach that dynamically switches between different se
 nsor combinations to reduce energy consumption without reducing perception
  performance in comparison to both early and late fusion methods. We addit
 ionally propose several context-identification strategies, analyze the ene
 rgy-performance trade-off, and present scenario-specific results.\n\nTopic
 : Embedded Systems\n\nKeyword: Embedded System Design Methodologies
END:VEVENT
END:VCALENDAR