Wildfire Emergency Response and Evacuation Framework Using Drones: Phase I [Research Brief]

Details

• Creators:
  Kulhandjian, Hovannes
• Corporate Creators:
  San Jose State University. College of Business. Mineta Transportation Institute
• Corporate Contributors:
  State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Artificial Intelligence Climate Change Data Collection Drones Ecosystems Emergency Response Time Evacuation Infrastructure Real Time Control Remote Sensing Wildfires

  More +
• Publication/ Report Number:
  2446
• Resource Type:
  Brief
• Geographical Coverage:
  United States ; California
• Edition:
  Research Brief
• Corporate Publisher:
  Mineta Transportation Institute
• Abstract:
  This study develops and tests an autonomous drone-based system for wildfire detection, evacuation planning, and search and rescue operations. The drone network features cutting edge technology such as advanced infrared (IR) cameras, visible spectrum imaging, LiDAR sensors, and onboard computing capabilities powered by NVIDIA Jetson AGX Orin processors. Machine learning algorithms analyze environmental data such as temperature, wind speed, and vegetation conditions to predict fire outbreaks and simulate wildfire spread. Real-time evacuation routes are computed using heuristic pathfinding algorithms including A star, Genetic Algorithms, and Ant Colony Optimization, which enable the system to dynamically adapt to evolving conditions. Additionally, drones equipped with AI-based thermal imaging and radar systems can identify victims in low-visibility conditions, sending information about their precise locations to emergency teams for quick response. Preliminary tests were conducted through field experiments to assess system accuracy, responsiveness, and coordination efficiency.
• Format:
  PDF
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:c5696247e1ad9ddabc57c2e845a76d184008f0d0b9f020bcba07e182e8091c87bf815d29542ce49f031814f76a384546d74f4af72deac6c12789994d65bf5746
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/86931/dot_86931_DS1.pdf
• File Type:
  [PDF - 409.16 KB ]