Robust Automatic Detection of Traffic Activity

Details

• Creators:
  Hauptmann, Alexander ; Yu, Lijun ; Liu, Wenhe ; Qian, Yijun ; Cheng, Zhiqi ; Gui, Liangke
• Corporate Creators:
  Mobility21, Carnegie Mellon University
• Corporate Contributors:
  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:
  • Alertness
  • Autonomous vehicles
  • Cognition
  • Decision making
  • Detection and identification
  • Human factors
  • Mathematical prediction
  • Highways
  • Safety
  • Vehicles
  More +
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  FINAL RESEARCH REPORT - June 30, 2023
• Corporate Publisher:
  Mobility21, Carnegie Mellon University
• Abstract:
  The accurate detection and prediction of actions by multiple traffic participants such as pedestrians, vehicles, cyclists and others is a critical prerequisite for enabling self-driving vehicles to make autonomous decisions. Current approaches to teach an autonomous vehicle how to drive use reinforcement learning which is essentially relies on already collected situations as examples relying purely on visual similarity without any understanding of the semantics of the situation and therefore no ability to reason about other similar situations that may have different appearance. This can be overcome by methods that provide situation awareness to the vehicle. The idea is to enable semantically meaningful representations of road scenarios which include the physical layout of the scene, the various participants prior and current activities. The ability to abstract this semantic representation and apply it to multiple scenes that are conceptually similar allows much more robust decision-making strategies by autonomous vehicles. Essentially this allows endowing autonomous vehicles with a reasoning process.
• Format:
  PDF
• Funding:
  69A3551747111
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/68085/dot_68085_DS1.pdf
• File Type:
  [PDF - 10.88 MB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:dbf54b5c219d39d04ad2af7152dfc99f4bc36e193ae9ec44b6bc4c467cbde10e7b34232f86be38917d56607f0ef7b614df8a1124d3b6dd0ce185df63279b7136