Vision-based Navigation of Autonomous Vehicle In Roadway Environments With Unexpected Hazards

Vision-based navigation of autonomous vehicles primarily depends on the Deep Neural Network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems of the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adversarial inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicle by unexpected roadway hazards, such as debris and roadblocks. In this study, the research team first introduces a roadway hazardous environment (both intentional and unintentional roadway hazards) that can compromise the DNN-based navigational system of an autonomous vehicle, and produces an incorrect steering wheel angle, which can cause crashes resulting in fatality and injury. Then, the team develops a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazardous environment, which helps the autonomous vehicle to navigate safely around such hazards. The team finds that their developed DNN-based autonomous vehicle driving system including hazardous object detection and semantic segmentation improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared to the traditional DNN-based autonomous vehicle driving system.

Language

• English

Project

• Status: Completed
• Contract Numbers:

  69A3551747117

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  Clemson University

  110 Lowry Hall
  Box 340911
  Clemson, SC  United States  29634-0911
• Managing Organizations:

  Clemson University

  110 Lowry Hall
  Box 340911
  Clemson, SC  United States  29634-0911
• Project Managers:

  Chowdhury, Mashrur

• Performing Organizations:

  Clemson University

  110 Lowry Hall
  Box 340911
  Clemson, SC  United States  29634-0911
• Principal Investigators:

  Chowdhury, Mashrur

• Start Date: 20170801
• Expected Completion Date: 20181201
• Actual Completion Date: 20181201
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Detection and identification systems; Hazards; Machine vision; Neural networks
• Subject Areas: Design; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01884935
• Record Type: Research project
• Source Agency: Center for Connected Multimodal Mobility
• Contract Numbers: 69A3551747117
• Files: UTC, RIP
• Created Date: Jun 14 2023 3:20PM