A Statistical and Machine Learning Approach to Assess Contextual Complexity of the Driving Environment Using Autonomous Vehicle Data

Description: This project aims to understand the static and dynamic scene complexity from a driver’s perspective using the speed, density, and proximity of the objects around the vehicle and integrate machine learning to develop a Contextual Risk Factor (CRF) model to estimate the driving scene’s complexity and classify contextual risk. The output will be a heatmap that classifies the driving environment’s complexity into high, medium, and low categories. The researchers will use open-source LiDAR data collected by Waymo autonomous vehicles to estimate frame-by-frame road complexity considering dynamic traffic conditions. The LiDAR data provides rich real-world activity information around the vehicle, including stationary and non-stationary objects such as vehicles, pedestrians, and signs. Intellectual Merit: This study considers the speed, density, and proximity of objects in the entire driving environment and within the driver’s cone of vision to develop a measure of the driving environment complexity. The authors plan to contrast the differences between the total environment complexity and the complexity within the driver UFOV. Broader Impacts: Goal: Understand the dynamic scene complexity from a driver’s perspective and develop a heatmap using unsupervised clustering approaches that classifies the driving environment’s complexity. Objectives: (1) Measure contextual complexity and risk considering the dynamic components of the driving environment; (2) Utilize data-rich LiDAR data collected by Waymo autonomous vehicles to reflect dynamic aspects of the environment; and (3) Apply unsupervised clustering methods to estimate the road environment’s complexity.

Language

• English

Project

• Status: Completed
• Funding: $94978
• 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

  Center for Connected Multimodal Mobility

  Clemson University
  Clemson, SC  United States  29634

  Clemson University

  110 Lowry Hall
  Box 340911
  Clemson, SC  United States  29634-0911

  Benedict College

  1600 Harden Street
  Columbia, South Carolina  United States  29204
• Managing Organizations:

  Clemson University

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

  Ogle, Jennifer

• Performing Organizations:

  Clemson University

  110 Lowry Hall
  Box 340911
  Clemson, SC  United States  29634-0911

  Benedict College

  1600 Harden Street
  Columbia, South Carolina  United States  29204
• Principal Investigators:

  Ogle, Jennifer

  Comert, Gurcan

  Bendigeri, Vijay

• Start Date: 20210901
• Expected Completion Date: 20220902
• Actual Completion Date: 20220502
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Drivers; Laser radar; Risk assessment; Traffic safety
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01838172
• Record Type: Research project
• Source Agency: Center for Connected Multimodal Mobility
• Contract Numbers: 69A3551747117
• Files: UTC, RIP
• Created Date: Mar 6 2022 3:05PM