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IPOD Abstract for presentation (Poster or Podium) with a Paper in the Conference Proceedings

Safety, Security, and Standards

1667214 - Prediction of Daily Disengagements of Automated Vehicles using Explainable Machine Learning Approach

Monday, June 17, 2024
4:15 PM - 4:30 PM ET
Room: 218
Abstract:

This study presents a comprehensive analysis of Automated Vehicle (AV) disengagements in California, leveraging traffic conditions and employing the XGBoost model and Explainable AI techniques. The study investigates six distinct categories of disengagement initiation: driver-initiated, system-initiated, freeway-based, arterial-based, driverless-capable, and driverless-incapable. The findings reveal that predictors of each disengagement type differ significantly. Generally, the day of the week and vehicle miles of travel showed a great contribution to the prediction of overall AV disengagements. On the other hand, human-driven VMT was the main predictor for driver-initiated disengagement, while human-driven vehicle crashes were the highest-ranked factor for system-initiated, arterial roadways, and driverless-capable AV disengagement. For freeway-based disengagements, the year of operation was the major factor. The study contributes to the existing body of knowledge by providing a nuanced understanding of AV disengagements, emphasizing the need for targeted interventions and strategies. The research serves as a pioneering step towards unveiling hidden patterns in AV disengagements, showcasing the potential of Explainable AI in unraveling complex AV disengagement scenarios, and providing a solid foundation for future advancements in AV technology and policy formulation. The findings point to further investigation of AV disengagements to understand the safe operation of AVs and better predict their maturity, thereby aiding policymakers in making informed decisions about AV testing and deployment.

Learning Objectives:

Attendees can expect to learn the following from this session:
  • Understand the application of explainable machine learning on examining disengagements of automated vehicles
  • Understand the variations of the disengagements of AVs by the source
  • Understand the key attributes associated with AV disengagements.