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S2O: An Integrated driving decision-making performance evaluation method bridging subjective feeling to objective

Yuning Wang1, Zehong Ke2, Yanbo Jiang3

  • 1School of Vehicle and Mobility, Tsinghua University, 100084 Beijing, China; Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Accident; Analysis and Prevention
|April 15, 2025
PubMed
Summary

This study introduces S2O, a new method for evaluating autonomous driving performance by integrating subjective human feelings with objective driving factors like safety and comfort. S2O significantly reduces evaluation errors compared to existing methods.

Keywords:
Autonomous drivingDecision-making evaluationDriving experience factorsHuman evaluation

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Area of Science:

  • Intelligent Transportation Systems
  • Autonomous Driving Technology
  • Decision-Making Systems

Background:

  • Evaluating autonomous driving performance is critical for intelligent transportation systems.
  • Current metrics like trajectory deviation and limited objective indicators lack comprehensiveness and precision.
  • Existing methods fail to accurately capture human subjective driving experience.

Purpose of the Study:

  • To propose S2O, a novel integrated method for evaluating autonomous driving decision-making.
  • To bridge the gap between subjective human perception and objective driving performance evaluation.
  • To develop a precise and comprehensive evaluation metric for autonomous driving systems.

Main Methods:

  • Developed modified fundamental models for safety, time efficiency, comfort, and energy efficiency.
  • Designed a segmental linear fitting model with an SVM segment classifier to interpret human subjective ratings.
  • Utilized the D2E dataset with approximately 1,000 driving cases and 40,000 human ratings for model training and validation.

Main Results:

  • S2O achieved a mean absolute error of 4.58% against ground truth.
  • The proposed method reduced evaluation error by 32.55% compared to baseline methods.
  • Demonstrated real-time evaluation efficiency on the SUMO platform and validated feasibility on three autonomous driving planning algorithms.

Conclusions:

  • S2O offers a significant improvement in the precision and comprehensiveness of autonomous driving performance evaluation.
  • The method effectively translates subjective human driving experience into objective, quantifiable metrics.
  • S2O is a feasible and efficient tool for enhancing the development and modification of autonomous driving decision-making systems.