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Enhancing Autonomous Vehicle Decision-Making at Intersections in Mixed-Autonomy Traffic: A Comparative Study Using an

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Summary
This summary is machine-generated.

Autonomous vehicles (AVs) can improve safety in mixed-traffic environments by using vehicle-to-vehicle (V2V) communication. This data enhances decision-making for priority-taking at intersections, reducing conservative driving behaviors and potential collisions.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Transportation Engineering

Background:

  • The transition to autonomous roadways involves a period of mixed-autonomy traffic, presenting challenges for autonomous vehicles (AVs).
  • AVs' conservative driving behaviors in complex scenarios can cause congestion and collisions with human drivers.
  • Developing sophisticated decision-making models is crucial for safe and efficient mixed-autonomy navigation.

Purpose of the Study:

  • To compare the performance of Time Series Forest (TSF) against state-of-the-art models for a priority-taking classification task in mixed-autonomy traffic.
  • To evaluate the impact of vehicle-to-vehicle (V2V) data, in addition to AV sensor data, on classification accuracy and model complexity.
  • To assess the effectiveness of these models in simulated hazardous intersection scenarios.

Main Methods:

  • Utilized a full-vehicle driving simulator to collect responses to left-turning hazards at signalized and stop-sign-controlled intersections.
  • Employed an explainable multi-variate time series classifier, Time Series Forest (TSF), and two other state-of-the-art models.
  • Compared classification performance using datasets with AV sensor-collected features and combined AV sensor/V2V transmitted features.

Main Results:

  • TSF demonstrated comparable performance on both signalized and stop-sign-controlled intersection datasets, with all models performing better on signalized datasets.
  • Incorporating V2V data slightly increased overall accuracy and significantly improved the true positive rate for stop-sign-controlled scenarios.
  • V2V data integration reduced the number of selected features, decreasing model complexity while maintaining accuracy.

Conclusions:

  • V2V data integration enhances the performance of priority-taking classification models for AVs in mixed-traffic environments.
  • The use of V2V data can potentially reduce the need for overly conservative AV driving strategies, improving traffic flow.
  • This approach offers a pathway to safer and more efficient autonomous navigation without compromising collision avoidance.