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Characterizing driver speeding behavior when using partial-automation in real-world driving.

Samantha H Haus1, Pnina Gershon1, Bruce Mehler1

  • 1Center for Transportation & Logistics, AgeLab, Massachusetts Institute of Technology, Cambridge, Massachusetts.

Traffic Injury Prevention
|July 12, 2022
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Summary
This summary is machine-generated.

Researchers identified four speeding behaviors: Incidental, Moderate, Elevated, and Extended. Partial-automation use altered speeding duration and magnitude, highlighting the need for targeted countermeasures for manual and automated driving states.

Keywords:
Automationcluster analysisdriver behaviornaturalistic datasafetyspeeding

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Area of Science:

  • Traffic Safety and Human Factors
  • Automotive Engineering
  • Behavioral Science

Background:

  • Speeding is a common risky driving behavior influenced by numerous factors.
  • Understanding speeding is crucial for developing effective countermeasures, especially with emerging automation.
  • Previous research has not fully characterized real-world speeding behaviors across driving states.

Purpose of the Study:

  • To identify and describe distinct types of real-world speeding behaviors.
  • To compare speeding behaviors during manual driving versus partial-automation use.
  • To inform the development of targeted interventions for risky driving.

Main Methods:

  • Utilized supervised and unsupervised data analysis on naturalistic driving data (MIT Advanced Vehicle Technology).
  • Defined speeding epochs as traveling >= 5 mph over the limit for >= 3 seconds.
  • Employed Dynamic Time Warping and Gower dissimilarity for behavioral classification and clustering.

Main Results:

  • Identified four speeding behaviors: Incidental, Moderate, Elevated, and Extended.
  • Partial-automation use led to longer durations for Incidental and Moderate speeding.
  • Elevated speeding was more prevalent and of higher magnitude in manual driving; Extended speeding was more prevalent but less intense with automation.

Conclusions:

  • Significant variability exists in speeding behaviors between and within manual and automated driving states.
  • System designs mitigating speeding should address the divergent behaviors observed in each driving state.
  • Targeting specific speeding behaviors associated with manual vs. automated driving is key for effective countermeasures.