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An interview study exploring Tesla drivers' behavioural adaptation.

Rui Lin1, Liang Ma2, Wei Zhang1

  • 1Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China; State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China.

Applied Ergonomics
|June 11, 2018
PubMed
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Early adopters of partially automated vehicles (PAVs) develop unique driving behaviors, including engaging in secondary tasks and establishing safety margins. Understanding these adaptations is key for future autonomous driving systems.

Area of Science:

  • Human-computer interaction
  • Automotive engineering
  • Cognitive psychology

Background:

  • Partially automated vehicles (PAVs) are increasingly common, necessitating an understanding of driver behavior during early adoption.
  • Drivers often exhibit unintended behavioral adaptations when interacting with new vehicle automation technologies.

Purpose of the Study:

  • To investigate behavioral adaptations, mental models, and trust in early adopters of PAVs.
  • To analyze how drivers adjust to and utilize partial automation systems (PAS) within the first few months of use.

Main Methods:

  • Conducted semi-structured interviews with 20 Tesla drivers experienced with Autopilot (1-5 months).
  • Synthesized interview data to identify patterns in driver adaptation, mental models, and trust.
Keywords:
Automated drivingPartial automation systemSecondary task

Related Experiment Videos

Main Results:

  • PAV drivers demonstrated a positive attitude towards PAS.
  • Drivers consistently engaged in secondary tasks while automated driving was active.
  • Drivers learned to identify safe operating conditions and implemented safety margins to mitigate risks.

Conclusions:

  • Early PAV adopters develop specific adaptive strategies, including task diversification and risk management.
  • Driver experience shapes their interaction with and reliance on partial automation systems.
  • Findings highlight the need for user-centered design in autonomous vehicle development to align with emergent driver behaviors.