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Area of Science:

  • Traffic safety
  • Human-computer interaction
  • Automated vehicle social dynamics

Background:

  • Roads will soon feature both human drivers and automated vehicles (AVs), creating mixed traffic environments.
  • Current safety assessments often overlook the complexities of human-AV social interactions, potentially overestimating safety benefits.

Purpose of the Study:

  • To develop mental models of human and AV drivers to understand mixed traffic risks.
  • To analyze the social interactions between human drivers and AVs and their safety implications.

Main Methods:

  • Development of two contrasting mental models: MM I (AVs as superior) and MM II (human-AV incompatibility).
  • Review of empirical studies using surveys, driving simulators, test-tracks, on-road observations, and AV accident analysis.

Main Results:

  • Mental Model I predicts safety benefits, but empirical evidence supports Model II, highlighting risks.
  • Observed risks include human aggression towards AVs, exploitation of AVs, negative peer influence, and increased driver challenges in mixed traffic.

Conclusions:

  • Human-AV interactions in mixed traffic present unique and emerging risks.
  • Addressing these social dynamics is crucial for mitigating potential negative outcomes and ensuring road safety.