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Wait or Pass? Promoting intersection's cooperation via identifying vehicle's social behavior.

Yubin Xie1, Yue Liu2, Ronggang Zhou3

  • 1School of Economics and Management, Beihang University, Beijing, China; Department of Systems Engineering, City University of Hong Kong, Hong Kong, China.

Accident; Analysis and Prevention
|July 30, 2024
PubMed
Summary
This summary is machine-generated.

Intelligent vehicle communication using social information significantly improves traffic safety and efficiency. Drivers cooperate more and adopt safer strategies when aware of other road users' social value orientation (SVO).

Keywords:
Automated VehiclesHuman–Machine InteractionSocial Value OrientationTraffic SafetyVehicle to Vehicle Communication

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Area of Science:

  • Human-computer interaction
  • Traffic safety research
  • Intelligent transportation systems

Background:

  • Poor communication among road users compromises traffic efficiency and safety, leading to accidents.
  • Intelligent vehicles require effective communication protocols with their environment, other vehicles, and road users.
  • Vehicle-to-vehicle (V2V) communication is a key area of research for enhancing road safety.

Purpose of the Study:

  • To investigate the impact of social information communication on traffic safety and efficiency at intersections.
  • To analyze how social value orientation (SVO) and driving agent identity influence driver decision-making and cooperation in V2V systems.
  • To inform the design of automated vehicle (AV) decision-support systems and enhance user satisfaction.

Main Methods:

  • Development of an experimental platform simulating intersection conflict scenarios.
  • Conducting three studies with 334 participants to assess responses to V2V social information.
  • Incorporating SVO and driving agent identity into V2V systems and AV decision-support algorithms.

Main Results:

  • Providing social information about opposing vehicles significantly promotes cooperative behavior and safer driving strategies.
  • Higher waiting rates were observed when participants faced proself vehicles (0.22) compared to prosocial vehicles (0.79).
  • Participants exhibited more waiting behavior towards AVs than human-driven vehicles, and AV recommendations based on SVO influenced waiting rates.

Conclusions:

  • Integrating social information into V2V communication is crucial for improving road safety and traffic efficiency.
  • Understanding and utilizing SVO in AV decision-making can lead to more predictable and cooperative traffic interactions.
  • Optimizing AV recommendations to align with other vehicles' expected behavior enhances acceptance and safety.