Assessing the potential impacts of connected vehicle systems on Driver's situation awareness and driving performance

Xiaomei Tan, Yiqi Zhang, Jing Wang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Vehicles equipped with connected vehicle technologies are able to communicate with each other and with infrastructures. Compared to Advanced Driving Assistance Systems (ADAS) using camera systems and sensor technologies, the Connected Vehicle Systems (CVS) leverage the wireless communication networks to detect hazards with a greater range, alert drivers of hazards much earlier, and therefore enhance driving safety. However, drivers’ reliance on the CVS to detect critical situations could negatively affect them maintaining situation awareness (SA) in noncritical situations when no warning is issued by the CVS. The present study conducted a driving simulator experiment with 40 participants to investigate the effect of connected vehicle systems on driver SA in normal, noncritical driving scenarios after they were exposed to the CVS with different designs of collision warning lead time (3 s, 6 s, and no warnings). After drivers experienced the CVS-supported warnings with the assigned design of lead time in critical situations, driver SA was measured in normal driving conditions using the freeze probe technique. Results revealed that drivers who experienced the CVS with early warnings (6 s) showed lower SA for normal driving events compared to those who experienced the CVS with late warnings (3 s) or no warnings. Although early warnings of CVS brought more safety benefits to drivers in critical situations, the degraded driver SA due to drivers’ reliance on such warning systems could endanger drivers when a system failure occurred. These findings highlight the importance of balancing the effects of warning lead time on driver SA and driving performance in designing connected vehicle systems.

Original languageEnglish (US)
Pages (from-to)177-193
Number of pages17
JournalTransportation Research Part F: Traffic Psychology and Behaviour
Volume84
DOIs
StatePublished - Jan 2022

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Applied Psychology

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