Driver Acceptance of False Alarms to Simulated Encroachment

Objective: We investigated driver acceptance of alerts to left-turn encroachment incidents that do not produce a crash. If an event that produces a crash is the criterion for a “true” alert, all the alerts we studied are technically false alarms. Our aim was to inform the design of intersection-assist active safety systems. Background: The premise of this study is that it may be possible to overcome driver resistance to alerts that are false alarms by designing systems to issue alerts when and only when drivers would expect and accept them. Method: Participants were passengers in a driving simulator that presented left-turn encroachment incidents. Participant point of view, the direction of encroachment, and postencroachment time (PET) were manipulated to produce 36 near-crash incidents. After viewing each incident, the participant rated the relative acceptability of a hypothetical alert to it. Results: Repeated-measures ANOVA and logistic regression indicate that acceptability varies inversely with PET. At PET intervals less than 2.2 s, driver point of view and encroachment direction interact. At PET intervals more than 2.2 s, alerts to lateral encroachments are more acceptable than alerts to oncoming encroachments. Conclusion: Driver acceptance of alerts by active safety systems will be sensitive to context. Application: This study demonstrates the utility of eliciting subjective criteria to inform system design to match driver (user) expectations. Intersection-assist active safety systems will need to be designed to adapt to the interaction of driver point of view, the direction of encroachment, and PET.

[1]  John D. Lee,et al.  Trust in Automation: Designing for Appropriate Reliance , 2004, Hum. Factors.

[2]  J. Gibson,et al.  A theoretical field-analysis of automobile-driving , 1938 .

[3]  David Shinar,et al.  Imperfect in-vehicle collision avoidance warning systems can aid distracted drivers , 2007 .

[4]  David M. Schultz,et al.  False Alarms and Close Calls: A Conceptual Model of Warning Accuracy , 2007 .

[5]  Raja Parasuraman,et al.  Humans and Automation: Use, Misuse, Disuse, Abuse , 1997, Hum. Factors.

[6]  Monica N. Lees,et al.  The influence of distraction and driving context on driver response to imperfect collision warning systems , 2007, Ergonomics.

[7]  P A Hancock,et al.  LEFT TURN AND GAP ACCEPTANCE CRASHES. IN: HUMAN FACTORS AND TRAFFIC SAFETY , 2001 .

[8]  Brian L Allen,et al.  ANALYSIS OF TRAFFIC CONFLICTS AND COLLISIONS , 1978 .

[9]  N. Moray,et al.  Trust in automation. Part II. Experimental studies of trust and human intervention in a process control simulation. , 1996, Ergonomics.

[10]  Christopher D. Wickens,et al.  False Alerts in Air Traffic Control Conflict Alerting System: Is There a “Cry Wolf” Effect? , 2009, Hum. Factors.

[11]  David Shinar,et al.  Imperfect In-Vehicle Collision Avoidance Warning Systems Can Aid Drivers , 2004, Hum. Factors.

[12]  S. Breznitz.  Cry Wolf: The Psychology of False Alarms , 1984 .

[13]  Joachim Meyer,et al.  Effects of Warning Validity and Proximity on Responses to Warnings , 2001, Hum. Factors.

[14]  Christopher D. Wickens,et al.  A model for types and levels of human interaction with automation , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[15]  P A Hancock,et al.  Alarm effectiveness in driver-centred collision-warning systems. , 1997, Ergonomics.

[16]  Joachim Meyer,et al.  Conceptual Issues in the Study of Dynamic Hazard Warnings , 2004, Hum. Factors.

[17]  E Farber,et al.  USING FREEWAY TRAFFIC DATA TO ESTIMATE THE EFFECTIVENESS OF REAR-END COLLISION COUNTERMEASURES , 1993 .

[18]  R W Huey,et al.  Inappropriate , 2020, Encyclopedia of the UN Sustainable Development Goals.