False Alarms and Human-Machine Warning Systems

This work illustrates that false alarms are likely to have significant frequencies as well as detrimental influence on the effectiveness of human-machine warning systems. Several factors are responsible for false alarm materialization, including the need to predict uncertain conditions in the future, variability of human perception, and low a priori probabilities of traffic collisions. The effect of false alarms on human trust in warning systems and on credibility of warnings could be considerable even for low false alarm rates. One way to decrease false alarm rates would be to focus on predicting possible conflicts, which are much more probable than actual collisions (thus increasing a priori probabilities). One of the suggested future research directions is to assess directly what different individuals will view as being a false alarm. In the majority of existing studies, the researchers defined this outcome. It is possible that people will change their behavior based on their perception of false alarms rather than on a researcher's definition of them.

[1]  Mark R. Lehto,et al.  An experimental comparison of conservative versus optimal collision avoidance warning system thresholds , 2000 .

[2]  David Shinar,et al.  Effects of an In-Vehicle Collision Avoidance Warning System on Short- and Long-Term Driving Performance , 2002, Hum. Factors.

[3]  Joachim Meyer,et al.  Why Better Operators Receive Worse Warnings , 2002, Hum. Factors.

[4]  Mark R. Lehto,et al.  Improving the effectiveness of warnings by increasing the appropriateness of their information content: some hypotheses about human compliance , 1996 .

[5]  Wassim G. Najm,et al.  Analysis of crossing path crashes , 2001 .

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

[7]  T Wilson,et al.  IVHS COUNTERMEASURES FOR REAR-END COLLISIONS, TASK 1 INTERIM REPORT. VOLUME VI: HUMAN FACTORS STUDIES , 1994 .

[8]  J. Goldberg Economic impact of motor vehicle crashes. , 2002, Annals of Emergency Medicine.

[9]  Raja Parasuraman,et al.  Fuzzy Signal Detection Theory: Basic Postulates and Formulas for Analyzing Human and Machine Performance , 2000, Hum. Factors.

[10]  R. John Hansman,et al.  Effect of false alarm rate on pilot use and trust of automation under conditions of simulated high risk , 1998 .

[11]  J D Chovan,et al.  EXAMINATION OF UNSIGNALIZED INTERSECTION, STRAIGHT CROSSING PATH CRASHES AND POTENTIAL IVHS COUNTERMEASURES. FINAL REPORT , 1994 .

[12]  C. Helstrom,et al.  Statistical theory of signal detection , 1968 .

[13]  J D Chovan,et al.  EXAMINATION OF INTERSECTION, LEFT TURN ACROSS PATH CRASHES AND POTENTIAL IVHS COUNTERMEASURES. FINAL REPORT , 1994 .

[14]  Mark S. Sanders,et al.  Human Factors in Engineering and Design , 1957 .

[15]  J. G. Hollands,et al.  Engineering Psychology and Human Performance , 1984 .

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

[17]  Thomas A. Dingus,et al.  Human Factors Field Evaluation of Automotive Headway Maintenance/Collision Warning Devices , 1997, Hum. Factors.

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

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

[20]  Mustapha Mouloua,et al.  Automation and Human Performance : Theory and Applications , 1996 .

[21]  Jeff Caird,et al.  In-Vehicle Train Warnings (ITW): The Effect of Reliability and Failure Type on Driver Perception Response Time and Trust , 1999 .

[22]  P. Hancock,et al.  The Perception of Arrival Time for Different Oncoming Vehicles at an Intersection , 1994 .

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