Are driving assistance systems making the roads safer?
Ashish Agarwal, professor in the IROM Department of Management in the McCombs School of Business at The University of Texas at Austin, gets behind the wheel to find out.
Ashish Agarwal is a professor of information, risk, and operations management at The University of Texas at Austin’s McCombs School of Business. He teaches courses on the introduction to information technology honors, digital technologies, and business innovations, and he has supervised numerous doctoral students.
Agarwal uses econometrics, Bayesian estimation, network analysis, structural modeling, and machine learning to inform his research, which has been published in numerous leading conferences and journals. He has been invited to speak about his research at several universities, including Carnegie Mellon University, Georgia Tech, Harvard University, New York University, and Purdue University.
Agarwal currently works on the editorial board for Management Science and Service Science and has been recognized by the INFORMS Information Systems Society for his intellectual contributions to the information systems industry.
Before joining Texas McCombs, Agarwal earned a Ph.D. in information systems from the Tepper School of Business, Carnegie Mellon University, and a bachelor’s in engineering from the Indian Institute of Technology Mumbai. He also holds an M.S. in engineering from the Massachusetts Institute of Technology.
Driving Assistance Systems Can Lead To More Hazardous Driving
Automakers are increasingly putting smart technologies into cars and trucks. An example is Advanced Driver Assistance Systems to prevent crashes by alerting drivers to potential dangers.
But an important question remains: Do these technologies also promote safer driving habits?
The key takeaway we found: Smart alerts are not uniformly effective.
Our study, based on telematics data from 200,000 vehicles, showed that the effect of these systems varies significantly depending on the urgency of the alerts.
We compared three types of alerts. A low-urgency alert, blind-spot detection, improved driving behavior, reducing daily hard braking by 6.8% and speeding incidents by 9.3%.
In contrast, the urgent alerts such as lane departure warnings and forward collision warnings raised both risky behaviors by about 5% to 6%.
So why the difference? Blind-spot detection gives drivers time to engage in deliberate, reflective “System 2” thinking, allowing safety lessons to take hold and improve driving habits. In contrast, urgent alerts trigger fast, automatic “System 1” responses. As drivers grow accustomed to these urgent warnings, they may feel safer and compensate by driving more aggressively. Over time, this divergence widens.
Crash data confirms the effect on driving behavior: Blind-spot detection cut hard-braking and speeding crashes by about 2% to 3%, but urgent alerts raised those risks by about 1.7%.
Smart alerts are not uniformly effective because of how drivers process information. To fully realize their safety benefits, designers should try to align the urgency of warnings with the way drivers process information.
Read More:
[Sage Journals] – General Behavioral Impact of Smart System Warnings: A Case of Advanced Driving Assistance Systems
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