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It just got easier to find out who caused that car accident

Rescue workers proceed with caution around the spot where a Tesla slammed into a tree in Baarn, Netherlands, on September 7, 2016.
Robin van Lonkhuijsen | AFP | Getty Images
Rescue workers proceed with caution around the spot where a Tesla slammed into a tree in Baarn, Netherlands, on September 7, 2016.

While it is difficult to predict when autonomous vehicles (AVs) will blend into our everyday lives, regulators appear receptive to the technology, publishing first-ever guidelines earlier this week that open the door to large-scale improvements in the transportation ecosystem.

What is certain about the AV revolution is that the new form of data and analytics their onboard systems provide will significantly improve how we determine fault and resolve disputes. Equally important, the big data from AVs can provide insights into how to prevent accidents and save lives.

Today, more than 90 percent of accidents result from human impairment, such as drunk driving or road rage, errant pedestrians, or just plain bad driving. In 2015, for example, roughly 38,000 Americans were killed and 4.4 million injured with damage costs exceeding $400 billion. In contrast, there were zero fatalities from commercial aviation in the U.S. in 2015, and a total of 136 civil aviation fatalities.

While some have cautioned against allowing AVs on the road until collision avoidance systems achieve perfection, less than perfect systems could be compelling enough if they help to reduce accidents. Every percentage point improvement means roughly 400 lives saved and 40,000 injuries avoided on the road annually.

Big data from onboard systems changes everything because we now have the ability to know the physics associated with accidents determining fault. The ever-increasing numbers of sensors on roads and vehicles move us towards a world of complete information where causes of accidents will be determined more reliably and fault easier to establish. With the detail and transparency that big data provides, no fault accidents will not be an option.

This clarity will also resolve disputes more readily. While we have decades of data about risk across demographics and conditions for pricing insurance efficiently, when it comes to dispute resolution and paying for accidents, the process can be messy and frustrating for all parties, and the outcomes are often unfair.

The question of which party should pay for the accident and how much they should pay has no easy answer based on currently available data, such as physical damage, police reports, and testimonies. None of these data sources are typically sufficient to establish fault. What ensues are systems of justice and practices that assume "no fault" of either party, or complex legal systems requiring a "preponderance of evidence," whose definition varies by state. Without accurate and reliable data, justice is difficult if not impossible to accomplish, and dispute resolution can be lengthy.

There will undoubtedly be a significant amount of trial and error before AVs are commonplace on the road. While it is interesting to make predictions about when AVs will be "good enough" and debate its merits, it is more important to focus on the implications of the data generated by their sensory systems.

The key issue is really the massive increase in data collection from vehicles, which will happen irrespective of whether vehicles are ever fully autonomous. Rather, the data could be used to design incentives and reward desirable driving practices in the emerging hybrid world of human and driverless vehicles. In other words, better data could induce better driving practices and lead to safer transportation with significantly lower insurance and overall costs to society.

Not unlike the "quantified self" (QS) community where personal knowledge discovery is being used to improve individual health without introducing new privacy or security risks, the world of AVs need not expose individuals to such risks either. As with personal devices, the data and analytics can reside "locally" on AVs where data are recorded securely, and analytics can be performed and shared with the appropriate parties according to well specified procedures.

Like the QS arena, where the personal data collected by devices is being used to improve health, the fine-grained transportation data could similarly improve individual and collective driving practices and lead to safer and more efficient transportation systems for us all. Data is the key, specifically, how we use it.

Commentary by Vasant Dhar, a professor of information systems at NYU Stern School of Business and editor-in-chief of Big Data. He also founded SCT Capital Management, a firm that specializes in machine learning based quantitative-trading strategies. Follow him on Twitter @vasantdhar.

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