Drivers More Likely to Be Distracted While Using Partial Automation Tech, Study Shows 

Cars are stuck in traffic after police blocked the road in West Palm Beach, Florida, on September 15, 2024 following a shooting incident at former US president Donald Trump's golf course. (AFP)
Cars are stuck in traffic after police blocked the road in West Palm Beach, Florida, on September 15, 2024 following a shooting incident at former US president Donald Trump's golf course. (AFP)
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Drivers More Likely to Be Distracted While Using Partial Automation Tech, Study Shows 

Cars are stuck in traffic after police blocked the road in West Palm Beach, Florida, on September 15, 2024 following a shooting incident at former US president Donald Trump's golf course. (AFP)
Cars are stuck in traffic after police blocked the road in West Palm Beach, Florida, on September 15, 2024 following a shooting incident at former US president Donald Trump's golf course. (AFP)

Drivers are more likely to engage in non-driving activities, such as checking their phones or eating a sandwich, when using partial automation systems, with some easily skirting rules set to limit distractions, new research showed on Tuesday.

Insurance Institute for Highway Safety (IIHS) conducted month-long studies with two such systems - Tesla's Autopilot and Volvo's Pilot Assist - to examine driver behavior when the technology was in use and how it evolved over time.

While launching and commercializing driverless taxis have been tougher than expected, major automakers are in a race to deploy technology that partially automates routine driving tasks to make it easier and safer for drivers, and generate revenue for the companies.

The rush has sparked concerns and litigation around the dangers of driver distraction and crashes involving such technology.

The studies show better safeguards are needed to ensure attentive driving, IIHS said in the report.

Partial automation - a level of "advanced driver assistance systems" - uses cameras, sensors and software to regulate the speed of the car based on other vehicles on the road and keep it in the center of the lane. Some enable lane changing automatically or when prompted.

Drivers, however, are required to continuously monitor the road and be ready to take over at any time, with most systems needing them to keep their hands on the wheel.

"These results are a good reminder of the way people learn," said IIHS President David Harkey. "If you train them to think that paying attention means nudging the steering wheel every few seconds, then that's exactly what they'll do."

"In both these studies, drivers adapted their behavior to engage in distracting activities," Harkey said. "This demonstrates why partial automation systems need more robust safeguards to prevent misuse."

The study with Tesla's Autopilot used 14 people who drove over 12,000 miles (19,300 km) with the system, triggering 3,858 attention-related warnings. On average, drivers responded in about three seconds, usually by nudging the steering wheel, mostly preventing an escalation.

The study with Volvo's Pilot Assist had 29 volunteers who were found to be distracted for 30% of the time while using the system - "exceedingly high" according to the authors.



As Storm Bebinca Approaches, Taiwan Uses AI to Predict Typhoon Paths 

Waves break against the protecting walls as Typhoon Gaemi approaches in Keelung, Taiwan July 24, 2024. (Reuters)
Waves break against the protecting walls as Typhoon Gaemi approaches in Keelung, Taiwan July 24, 2024. (Reuters)
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As Storm Bebinca Approaches, Taiwan Uses AI to Predict Typhoon Paths 

Waves break against the protecting walls as Typhoon Gaemi approaches in Keelung, Taiwan July 24, 2024. (Reuters)
Waves break against the protecting walls as Typhoon Gaemi approaches in Keelung, Taiwan July 24, 2024. (Reuters)

As tropical storm Bebinca barrels towards waters off northern Taiwan gathering strength into a possible typhoon, weather forecasters in Taipei are using a new and so far successful method to help track its path - artificial intelligence (AI).

AI-generated forecasts, some powered by software from tech giants including Nvidia, whose chips are made by Taiwan's homegrown semiconductor champion TSMC, have so far outperformed traditional methods in predicting typhoon tracks.

In July, it was AI-based weather models, used for the first time, that helped Taiwan better predict the path and impact of Typhoon Gaemi, the strongest to strike the island in eight years that brought record-breaking rainfall.

The new technology impressed Taiwan forecasters by predicting a direct hit as early as eight days before Gaemi made landfall - handily outperforming conventional methods, which remain the mainstay of prediction planning.

"People are starting to realize AI indeed delivered some stunning performances compared to conventional models," said Chia Hsin-sing, director at the weather service provider Taiwan Integrated Disaster Prevention of Technology Engineering Consulting Company Ltd.

Bebinca is now being tracked using the same AI tools by people including Lin Ping-yu, a forecaster at Taiwan's Central Weather Administration (CWA), who said AI has given them a higher degree of confidence there will not be a direct hit.

"This (AI) is a good thing for us. It is like having one more useful tool to use," said Lin.

The AI weather programs on offer include Nvidia's FourCastNet, Google's GraphCast and Huawei's Pangu-Weather, as well as a deep learning-based system by European Center for Medium-Range Weather Forecasts.

"It is a hotly watched competition. We will know soon who is winning," said Chia.

Such AI models have also begun to be used to predict storms and hurricanes in other regions with good accuracy, according to forecasters and academics.

The AI-based software is trained using historical weather data to learn the cause and effect relationships of meteorological systems and can predict hundreds of weather variables days in advance - a process that requires only a few minutes to complete.

For all the typhoons in the Western Pacific this year up until mid-September, AI's accuracy in predicting storm tracks over a three-day window was nearly 20% higher than that of conventional models, according to data compiled by the CWA.

Ahead of Gaemi, AI helped the administration foresee an unusual loop in its path that prolonged its impact on Taiwan and prompted them to swiftly issue a rare warning for rainfall of 1.8 meters (5.9 feet), which was later proven accurate, according to CWA's deputy head Lu Kuo-Chen.

"(AI) boosted the confidence for forecasters to make that prediction," Lu said, adding the early warning gave extra time for authorities to carry out preparations.

Lu is also pinning hopes on a partnership with Nvidia, which this year announced a generative AI tool called CorrDiff that aims to forecast more precise locations of typhoon landfall and provide higher resolution images inside a storm.

"We are seeing the potential," Lu said.

For now, however, experts say the AI tools were not able to deliver quality forecasts for more detailed impact of a typhoon, such as its strength and winds, and more time is needed for the new technology to solidify its lead over more traditional ways.

"Was it just good luck?" said Chia, pointing to AI's stellar performance on Gaemi. "We need to give AI a bit more time. It is something to look forward to."