Understanding the relationship between mild cognitive impairment (MCI) and driving behavior is essential for enhancing road safety, particularly among older adults. This study introduces a novel approach by incorporating specific trip destinations-such as home, work, medical appointments, social activities, and errands-using geohashing to analyze the driving habits of older drivers in Nebraska. We employed a two-fold methodology that combines data visualization with advanced machine learning models, including C5.0, Random Forest, and Support Vector Machines, to assess the effectiveness of these location-based variables in predicting cognitive impairment. Notably, the C5.0 model showed a robust and stable performance, achieving a median recall of 0.68, which indicates that our methodology accurately identifies cognitive impairment in drivers 68\% of the time. This emphasizes our model's capacity to reduce false negatives, a crucial factor given the profound implications of failing to identify impaired drivers. Our findings underscore the innovative use of life-space variables in understanding and predicting cognitive decline, offering avenues for early intervention and tailored support for affected individuals.

As more doctors and nurses leave the profession, providers are turning to AI technology to help bridge the coverage gap, especially among older Americans. Young drivers must be a certain age to get a license -- but it's less clear when older adults should hang up the keys. Between 2020 and 2021, the number of motor vehicle deaths involving adults 65 and older rose by 15%, exceeding 8,200 fatalities, according to data from the National Safety Council. Tina Paff, president of Bick's Driving School of Western Hills in Ohio, spoke with Fox News Digital about how to ensure safety on the road for aging drivers -- and how family members can recognize the potential warning signs. Paff heads up the Bick's Driver Rehabilitation Program, which evaluates older adults' driving skills to determine whether to recommend that they "retire" from operating vehicles.

Driving is a complex daily activity indicating age and disease related cognitive declines. Therefore, deficits in driving performance compared with ones without mild cognitive impairment (MCI) can reflect changes in cognitive functioning. There is increasing evidence that unobtrusive monitoring of older adults driving performance in a daily-life setting may allow us to detect subtle early changes in cognition. The objectives of this paper include designing low-cost in-vehicle sensing hardware capable of obtaining high-precision positioning and telematics data, identifying important indicators for early changes in cognition, and detecting early-warning signs of cognitive impairment in a truly normal, day-to-day driving condition with machine learning approaches. Our statistical analysis comparing drivers with MCI to those without reveals that those with MCI exhibit smoother and safer driving patterns. This suggests that drivers with MCI are cognizant of their condition and tend to avoid erratic driving behaviors. Furthermore, our Random Forest models identified the number of night trips, number of trips, and education as the most influential factors in our data evaluation.

Driver gaze plays an important role in different gaze-based applications such as driver attentiveness detection, visual distraction detection, gaze behavior understanding, and building driver assistance system. The main objective of this study is to perform a comprehensive summary of driver gaze fundamentals, methods to estimate driver gaze, and it's applications in real world driving scenarios. We first discuss the fundamentals related to driver gaze, involving head-mounted and remote setup based gaze estimation and the terminologies used for each of these data collection methods. Next, we list out the existing benchmark driver gaze datasets, highlighting the collection methodology and the equipment used for such data collection. This is followed by a discussion of the algorithms used for driver gaze estimation, which primarily involves traditional machine learning and deep learning based techniques. The estimated driver gaze is then used for understanding gaze behavior while maneuvering through intersections, on-ramps, off-ramps, lane changing, and determining the effect of roadside advertising structures. Finally, we have discussed the limitations in the existing literature, challenges, and the future scope in driver gaze estimation and gaze-based applications.

Dementia can make it hard for a person to focus and remain alert -- two things that are really important for road safety. Being able to identify when someone is experiencing early signs of cognitive impairment could be key to saving lives on the road -- unfortunately, it's not always easy to notice these early signs. Now, machine learning could make it easier to detect dementia. The innovation: A team led by researchers at Columbia University has developed machine learning models that detect early and mild cognitive impairment in older drivers with 88% accuracy. The opportunity: By analyzing driving behavior, these machine learning algorithms can help identify when a driver might be exhibiting early indicators of dementia and mild cognitive impairment.

Fukuoka – A driving school in Onojo, Fukuoka Prefecture, has started using an artificial intelligence-based system on a trial basis to evaluate students' driving skills. It is the first time for a driving school in Japan to introduce such a system for evaluating skills. The AI technology analyzes a variety of data, including the route the vehicle traveled and its speed, as well as the movements of the driver's eyes. Such a system is expected to reduce instructors' workload and ensure appropriate evaluation amid increasing demand for driving lessons for elderly drivers. This month, Minami Fukuoka Driver's School, operated by Onojo-based Minami Holdings, introduced the AI-based system, developed jointly by Tokyo-based Tier IV Inc. and Brain IV Inc., based in Nagoya, both of which develop systems for autonomous driving.

When Carol Baker, a 76-year-old driver from Annapolis, Maryland, gets behind the wheel of her 2016 Buick Enclave, her phone automatically connects to the car. She uses the Bluetooth technology to call friends and change the radio station without taking her eyes off the road. "It doesn't always understand what I'm saying," she says. "That can be a little frustrating." But audio commands and touchscreens in cars are more than frustrating for older drivers -- they can be downright dangerous.

When Honda Motor Co. launched the latest version of its N-Box a year ago, it promoted features on the pint-sized minicar such as error-detecting pedals, automatic emergency braking and moveable seats, part of a push to market the vehicle to young families. But a drastically different demographic has made the N-Box the country's best-selling passenger vehicle: roughly half the owners of the most recent model are 50 or older. Automakers had hoped high-tech options would attract younger buyers to kei cars (minicars) even as the number of Japanese drivers under 30 has slid nearly 40 percent since 2001. Instead, with a price tag starting around ¥850,000 and low ownership taxes, minicars have gained a more loyal following among the rapidly growing population of elderly Japanese, many of whom are on fixed incomes. "After their children are grown and leave home, more people are looking to downsize from larger family cars to more compact ones," said Kiminori Murano, managing director at Tortoise, a dealership specializing in minicars in Yamato, Kanagawa Prefecture.

Drivers over 65 were responsible for 965 deadly accidents across Japan -- more than a quarter of the total -- in 2016, according to the National Police Agency. In one of the most shocking cases, an 87-year-old crashed his truck into a group of schoolchildren, killing a 6-year-old and injuring others, prompting demands for action on the issue. In a tranquil countryside setting outside the town of Kanuma, Tochigi Prefecture, on a track surrounded by rice paddies and mountains, elderly drivers are taking public safety into their own hands and completing refresher courses behind the wheel. Emiko Takahashi, a 73-year-old taking the course, admitted she had "no confidence" in her driving as she got older. "That's why I came here," she said, adding that she has no choice but to drive her ailing husband, seven years her senior, to a hospital every day.

The number of older drivers in the USA is rising rapidly. In the quest to be able to keep driving as they grow older, more Boomers are anxious to try the latest in car safety tech that might help keep them stay behind the wheel, a new study finds. Some 76% drivers age 50 say they would look for a car with the latest safety features, finds the online survey by insurer The Hartford. "Our findings indicate that some drivers, age 50-plus, would be more willing to drive in certain situations if they had particular technologies," said Jodi Olshevsky, a gerontologist who is executive director of The Hartford Center for Mature Marketing Excellence. He says it suggests "they associate advanced technologies with enhanced safety," They are looking for advanced safety features like blind-spot warning, crash mitigation, lane departure warnings and advanced headlights.