Pedestrian fatalities continue to rise in the United States, driven by factors such as human distraction, increased vehicle size, and complex traffic environments. Advanced Driver Assistance Systems (ADAS) offer a promising avenue for improving pedestrian safety by enhancing driver awareness and vehicle responsiveness. This study conducts a comprehensive data-driven analysis utilizing the Fatality Analysis Reporting System (FARS) to quantify the effectiveness of specific ADAS features like Pedestrian Automatic Emergency Braking (PAEB), Forward Collision Warning (FCW), and Lane Departure Warning (LDW), in lowering pedestrian fatalities. By linking vehicle specifications with crash data, we assess how ADAS performance varies under different environmental and behavioral conditions, such as lighting, weather, and driver/pedestrian distraction. Results indicate that while ADAS can reduce crash severity and prevent some fatalities, its effectiveness is diminished in low-light and adverse weather. The findings highlight the need for enhanced sensor technologies and improved driver education. This research informs policymakers, transportation planners, and automotive manufacturers on optimizing ADAS deployment to improve pedestrian safety and reduce traffic-related deaths.

Road fatalities pose significant public safety and health challenges worldwide, with pedestrians being particularly vulnerable in vehicle-pedestrian crashes due to disparities in physical and performance characteristics. This study employs explainable artificial intelligence (XAI) to identify key factors contributing to pedestrian fatalities across the five U.S. states with the highest crash rates (2018-2022). It compares them to the five states with the lowest fatality rates. Using data from the Fatality Analysis Reporting System (FARS), the study applies machine learning techniques-including Decision Trees, Gradient Boosting Trees, Random Forests, and XGBoost-to predict contributing factors to pedestrian fatalities. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is utilized, while SHapley Additive Explanations (SHAP) values enhance model interpretability. The results indicate that age, alcohol and drug use, location, and environmental conditions are significant predictors of pedestrian fatalities. The XGBoost model outperformed others, achieving a balanced accuracy of 98 %, accuracy of 90 %, precision of 92 %, recall of 90 %, and an F1 score of 91 %. Findings reveal that pedestrian fatalities are more common in mid-block locations and areas with poor visibility, with older adults and substance-impaired individuals at higher risk. These insights can inform policymakers and urban planners in implementing targeted safety measures, such as improved lighting, enhanced pedestrian infrastructure, and stricter traffic law enforcement, to reduce fatalities and improve public safety.

How will pedestrians fare in an era of self-driving cars? When growing up, most children are taught the rather simple but altogether life-saving idea that they should look both ways before crossing the street. Why is there a need to teach such a safety precaution? Because the streets are occupied by moving objects, including heavy ones that can knock the stuffing out of you. Cars coming down a street can ram into a person and the result is downright ugly. It is likely that the pedestrian struck by a car is going to suffer some number of injuries, ranging from mild scratches and a few broken bones to the sad and all too often loss-of-life entirely. According to U.S. government statistics, the year 2019 had about 6,200 pedestrian fatalities and approximately 76,000 pedestrians were injured, which is generally the annual counts that occur year after year (for my collection of driving stats, see the link here).

In 2010, the small community of specialists who pay attention to US road safety statistics picked up the first signs of a troubling trend: more and more pedestrians were being killed on American roads. That year, 4,302 American pedestrians died, an increase of almost 5% from 2009. The tally has increased almost every year since, with particularly sharp spikes in 2015 and 2016. Last year, 41% more US pedestrians were killed than in 2008. During this same period, overall non-pedestrian road fatalities moved in the opposite direction, decreasing by more than 7%. For drivers, roads are as safe as they have ever been; for people on foot, roads keep getting deadlier. Through the 90s and 00s, the pedestrian death count had declined almost every year. No one would have confused the US for a walkers' paradise – at least part of the reason fewer pedestrians died in this period was that people were driving more and walking less, which meant that there were fewer opportunities to be struck. But at least the death toll was shrinking. The fact that, globally, pedestrian fatalities were much more common in poorer countries made it possible to view pedestrian death as part of an unfortunate, but temporary, stage of development: growing pains on the road to modernity, destined to decrease eventually as a matter of course. The US road death statistics of the last decade have blasted a hole in that theory.

On Monday, the nascent self-driving vehicle sector reached an unfortunate milestone when, for the first time, a self-driving car killed a pedestrian in Tempe, Arizona. This also means robot drivers are becoming more like their human predecessors--who kill thousands of pedestrians every year. And that number has risen dramatically in the past several years. In 2016, cars hit and killed nearly 6,000 pedestrians. The Great Recession explains some of the fluctuation.

As a result of this incident, Uber has stopped all self-driving vehicle tests in San Francisco, Pittsburgh, Toronto and the greater Phoenix area. "Our hearts go out to the victim's family. We are fully cooperating with local authorities in their investigation of this incident," said Uber in a statement. CEO Dara Khosrowshahi echoed the sentiment on Twitter, saying that the authorities were trying to figure out what happened. We're thinking of the victim's family as we work with local law enforcement to understand what happened.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. It was bound to happen. With autonomous vehicles on the streets across the country, one of them--through computer error, supervisor carelessness, or a pedestrian's mistake--was going to hit someone. On Monday morning, a self-driving Uber with a supervising driver struck and killed 49-year-old Elaine Herzberg as she tried to cross eight lanes of traffic in Tempe, Arizona. Herzberg's death was the first recorded fatality caused by a self-driving car.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. A self-driving Uber car struck and killed a pedestrian early Monday morning in Tempe, Arizona, marking the first pedestrian fatality of the self-driving car era in the United States. The woman, 49-year-old Elaine Herzberg, died at the hospital of her injuries. The vehicle was in autonomous mode, Tempe police told a local ABC affiliate, and an operator--capable of taking control of the car at any moment--was behind the wheel. "Our hearts go out to the victim's family," Uber said in a statement.