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.

V2P Collision Warnings for Distracted Pedestrians: A Comparative Study with Traditional Auditory Alerts Novel Certad ID Graduate Student Member, IEEE, Enrico Del Re ID Student Member, IEEE, Joshua V arughese ID Member, IEEE, and Cristina Olaverri-Monreal ID Senior Member, IEEE Abstract -- This study assesses a V ehicle-to-Pedestrian (V2P) collision warning system compared to conventional vehicle-issued auditory alerts in a real-world scenario simulating a vehicle on a fixed track, characterized by limited maneuverability and the need for timely pedestrian response. The results from analyzing speed variations show that V2P warnings are particularly effective for pedestrians distracted by phone use (gaming or listening to music), highlighting the limitations of auditory alerts in noisy environments. The findings suggest that V2P technology offers a promising approach to improving pedestrian safety in urban areas I. I NTRODUCTION Road traffic accidents are a significant global concern, with a disproportionate number of fatalities and injuries affecting Vulnerable Road Users (VRUs) [1]. Among the various factors contributing to these accidents, pedestrian distraction, particularly due to smartphone use, has become a critical issue. Studies have shown that a substantial percentage of pedestrians engage with their smartphones while walking, leading to reduced situational awareness, increased risky behavior, and a higher likelihood of near collisions and accidents [1] [2].

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.

Computer vision has advanced research methodologies, enhancing system services across various fields. It is a core component in traffic monitoring systems for improving road safety; however, these monitoring systems don't preserve the privacy of pedestrians who appear in the videos, potentially revealing their identities. Addressing this issue, our paper introduces Video-to-Text Pedestrian Monitoring (VTPM), which monitors pedestrian movements at intersections and generates real-time textual reports, including traffic signal and weather information. VTPM uses computer vision models for pedestrian detection and tracking, achieving a latency of 0.05 seconds per video frame. Additionally, it detects crossing violations with 90.2% accuracy by incorporating traffic signal data. The proposed framework is equipped with Phi-3 mini-4k to generate real-time textual reports of pedestrian activity while stating safety concerns like crossing violations, conflicts, and the impact of weather on their behavior with latency of 0.33 seconds. To enhance comprehensive analysis of the generated textual reports, Phi-3 medium is fine-tuned for historical analysis of these generated textual reports. This fine-tuning enables more reliable analysis about the pedestrian safety at intersections, effectively detecting patterns and safety critical events. The proposed VTPM offers a more efficient alternative to video footage by using textual reports reducing memory usage, saving up to 253 million percent, eliminating privacy issues, and enabling comprehensive interactive historical analysis.

The emergence of smart cities demands harnessing advanced technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) and promises to unlock cities' potential to become more sustainable, efficient, and ultimately livable for their inhabitants. This work introduces an intelligent city management system that provides a data-driven approach to three use cases: (i) analyze traffic information to reduce the risk of traffic collisions and improve driver and pedestrian safety, (ii) identify when and where energy consumption can be reduced to improve cost savings, and (iii) detect maintenance issues like potholes in the city's roads and sidewalks, as well as the beginning of hazards like floods and fires. A case study in Aveiro City demonstrates the system's effectiveness in generating actionable insights that enhance security, energy efficiency, and sustainability, while highlighting the potential of AI and IoT-driven solutions for smart city development.

It is critical for vehicles to prevent any collisions with pedestrians. Current methods for pedestrian collision prevention focus on integrating visual pedestrian detectors with Automatic Emergency Braking (AEB) systems which can trigger warnings and apply brakes as a pedestrian enters a vehicle's path. Unfortunately, pedestrian-detection-based systems can be hindered in certain situations such as night-time or when pedestrians are occluded. Our system addresses such issues using an online, map-based pedestrian detection aggregation system where common pedestrian locations are learned after repeated passes of locations. Using a carefully collected and annotated dataset in La Jolla, CA, we demonstrate the system's ability to learn pedestrian zones and generate advisory notices when a vehicle is approaching a pedestrian despite challenges like dark lighting or pedestrian occlusion. Using the number of correct advisories, false advisories, and missed advisories to define precision and recall performance metrics, we evaluate our system and discuss future positive effects with further data collection. We have made our code available at https://github.com/s7desai/ped-mapping, and a video demonstration of the CHAMP system at https://youtu.be/dxeCrS_Gpkw.

Rather than subject us to useless grilles on its EVs, Škoda is experimenting with color-coded warnings that show when it's safe or dangerous to use the crosswalk. The Czech carmaker has built a crude prototype for now that hardly seems as cool as the renders, but the company says the device could be integrated on the Škoda Enyaq iV within a couple of years. That EV is built on the same platform as the Volkswagen ID.4, and while the VW doesn't suffer from a big grille, the technology would nonetheless be useful on the German EV, which is sold in the U.S. where pedestrian injuries and deaths are increasing at an alarming rate. The robot is known as the IPA2X, and it was designed to help kids, seniors, and people with disabilities cross roads safely. The 6.5-foot robot will be tall enough to look over rows of parked cars to detect oncoming traffic, and will be able to "talk" with modern cars, alerting drivers to the presence of pedestrians.

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.

This will make you think twice before putting your feet up on the dashboard during your next road trip. General Motors stock falls on Monday after Goldman Sachs rates the stock a sell. File photo shows GM logo. General Motors has received a patent for an airbag on the outside of vehicles designed to "provide protection to a pedestrian," the latest iteration in an industry effort to address a growing problem that accounts for roughly one-in-seven U.S. traffic deaths. "The pedestrian protection airbag could become an important engineering solution in the future," said Tom Wilkerson, safety communications spokesman for GM.