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].

The Indian Ministry of Science and Technology said this unique approach uses the predictive power of AI to identify road hazards and a collision warning system to communicate timely alerts to drivers, to make various safety-related improvements. Artificial intelligence (AI)-powered solutions may soon make roads in India a safer place to drive. The Indian government announced on Tuesday that an AI-powered technology could reduce the risk of road accidents in the country, which have killed more than a lakh people in 2020. In a bid to prevent this from happening, the Indian government said the AI approach will use a first-of-its-kind dataset consisting of 10,000 images. He said that this dataset is finely annotated with 34 classes collected from 182 driving sequences on Indian roads obtained from a front-facing camera attached to a car driving through the cities of Hyderabad, Bangalore and their outskirts.

Streetlogic wants to help e-bike riders have a safer experience on the road. The company announced a $2.1 million pre-seed raise, as well as the launch of its flagship product, a surround-view camera that can predict front, side and rear collisions and notify riders in order to prevent accidents. Starting Tuesday, customers in the U.S., Canada and Europe can pre-order Streetlogic's advanced driver assistance system (ADAS) for e-bikes with a down payment of $30. The final retail price will be around $300 to $400, and the first batch of mass produced systems is expected for delivery by the end of 2022, according to Jonathan Denby, CEO and founder of Streetlogic. Customers based in San Francisco, where Streetlogic is based, will be eligible to try one of the systems sooner via a limited, invite-only beta deployment program beginning early next year.