Steering Safer Streets: The Role of AI and Synthetic Data in Understanding Pedestrian Behavior

Imagine a world where cars drive themselves, smoothly navigating through streets filled with pedestrians. But how do these cars know what a person on the street might do next? This is where the paper “Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction,” written by Muhammad Naveed Riaz and team, comes into play. Published in the field of self-driving cars, this paper, dated 12 Jan 2024 , tackles the challenging task of figuring out what pedestrians might do next — will they cross the road or not? This research is quite special because it doesn’t just use real-life examples (from datasets named JAAD and PIE) but also creates its own simulated world with a dataset called PedSynth, made using something called ARCANE. This is like creating a video game world to test how well their system works. PedSynth is pretty vast, covering around 400 places in virtual cities with different weather and light, which makes it really good for testing. They split this virtual world data into parts: 80% for training their system, 10% for checking if it’s learning right, and the last 10% to test how well it does. <a href="https://neurog.medium.com/steering-safer-streets-the-role-of-ai-and-synthetic-data-in-understanding-pedestrian-behavior-24eb3d02e319">Website</a>