Trusted and exPlainable Artificial Intelligence for Saving Lives (TruePAL) Technology for First Responder Safety
Description
Fatalities caused by emergency vehicle collisions are 4.8 times higher for emergency responders than the national average. The Trusted and exPlainable Artificial Intelligence for Saving Lives (TruePAL) project was funded by DOT to study the AI technology for saving first responders and roadside crews lives in and around active traffic. The TruePAL, an AI assistant, provides real-time warning of risks by analyzing the environment and traffic patterns to generate timely warning to drivers and roadside crews to avoid crashes. A deep neural network (DNN) is developed to detect and track vehicles, pedestrians, traffics signs, etc., and a Non-Axiomatic Reasoning System (NARS) to analyze the risk and provide prioritized warning messages. A mobile app with AI interface is developed to perform verbal communication with the first responders. The TruePAL AI assistant demonstrated real-time crash warning and human factor design capabilities using a CARLA simulator. The TruePAL project has developed cutting-edge AI tools: a Human-machine AI interface has potential to save lives for first responders and roadside crews.
Detailed example
timely warning to drivers and roadside crews to avoid crashes
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Fatalities caused by emergency vehicle collisions are 4.8 times higher for emergency responders than the national average. The Trusted and exPlainable Artificial Intelligence for Saving Lives (TruePAL) project was funded by DOT to study the AI technology for saving first responders and roadside crews lives in and around active traffic. The TruePAL, an AI assistant, provides real-time warning of risks by analyzing the environment and traffic patterns to generate timely warning to drivers and roadside crews to avoid crashes. A deep neural network (DNN) is developed to detect and track vehicles, pedestrians, traffics signs, etc., and a Non-Axiomatic Reasoning System (NARS) to analyze the risk and provide prioritized warning messages. A mobile app with AI interface is developed to perform verbal communication with the first responders. The TruePAL AI assistant demonstrated real-time crash warning and human factor design capabilities using a CARLA simulator. The TruePAL project has developed cutting-edge AI tools: a Human-machine AI interface has potential to save lives for first responders and roadside crews.
Controls / human review
ATO: Not reported; PIA: Not published