RoadSoS: An Intelligent Road Safety and Emergency Rescue System Based on RAG Technology

RoadSoS is an intelligent road safety application integrating Retrieval-Augmented Generation (RAG), Google Maps, and AI technologies. It aims to address pain points such as lack of first aid knowledge at accident scenes, difficulty in obtaining information, unclear geographical location, and complex resource positioning. It provides users with full-process rescue services including accident first aid guidance, emergency protocol execution, and location of nearby hospitals/police stations. The project ensures the accuracy and traceability of first aid advice through the RAG architecture, making it an innovative application of AI technology in the field of road safety.

Road traffic accidents are a major global public safety issue, with high annual casualties. In emergency situations, ordinary people face difficulties such as lack of first aid knowledge, difficulty in obtaining information, unclear location description, and time-consuming resource positioning. Traditional road rescue applications only provide simple phone call functions and lack intelligent auxiliary decision-making capabilities. The RoadSoS project addresses these pain points by introducing modern AI technology into the field of road safety.

Retrieval-Augmented Generation (RAG)

The core innovation of the project lies in RAG technology, which first retrieves information from external authoritative knowledge bases and then inputs it into large language models for processing. This improves accuracy, ensures timeliness, achieves traceability, and suppresses AI hallucinations.

Geolocation and Map Services

Deeply integrated with the Google Maps API, it realizes real-time location acquisition, surrounding resource search, optimal route planning, and multi-mode navigation.

Large Language Model Interaction

Integrated with mainstream LLMs, it supports natural language understanding and quickly responds to users' first aid and resource query needs.

Intelligent First Aid Guidance

By describing the scene via voice/text, it provides injury assessment, step-by-step first aid guidance, contraindication reminders, and continuous dialogue support.

Emergency Protocol Execution

Built-in standardized processes covering scenarios such as traffic accidents, fires, hazardous chemical leaks, and natural disasters.

One-Click Rescue Call

Intelligently recommends rescue phone numbers, automatically fills in location information, and supports multiple languages.

Surrounding Resources Map

Intuitively displays the distribution and real-time status of medical institutions, police resources, and rescue points.

Modular Design

Adopts a loosely coupled architecture of data layer, service layer, business layer, and presentation layer, facilitating maintenance and expansion.

Offline Capability

Supports local knowledge caching, offline map packages, and emergency mode to cope with poor network scenarios.

Privacy Protection

Follows the principle of least privilege, encrypts location information during transmission, and provides anonymous options.

Individual Users

Suitable for long-distance drivers, self-driving travelers, new drivers, and residents in areas with scarce medical resources. It allows learning first aid knowledge in daily life and provides rescue support in critical moments.

Enterprise Applications

Can be integrated into fleet management systems for driver training, emergency response, and insurance docking.

Public Services

Helps government departments popularize safety education, emergency drills, and disaster response.

IoT Integration

Deeply integrates with in-vehicle systems and smart wearable devices to realize automatic accident detection, vital sign monitoring, and drone support.

Intelligent Prediction

Uses historical data to predict accident hotspots, medical resource shortages, and weather-related road risks.

Community Mutual Aid Network

Builds mechanisms for volunteer recruitment, neighborhood mutual aid, and point incentive systems.

The RoadSoS project demonstrates the huge potential of AI technology in the field of social public welfare. Through the integration of RAG and other technologies, it provides innovative solutions for road safety. In the future, such intelligent rescue systems are expected to become standard applications, building an intelligent protection network for life safety and reflecting technological progress and social responsibility. This article is compiled based on the GitHub open-source project Road_SOS_RAG; developers are welcome to contribute.