We've been recognized by New Jersey's leaders as the next leaders in public safety. RADR is a pivotal technology transforming security.
Our mission statement.
When a suspect is confirmed by law enforcement on the web portal, RADR will begin tracking for their appearances in dashcams.
Presenting RADR: Rapid Amber Detection Response. All rights reserved. (Logo attached)
The suspect tracking page of the web portal, from where law enforcement can flag culprits.
The sign-up page, which is dynamic to onboard both individuals & law enforcement.
The upcoming RADR mobile app, enhancing rapid recovery & public transparency.
A snippet of our revolutionary AI technology that gained us recognition by our governors.
What's the problem RADR's trying to solve?
The team members that made RADR a reality. CEO: Ekya Dogra. COO: Sahil Ghosh. CTO: Adithiya Venkatakrishnan.

RADR: Revolutionizing AMBER Alerts with AI

Video link: https://vimeo.com/1070467699/66c7846d15?share=copy

Inspiration

When a child goes missing, every second matters. Yet, traditional AMBER Alerts rely on human observation, often leading to delays and missed opportunities. We asked ourselves: What if technology could enhance these alerts, turning every vehicle on the road into a pair of watchful eyes? That question sparked the idea for RADR—a system that leverages AI-powered dashcams to automatically detect and locate suspect vehicles in child abduction cases.

What It Does

RADR transforms ordinary dashcams into a real-time search network for AMBER Alerts. When an alert is issued, our AI scans for suspect vehicles based on license plates, make, model, and other unique identifiers. If a match is detected, RADR instantly notifies law enforcement with precise location data, drastically reducing response times and improving the chances of recovery.

How We Built It

We developed RADR using a combination of:

Computer vision algorithms to analyze dashcam footage in real-time.
Machine learning models trained to recognize vehicle features and match them to alert data.
Secure cloud-based infrastructure to enable seamless data processing and instant law enforcement notifications.
Privacy-first design, ensuring that data is encrypted and only activated during active AMBER Alerts.

Challenges We Ran Into

Building RADR came with its share of obstacles:

Balancing speed and accuracy: AI models had to be both precise and efficient in identifying vehicles under varying conditions.
Data privacy concerns: Ensuring compliance with regulations while maintaining effectiveness.
Infrastructure limitations: Optimizing for real-time analysis without overwhelming cloud resources or user devices.

Accomplishments That We're Proud Of

Successfully developing an AI model that detects and flags suspect vehicles in seconds.
Creating a privacy-first system that works only when an AMBER Alert is active.
Designing a scalable solution that can integrate with existing dashcams and law enforcement systems.

What We Learned

AI can significantly enhance public safety when deployed responsibly.
Collaboration with law enforcement is key to making RADR a practical, real-world solution.
Privacy and security must be at the forefront of any surveillance-based technology to ensure public trust and ethical use.

What's Next for RADR

RADR’s potential goes beyond AMBER Alerts. Our next steps include:

Expanding to general crime detection, using AI-powered Beacons in high-risk areas.
Partnering with law enforcement agencies to integrate RADR into their response systems.
Scaling our technology to more vehicles and devices, creating a smarter, safer network for public security.

RADR isn’t just an idea—it’s a movement to bring missing children home faster and make communities safer.