RIDESIGHT

Inspiration

Motorcyclists face road hazards that drivers in cars may barely notice. Loose gravel, potholes, road debris, standing water, sharp curves, and sudden vehicle movements can quickly become life-threatening situations for a rider.

Existing motorcycle apps mainly focus on navigation, ride tracking, social features, or crash detection after an accident has already happened. We wanted to build something more proactive: a system that helps riders identify danger before they reach it.

That idea became RideSight, an AI-powered motorcycle safety camera designed to act as an extra set of eyes on the road.

What it does

RideSight uses a separate forward-facing camera mounted to the motorcycle to monitor the road ahead in real time.

The system analyzes the camera feed and identifies motorcycle-specific hazards such as:

Potholes
Loose gravel
Road debris
Standing water
Vehicles entering the rider’s path
Dangerous road conditions
Unexpected obstacles

When a hazard is detected, the rider receives a simple visual or audio warning so they can react without having to constantly look at a screen.

RideSight can also attach the hazard’s location to a shared map. This allows riders approaching the same area to receive an early warning based on hazards detected by other motorcycles.

The separate camera is mounted using a vibration-resistant design, allowing it to maintain a more stable view than a phone mounted directly to the handlebars. The rider’s phone can then be used for navigation, alerts, connectivity, and viewing detected hazards.

How we built it

We built RideSight as a combination of computer vision, real-time location tracking, and a connected mobile interface.

A forward-facing camera captures video from the motorcycle. The video is processed using an object-detection model trained to recognize road hazards and surrounding vehicles.

Our system processes the camera feed, classifies possible hazards, and assigns each detection a confidence and risk level. When the detected object presents a meaningful danger to the rider, the application generates an alert.

We also integrated location data so detected hazards can be placed on a live map. Hazard reports can be shared with other users, allowing the system to become more useful as more riders participate.

The main components included:

A separate forward-facing motorcycle camera
A vibration-resistant camera mount
A computer-vision detection model
A backend for storing hazard reports
GPS-based hazard mapping
A mobile or web interface for alerts and ride information
Audio warnings for hands-free rider notifications

For testing and demonstration, we used recorded motorcycle footage to simulate real-world riding conditions and verify that the system could identify hazards and display warnings.

Challenges we ran into

One of our biggest challenges was dealing with motorcycle vibration. A camera mounted to a motorcycle experiences constant movement from the engine, road surface, acceleration, and rider input. This can create blurry images and unstable detections.

To address this, we designed the system around a separate camera and vibration-resistant mount rather than relying on the rider’s phone camera.

Another challenge was deciding which objects should trigger an alert. Detecting an object is not enough. The system also needs to determine whether it is actually in the rider’s path and whether it presents an immediate risk.

We also had to balance detection accuracy with processing speed. A larger AI model may recognize hazards more accurately, but it can introduce delays. For a safety system, alerts must be generated quickly enough to give the rider time to react.

Additional challenges included:

Reducing false warnings
Detecting small hazards at a distance
Handling low-light and poor-weather conditions
Estimating whether a hazard is inside the motorcycle’s path
Keeping alerts simple enough to avoid distracting the rider
Processing video efficiently on limited hardware

Accomplishments that we're proud of

We are proud that we were able to turn a broad motorcycle safety problem into a practical working concept.

Instead of creating another motorcycle social or navigation app, we developed a system focused on preventing accidents before they happen.

Our biggest accomplishments include:

Building a working hazard-detection pipeline
Identifying road hazards from motorcycle footage
Creating real-time rider alerts
Mapping detected hazards using location data
Designing the product around a separate stabilized camera
Developing a system that improves as more riders contribute data

We are also proud that RideSight has the potential to provide value beyond an individual rider. Every detected hazard can help protect the next rider traveling on the same road.

What we learned

We learned that building a real-world computer-vision system requires much more than simply detecting objects.

A motorcycle safety system must understand context. It needs to know where an object is located, whether the motorcycle is approaching it, how dangerous it is, and whether warning the rider would actually be helpful.

We also learned how important hardware placement and stabilization are. Even a strong detection model can become unreliable if the camera feed is constantly shaking or blurred.

The project taught us about:

Real-time computer vision
Object detection and classification
Camera stabilization
Motorcycle-specific safety risks
GPS and map integration
Real-time data sharing
Designing alerts that minimize rider distraction
Balancing AI accuracy, latency, and hardware limitations

Most importantly, we learned that combining physical hardware with software can create a stronger and more reliable solution than using a phone alone.

What's next for RideSight

Our next goal is to move from a prototype into a system that can be tested during real motorcycle rides.

We plan to improve RideSight by:

Training the model on more motorcycle-specific footage
Expanding detection for gravel, oil, water, and damaged roads
Improving distance and lane-position estimation
Adding night and bad-weather detection
Integrating Bluetooth helmet audio
Developing a weatherproof camera enclosure
Improving the vibration-isolation system
Adding automatic incident recording
Allowing riders to confirm or dismiss reported hazards
Creating a reliability score for community hazard reports

In the future, RideSight could become a compact safety device that works with any motorcycle and smartphone. It could also integrate directly with motorcycle dashboards, helmet communication systems, insurance programs, delivery fleets, and navigation platforms.

Our long-term vision is to create a connected safety network where every RideSight-equipped motorcycle helps make the road safer for every rider behind it.