Intelligent safety and monitoring system for motorcycle fleets
AI-powered all-in-one rider assistance and fleet management solution
Services
UX/UI research and design
Web app development
Mobile app development
Product management
Technology
React.js
Node.js
Kotlin
Swift
Node.js
Kotlin
Swift
Challenge
Addressing the rising accident rates among commercial motorcycles
Commercial motorcycles have become an increasingly prominent part of modern urban mobility. Unfortunately, this has been accompanied by a global rise in accidents involving them, particularly among delivery riders. The primary causes typically include speeding, tailgating, sudden swerving, and other similar unsafe manoeuvres.
At the request of a delivery fleet owner, the Bamboo Apps team designed a solution to improve rider safety and give the company better oversight of incidents.
Solution
All-in-one platform for rider safety and fleet management
To meet the client’s needs, Bamboo Apps delivered a comprehensive solution combining two key interrelated systems:
- AI-powered rider assistance;
- cloud-based system for fleet monitoring and analytics.
Leveraging sensors and computer vision, the assistance system monitors the rider’s 360-degree surroundings in real time. It proactively warns them of potential hazards through visual and audio alerts, giving critical extra seconds to respond. As a result, the solution significantly reduces risks to rider safety and lowers the potential costs associated with accidents.
In turn, the monitoring and analytics system delivers data to fleet operators and regulators instantly, enabling swift incident response and improving overall fleet coordination efficiency.
Elaboration
Safety, monitoring, and analytics at scale
The platform combines hardware and software components into a unified ecosystem, delivering extensive capabilities for proactive rider safety, incident monitoring, and optimised fleet management.
Rider assistance
A key component of the solution is the real-time visual and audio alert indicators managed by the Rider Alert Unit. Discreetly mounted on the stems of the motorcycle’s side mirrors, the device delivers signals directly into the rider’s peripheral vision and hearing range. This approach provides rapid warning of threats while minimising distraction for the rider.
Among the main warning signals are:
- collision alert;
- safe distance alert;
- blindspot alert;
- dangerous overtaking alert.
The system can be installed on any type of motorcycle, including the rapidly growing segment of electric motorcycles. At the same time, the built-in cellular module ensures the rider remains independent of their phone.
Monitoring system
The platform collects and consolidates fleet data in real time to monitor, track, and assess safety. It gives operators continuous insight into rider behaviour and surroundings, helping them stay in control at all times.
This includes:
- data analytics;
- video footage captured from bike cameras;
- live telematics;
- urban safety heatmap;
- …and more.
Over-the-Air updates
The system supports remote AI model and software updates through cloud management. As a result, safety algorithms are continuously improved and new features added without physical access to devices, keeping riders protected with the latest enhancements.
Specifically, the solution enables:
- version updates;
- new alerts;
- new system capabilities;
- ON/OFF features.
Remote system analytics
The platform performs automated remote health checks to ensure the proper functioning of all critical components, including:
- camera (position and view);
- Rider Alert Unit;
- communication modules.
Such smart diagnostics help identify potential issues before they escalate, minimising downtime and maintaining consistent system performance.
Automated weekly reports
The automated reporting feature turns vast amounts of data into actionable insights that support smarter operational decisions.
These insights cover:
- safety statistics and abnormalities;
- motorcycle utilisation;
- fleet productivity;
- …and other key metrics.
Development
Adaptive engineering to meet complex requirements
When designing both systems, the Bamboo Apps development team faced technical challenges that required tailored, specialised solutions in the areas of low-latency local data processing and scalable cloud infrastructure.
Real-time video processing
High-resolution wide-angle cameras, mounted at the front and rear of the motorcycle, continuously capture lane positioning and surrounding traffic conditions. To enable on-the-fly video analysis with minimal latency on limited resources, we used a hardware solution based on the NVIDIA AI Processing Unit (APU), which processes data directly on the motorcycle.
Reliable object detection
Based on the processed video stream, the system must detect cars, trucks, and other vehicles under varying lighting, weather, and traffic conditions – all from a motorcycle’s constantly shifting viewpoint. To ensure high-quality real-time object identification without relying on cloud services, we selected a computer vision framework specifically optimised for edge devices.
Minimising false alerts
Safety warnings had to be accurate and context-aware. Misleading or missed alerts could put the rider at risk rather than protect them.
We leveraged the machine learning module integrated into the APU, along with advanced algorithms, to perform contextual threat assessment that takes into account vehicle speed, traffic density, road conditions, and rider behaviour patterns. The system continuously compares camera and sensor readings with these factors to filter alerts and adjust sensitivity, minimising the likelihood of false warnings.
Achieving minimal alert latency
For alerts to be truly effective, they must be not only accurate but also delivered instantly. At the same time, cloud-based data processing can sometimes cause unpredictable delays due to network connectivity issues.
To address this, we developed a customised system for local alert processing. This eliminated reliance on external services and ensured consistently low latency from threat detection to Rider Alert Unit activation.
Scaling on-the-fly cloud data processing
The monitoring system had to reliably handle data streams from several thousand motorcycles simultaneously, including video footage, telemetry, GPS coordinates, and safety alerts. At this scale, daily data volumes reached terabytes, with peak-hour loads exceeding normal levels by a factor of three to five.
Therefore, we designed a microservices-based architecture with horizontal scaling capabilities, using distributed stream processing and in-memory caching for critical information. The system ensured reliable, low-latency performance and adapted automatically to fluctuating data loads.
Reliable transmission of critical data
Given mobile network instability, the system needed to guarantee the delivery of emergency alerts, critical telemetry, and messages, while optimising bandwidth usage for bulk data.
To achieve this, we implemented a prioritised data transmission system with multiple reliability levels. Critical alerts are sent via redundant communication channels, while bulk data is compressed and synchronised later, once the connection is restored.
Results
Successful delivery
The solution was successfully delivered and is now undergoing testing in the client’s fleet.