CycleSafe - #1 - Introduction

I'd like to thank HAMMOND and Element14 for the selection of my CycleSafe project proposal for this design challenge!

Project goals

The goal of CycleSafe project is to improve safety of cyclists on the road. The secondary goal is to make it affordable for a wider community.

Project Overview

The project will use RPi 4, a camera, and machine learning algorithms to detect cars and trucks in the back of the cyclist and warn of potential collisions. The camera will classify continuously objects and will calculate the distance. If an object is recognized as a car or a truck by measuring the distance to it and the change of the distance it will be able to warn cyclists of the potential collision.

Object Detection and Classification
Implement an ML algorithm for efficient and accurate object recognition.
Continuously analyze the camera feed to identify objects in the cyclist’s vicinity.

Distance Calculation
Use computer vision techniques to estimate the distance between the cyclist and detected objects.
Combine depth perception with object size to calculate accurate distances.

Cars and Trucks Recognition and Tracking
Focus on identifying cars and trucks specifically.
Maintain a list of recognized car objects and their positions.

Collision Warning System
Monitor changes in distance to approaching cars.
If the distance decreases rapidly, trigger a warning signal (LED for cyclists, flash taillight to alert cars behind and buzzer).

User Interface and Alerts
Design a simple user interface (UI) for the cyclist.
Communicate real-time information about detected cars/trucks and their proximity.
Provide clear alerts when collision risk is high.

Testing and Optimization
Conduct extensive testing in various scenarios (day/night, different speeds, different environmental conditions, including rain, snow, vibration, dust ).
Optimize the system for accuracy, low latency, and minimal false positives/negatives.

Power Efficiency and Durability:
Optimize for energy-efficient components to prolong battery life.
Ensure the system is robust and weather-resistant.

Components

HAMMOND 1554VA2GYCL Plastic Enclosure, Watertight, Clear Lid, PCB Box, Polycarbonate, 88.9 mm, 160 mm, 240 mm, IP68

RASPBERRY-PI CM4104000 Raspberry Pi Compute Module 4 Lite, 4GB RAM, Wireless, BCM2711, ARM Cortex-A72

RASPBERRY-PI CM4IO Compute Module 4 I/O Board, Raspberry Pi, BCM2711, ARM Cortex-A72

RASPBERRY-PI RPI 8MP CAMERA BOARD Daughter Board, Raspberry Pi Camera Board, Version 2, Sony IMX219 8-Megapixel Sensor

Portable power supply

Bike rear rack

Fixtures

Potential Challenges

Creating a machine learning algorithm that can process video (object detection and classification) in near-real time using RPi4

Achieve acceptable distance calculation precision

Environmental impact (vibration, rain, snow) on hardware and video processing

Securely mounting hardware on a bicycle

Optimize power consumption to allow at least 60 minutes of ride without recharging.