Author: zexiy

Software

• Working on UI and app interface for the user.
• Finished testing of collision detect algorithm, ready for final integration
• Socket connection code verified and tested for data transmission

To Do

• Finish UI interface for user experience
• Integrate sensor data into app.

• Parts for car, as well as extra decawaves came in. Beginning installation of hardware for car now.
• Tested reliability of bicycle sensors, started integration of software and hardware of bicycle sensors.

• Wrote socket integration into collision detection code, awaiting debugging and live testing
• Started working with the API of the apps and sensors on the bicycle, and am working on passing live feed data from the sensors to the app.
• Waiting on hardware installation on the car side before integration can commence for the car

Team Status

• Individual hardware and software parts are currently making good progress. We estimate to be able to start integration by Monday, and to have all subsystems ready to begin integration by the Monday after that
• Plans for integration have been laid out, we know exactly what data needs to be transmitted from the hardware side to the software side
  • Speed for the car and bike
  • Location for the car and bike
  • Heading of the car and bike

Software 

• Basic Collision Detection program completed.
• Started testing of socket code between phone and laptop – theoretically should work fine between phones. Behavior is identical with regards to sockets.
• Prepared software for midterm demo! 😀

Schedule

We are on track to our goals for the midterm demo, however we need to work on integration ASAP, as we expect to encounter some bugs in the future.

To Do

1. Touch up on the final parts of the mid term demo.

2. Begin integration with hardware side ASAP.

Capstone Status Update

Software Design

• Algorithm design has been finalized
• Work began on the basic algorithm using Python. Python was chosen because it was a simple programming language to use, and existing software for running python code on an android device exist
• Installed and verified Python compiler on Android device.
• Implemented basic socket connection code to enable communications between both Android devices.
• Developed equations for calculating trajectories for vehicles (see design review report.

Schedule

On the software side, we are on schedule, however, we have been having delays regarding our bluetooth connection between the sensors and the android device.

To Do

1. Implement code for bluetooth connection with Raspberry Pi to pass sensor data.
2. Pull localization data off the decawave app for the collision detection algorithm.

Design Proposal for Software

As the primary designer for the software side of the project, I have spent this week identifying the workflow for collision detection, and also the required input from the hardware side of the project.

Required Input

• Relative Location and Distance of Car and Bicycle with respect to each other
• Absolute velocity of the car and bicycle.
• Steering input on bicycle and car

Workflow

1. Bluetooth decawave sensors do automatic pairing once the cyclist and driver are within range
2. This prompts phones that the decawave sensors are connected to, to also pair with each other and begin sharing information.
3. The bluetooth sensors send the data over the decaWave app to the phones (for both the car and the bicycle)
4. The software computes the relative position and velocity of both vehicles.
5. Machine learning determines based on current steering and velocity if the vehicles are going to turn in the near future and determine the probability of a collision in the future
6. If a collision is imminent, the phone will send an audio alert to its owner.
7. The phones will then check with each other to ensure that the other party has also been alerted.

Collision detection algorithm.

The non ML part of the collision detection algorithm has been fully designed. The current algorithm uses trigonometry between the different sensors to determine not only the position, but also the heading of both vehicles.

I am still looking into various options for the ML algorithm, currently I am looking into predicting steering by looking at vehicle velocity before a turn.

Schedule

We are somewhat behind schedule, since a vital part of our design process hinges on testing the capabilities of our sensors first, and we need our batteries delivered in order for us to conduct the testing. We aim to make up for this lack of progress next week, since the testing will give us experience with the sensors in order to finish the document.

On the software side, this setback is not a considerable one for the design, as we can work with black box input for the algorithm for now.

A major cause of cyclist injuries on the roads is due to collisions with vehicles, oftentimes due to drivers that simply did not notice the cyclist until it was too late. Our project aims to improve cyclist safety by creating a collision avoidance system that provides advanced warning for drivers and cyclists, alerting them to each other’s presence and enabling them to take evasive action.

We will use bluetooth technology to provide accurate real time position and velocity of both vehicles. When the system determines that a collision is imminent, it will send an audio alert, drawing the attention of both parties and prompting them to take the necessary actions to avoid a collision.