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Auto Drive Assist ​

DifficultyAdvanced
Team Size2-3 people
Time~25-30 hours
Demo-ready byStep 4
PrerequisitesPython, accelerometer/gyroscope basics, basic ML
Built byTesla Autopilot, Comma.ai, Mobileye, Waymo

Skills you'll earn: IMU sensor fusion, real-time data processing, driving behavior analysis, alert systems, kalman filtering

Start with reading an accelerometer. End with a road inclination detection and driver assist system.

Step 1: Read accelerometer data ​

You need to know the orientation of the vehicle.

  • Set up an Arduino or Raspberry Pi with an MPU6050 IMU (accelerometer + gyroscope)
  • Read raw accelerometer values (X, Y, Z) over I2C
  • Print the values to the serial monitor
  • Observe how values change when you tilt the sensor

You now have: Raw motion data.

Step 2: Calculate inclination angle ​

Raw values aren't intuitive. You need degrees.

  • Calculate pitch and roll angles from accelerometer data using atan2
  • Pitch = forward/backward tilt (uphill/downhill)
  • Roll = left/right tilt (banking)
  • Display the angles in real time
  • Smooth the readings with a complementary filter (blend accelerometer and gyroscope data)

You now have: Real-time inclination measurement.

Step 3: Visualize on a dashboard ​

Numbers scrolling on a serial monitor aren't useful while driving.

  • Send sensor data from the Arduino to a Raspberry Pi (or phone) via serial or Bluetooth
  • Build a web dashboard with Flask or FastAPI
  • Show a virtual horizon (like an aircraft attitude indicator) using Canvas or SVG
  • Color-code: green = flat, yellow = moderate incline, red = steep

You now have: A visual inclinometer.

Step 4: Incline alerts ​

You're focused on the road. You need audio warnings, not a screen.

  • Define thresholds: > 15° pitch = warning, > 25° = danger
  • Play audio alerts using a buzzer (hardware) or the Web Audio API (software)
  • Different tones for different severity levels
  • Voice alerts: "Steep incline ahead — reduce speed"

You now have: Driver warnings.

Step 5: Speed integration ​

Inclination alone isn't enough. Going 80 km/h on a steep decline is different from 20 km/h.

  • Add a GPS module (NEO-6M) for speed data
  • Combine speed + inclination to assess risk: fast + steep = critical
  • Log GPS coordinates alongside inclination data
  • Show speed on the dashboard

You now have: Speed-aware risk assessment.

Step 6: Route logging and replay ​

  • Log all sensor data with GPS coordinates and timestamps
  • Replay a route on a map showing inclination at each point
  • Export as a GPX file with custom extensions for inclination
  • Use Leaflet to visualize the route on a map, color-coded by steepness

You now have: Route analysis.

Step 7: Predictive warnings ​

  • Build a database of known steep segments from logged routes
  • When approaching a known steep section (based on GPS), warn in advance
  • Crowdsource data: upload routes to a server, build a shared incline map

Step 8: OBD-II integration ​

  • Connect to the vehicle's OBD-II port using an ELM327 adapter
  • Read engine RPM, throttle position, brake status
  • Correlate with inclination: suggest gear changes on steep inclines
  • Detect if the driver is riding the brakes on a long downhill

Useful Resources ​

Where to go from here ​

  • Lane departure detection using a camera
  • Forward collision warning (ultrasonic or camera)
  • Integration with Android Auto / Apple CarPlay
  • Autonomous emergency braking prototype (simulation only)
  • Fleet management: monitor multiple vehicles from a central dashboard