2. Problem 5.6 million accidents 34,000 fatalities 2,632,000 injuries 90% >30 MPH 25,000 fatalities Makes roadway usage inefficient!  Wasted fuel consumption  Decreased productivity

3. Objective Sensors monitor vehicle surroundings: Blind spot detection Forward collision detection Driver warning systems: Audible Vehicle Overhead Map

4. Functional Block Diagram – Level 0

5. Functional Block Diagram – Level 1

6. FBD – Voltage Regulator

7. FBD – Front Radar Sensor

8. FBD – Side & Rear Ultrasonic Sensors

9. FBD – BeagleBone Black (Processor)

10. FBD – Vehicle Map Display

11. FBD – Audible Collision Warning System

12. Final Solution

13. Ultrasonic Sensors  Maxbotix MB7380 (x4) : 5m range Cover area directly along both sides of car Enough to cover average lane width ~3.7m  Maxbotix MB7386 (x2) : 10m Cover deep blindspot for approaching vehicles

14. Ultrasonic Sensors  Pulse simultaneously at 7 Hz  Accurate to within 5 mm

15. Front Radar Sensor  Mounts on front bumper  Tracks up to 64 in range  Processes data about objects  Sends 74 line CAN messages every 50ms StartEnd ID4E0h4E1h4E2h4E3h500h Track1 …53Fh Track64 540h Msg 1 through 10 5E4h5E5h5E6h5E7h5E8h

16. Processing

17. Processing Ultrasonic Sensors  Python Script Thread for each sensor Sensor objects ○ Location ○ Voltage ○ Distance ○ Warnings Analog voltage read into ADC (100ms cycle) Voltage converted to distance based upon testing calibration  50ms cycle thread callback

18. Processing CAN Bus Data  Can-utils : Linux package for CAN bus access through command line Dump broadcasted CAN messages CAN messages filtered based upon message ID  Python script with 30ms cycle thread callback Issues can-utils functions through system calls Tracked objects data (max. 3) pulled off CAN bus ○ Position ○ Velocity ○ Acceleration ○ Angle Data read into queue, parsed, and passed onto collision detection algorithm

19. Collision Detection Algorithm  Takes sensor/radar data and determines what warnings to output  Undeveloped due to limited testing  Ultrasonic detection Basic warning: any object detected >0.5 seconds Imminent warning: minimum distance threshold (0.75 m)  Radar detection “Time to Collision” (TTC) calculated based upon tracked object position and velocity Basic warning: TTC <3 seconds Imminent warning: TTC <2 seconds ○ Conservative for ensuring safety during testing

20. Visual Warning System  LCD on dashboard provides overhead view of vehicle’s surroundings based upon sensor data  Driver is visually alerted of vehicles detected (red zones), as well as imminent collisions (yellow warning symbols)

21. Audible Warning System  Piezo buzzers located in driver’s headrest beep when collision is imminent Left buzzer = Left side imminent warning Right buzzer = Right side Both buzzers = Front Different frequency for forward warning to differentiate from multiple active side warnings

22. Testing  Modular Testing  Ultrasonic Sensors Voltage/Distance calibration  Radar DataView and CAN bus comparison  Processing Unit testing on Python scripts  Warning Systems  Car Installation

25. Cost ItemCostQuantityTotal BeagleBone Black$55.001 Ultrasonic Sensors$110.006$660.00 Delphi ESR$3600.001 Raspberry Pi$40.001 CAN Cape for BBB$39.951 Power Supply for BBB$7.951 Arduino$24.951 Piezo Elements$0.952$1.90 7” Display$59.951 Power Supply Filters$2.256$13.50 Mounts$3.006$18.00 Wires$3.001 Component Cost $4524.20

26. Break-Even Analysis Fixed Costs (estimate) CostQuantityTotal Factory/Labor$10,000,000 Variable Costs Mounts$3.006$18.00 Wires$3.001 BeagleBone Black$55.001 Ultrasonic Sensors$110.006$660.00 Delphi ESR$3,600.001 7” Display$59.951 Raspberry Pi$40.001 CAN Cape for BBB$39.951 Power Supply for BBB $7.951 Arduino$24.951 Piezo Element$0.952$1.90 Power Supply Filters $2.256$13.50 Total Variable Costs $4,524.20 Unit Price $5,000.00 Profit per Unit $475.80 5000x = 10000000 + 4524.20x x = 21,018 units to break even

27. Questions/Comments?