Month: November 2018

What we did as a group:

• We all had reduced workload because of thanksgiving. Work was mostly done remotely. We continued working on the algorithm and the recording. We did feasibility research for a new features we were thinking of.

Hubert:

• Feasibility research on animation for instructing drivers to get closer or further from the right
  • This will be during the stage where we’re trying to get to the position that’s parallel with the reference car in front of the parking spot.

Zilei:

• Progress on the algorithm
  • Still using text overlay for now, pending integration with the audio in the upcoming week
  • Redesign of different sub-states within a state
    • currently a mess in code, but pretty comprehensive of the different scenarios

Yuqing:

• Recording of the audio files
• Playing audio files from the android app

 

What we worked on as a group

• Code implementation for parking algorithm
• Preliminary testing for state detection accuracy

• Preliminary draft for instruction generation
  • Verbal instruction
  • Plot of desired position on our obstacle outline

 

Hubert

• Cont’d work on headless start
  • emulator doesn’t have cellular and cannot create mobile hot spot
• Plotting the desired position for the initial parallel position

• Adding code infrastructures for debugging and plotting destination car positions.

Zilei

• Set up code structure for parking algorithm
• State detection & handling different cases
  • Required environment: existence of a reference vehicle,
  • display debug statements to textfield
  • Idle state
    • idle, waiting for button to start
    • could potentially add beeping when vehicle gets too close to surrounding
  • Search state
    • the most complicated of all
    • Will rely on readings from
      • a designated parking sensor on the rear right end of the vehicle to determine whether the car is parallel with the reference car in wrt the y-axis
      • all right sensors & parking sensor for when the distance is too close, in range, too far, or the reference car completely out of sight
        • angle with respect to the reference car
  • Reverse right state
  • Reverse left state

Yuqing

• Restart & end algorithm buttons
  • change text
  • communicate with the algorithm class

• Researched on packages available for playing verbal instruction
• Integrated one sample sound into the App

What we did as a team:

• Integrated all parts of the system and demoed the first version of the app (without parking instruction) on an Android device
• Experimentally determined the exact points of references when generating parallel parking instruction
  • We assigned parking lot on an empty space
  • Yuqing drove the testing vehicle while Zilei and Hubert experimented with giving different instructions at different reference points
  • Iterated the process with different reference points and different instructions and decided on the first version of our reference points
  • Testing setup
  • 
  • What we found out:
    • The parking spot needs to be at least of dimension 3m x 1.2m
    • The exact point at which the driver needs to turn the steering wheel all the way to the right
    • The exact point at which the driver needs to turn the steering wheel all the way to the left
    • 
    • The systematic approach specified above ensures mostly successful parallel parking into the allocated parking spot

Hubert

• Updated ultrasonic sensors driver to minimize the delay of outline plotting
• Refined Gyroscope driver to ensure handling of errors occurred during accidental disconnection

Zilei

• Drafted a flow chart for Parking Instruction Generation algorithm

• Helped experimentally determined reference points for the testing vehicle in real life
  • Removal of the “straight reverse” stage
  • Determined the approximate size of the spot we need
  • When to steer left from full right

Yuqing

• Refined path plotting algorithm to reflect more realistic projection of car path by measuring the curvature offset of the car path in the video image
• Fixed multiple bugs in the current plotting algorithm

What we did as a group

• Reconnected jumper wires
• Changed our current network protocol, so that no socket is reused and RPI can handle the video streaming between socket read/writes. This solved our previous problem not being able to stream & draw
• Changed ultrasonic sensor pulling rate so that (1) no outdated data is being used to draw contour,  (2) delay for each reading is less. We improved our reading delay significantly.
• Progress was made toward path prediction shown on the outline graph and path prediction overlay on the streamed video. Testing still needs to done next week to fine tune those.
• Prepare for midpoint demo: update our schedule & distribute work for the next 5 weeks

Hubert

• Rearranged socket code so that RPI can execute stream-related code when the outline drawing isn’t requesting data
• Worked on Arduino code for ultrasonic sensor sampling algorithm
  • Changed continuous write to pull based write
  • Changed median method to sample fewer values for lower delay without noticeable decrease in accuracy
  • Worked around serial race condition to decrease delay

Yuqing

• Debug socket connection & analyze the problem
• Worked on front end and aesthetics
  • Buttons
  • Shaded area
• Worked on path prediction overlay & finish path prediction graph on the outline
• 

Zilei

• Debug socket connection & analyze the problem
• Debug Arduino readings after it was changed from continuous write to pull-based
• Prepare for demo & revised Gantt Chart