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Sharing Pixelopolis, a self-driving car demo from Google I/O built with TF-Lite

↗ Quelle (blog.tensorflow.org)
🗣️ Stimme:
📑 Inhaltsübersicht
Posted by Miguel de Andrés-Clavera, Product Manager, Google PI

In this post, I’d like to share with you a demo we built for (and had planned to show at) Google I/O this year with

Pixelopolis

Pixelopolis is an interactive installation that showcases self-driving miniature cars powered by , which contains a version of an implementation is a good option to make projects like this possible. Processing video and detecting objects are much more difficult using Cloud-based methods - due to latency. If you can, doing it on-device is much faster.

Users can interact with Pixelopolis via a “station” (an app running on a phone), where they can select the destination the car will drive to. The car will navigate to the destination, and during the journey, the app shows real-time streaming video from the Car -- this allows the user to see what the car sees and detects. As you may notice from the gifs below, Pixelopolis has multilingual support built-in as well.
Car App

How it works

Using the front camera on a mobile device, we perform lane-keeping, localization and object detection right on the device in real-time. Not only that, in our case, the Pixel 4 also controls the motors and other electronic components via USB-C, so the car can stop when it detects other cars or turn at a right interaction when it needs to.

If you’re interested in technical details, the remainder of this article describes the major components of the car, and our journey building it.

Lane-keeping

We explored a variety of models for Lane-keeping. As a baseline, we used a CNN to detect the traffic lines in each frame and adjust the steering wheel every frame, which works fine. We improved this by adding an LSTM and using multiple previous frames. After experimenting a bit more, we followed a similar model architecture to this CNN model input and output

Model Architecture

TEXT 
net_in = Input(shape = (80, 120, 3))
x = Lambda(lambda x: x/127.5 - 1.0)(net_in)
x = Conv2D(24, (5, 5), strides=(2, 2),padding="same", activation='elu')(x)  
x = Conv2D(36, (5, 5), strides=(2, 2),padding="same", activation='elu')(x)
x = Conv2D(48, (5, 5), strides=(2, 2),padding="same", activation='elu')(x)
x = Conv2D(64, (3, 3), padding="same",activation='elu')(x)   
x = Conv2D(64, (3, 3), padding="same",activation='elu')(x)
x = Dropout(0.3)(x)
x = Flatten()(x)
x = Dense(100, activation='elu')(x)
x = Dense(50, activation='elu')(x)
x = Dense(10, activation='elu')(x) 
net_out = Dense(1, name='net_out')(x)
model = Model(inputs=net_in, outputs=net_out)

Data Collection

Before we are able to use this model, we need to find a way to collect the image data from the car to train. The problem is we didn’t have a car or a track to use at the time. So, we decided to use a simulator. We chose Unity and this Multiple waypoints on the track in the simulatorBy setting multiple waypoints on the track, the Data Augmentation with various environmentsSince we do all data collection within the simulator, we need to create various environments in the scene because we want our model to be able to handle different lighting, background environment and other noises. We added these variables to the scene: random HDRI sphere ( with different rotation and exposure values), random brightness and color, and random cars.

Training


Early versions of the toy car running off the track/td>
Later, we found out that we only trained the model using mostly straight tracks. To fix this imbalance data issue, we added various shapes of curves.
Car successfully turn at the corners

Training with the final track design

Training the model with additional routing
We tested out many solutions and went with the one that was most simple and effective. We cropped only the bottom ¼ of the image and fed it to the lane keeping model, then adjusted the model input size to 120x40 and it works like a charm.
in TensorFlow object detection model zoo. But, for the Pixel 4 edge TPU, we use the ssd_mobilenet_edgetpu model.

Pixel 4 Edge TPU model performance

Data labelling and Simulation

We use image data from both simulation and real scenes to train the model. Next, we developed our own simulator for this using .
Data labeling with labelImg

Training

to monitor training progress. We use it to evaluate mAP (mean Average Precision), which normally you have to do it manually.
Detection result and the groundtruth

TensorFlow Lite

Since we want to run our ML model on the Pixel 4, which is running Android, we need to convert all the models to .tflite. Of course, you can use TensorFlow lite to target iOS and other devices as well (including microcontrollers). Here are the steps we did:
Lane keeping
First, we convert the lane keeping model from .h5 to .tflite by using
TEXT 
import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_keras_model_file("lane_keeping.h5")
model = converter.convert()
file = open("lane_keeping.tflite",'wb') 
file.write( model )
file.close()
Now, we have the model ready for the Android project. Next, we build a lane keeping class in our app. We began with an example android project from script to convert .ckpt to .pb file (the script already provided in Tensorflow object detector API)
  • Using toco: TensorFlow Lite Converter to convert .pb to .tflite format

    • Using Neural Core

    We use an Android sample project from
    Node/Edge
    Since we will have a fleet of cars running around in the city, we need to find a way to navigate them. We use the Node/Edge concept. Node is a place on the map and Edge is the path between two Nodes. We then map each node to the actual signs in the city.
    as our development board. We chose
    NUCLEO-F411RE
    We designed and developed a shield for additional components such as motors to reduce the number of wires inside the car chassis.There are three parts in the shield: 1) Battery measurement in voltage, 2) On/off switch with MOSFET, 3) Buttons.
    NUCLEO-L432KC

    Car Chassis & Exterior

    Mark I

    Mark II Design
    We added a battery measurement circuit to the board and cut off the power when the phone detached from the board.

    Mark III

    Mark IV Design
    We moved all the control buttons and status LEDs to the back of the car for an easy access.

    Mark V

    Roadmap

    There are many areas that we plan to improve this experience.

    Battery

    Currently, the motor and the controller board are powered by three packs of 3000mAh lithium-ion batteries and we have a charging circuit to handle the charging process. When we want to charge the battery, we would need to move the car to the charging station and plug the power adapter to the back of the car to charge. This has a lot of downsides because the car won’t be able to run on the track if it’s charging and the charging time is a few hours which is quite long.
    Localization with SLAM
    Localization is a very important process for this installation and we would like to make it more robust by adding SLAM to our app. We believe that this would improve the turning mechanism significantly.

    Learning more

    Thanks so much for reading! It's incredible what you can do with a phone camera, TensorFlow and a bit of imagination. Hopefully, this post gave you ideas for your own projects - we learned a lot working on this one, and hope you will in yours as well. The article provides links to resources for you to delve deeper into all the different areas and you can find plenty of ML models and tutorials by the developer community to learn from on . It’s perfect for engineers & students looking for complete training in all aspects of self-driving cars, including computer vision, sensor fusion & localization.

    Acknowledgements

    This project would not have been possible without the following awesome and talented group of people: Sina Hassani, Ashok Halambi, Pohung Chen, Eddie Azadi, Shigeki Hanawa, Clara Tan Su Yi, Daniel Bactol, Kiattiyot Panichprecha Praiya Chinagarn, Pittayathorn Nomrak, Nonthakorn Seelapun, Jirat Nakarit, Phatchara Pongsakorntorn, Tarit Nakavajara, Witsarut Buadit, Nithi Aiempongpaiboon, Witaya Junma, Taksapon Jaionnom and Watthanasuk Shuaytong.
    Vollständiger Original-Bericht
    Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org.
    ↗ Original-Artikel auf blog.tensorflow.org lesen
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