This document discusses how to develop autonomous driving algorithms using a simulator on a developer PC. It outlines several autonomous driving algorithms like object detection and trajectory planning. It recommends using an LG simulator within Unity for environment modeling, ROS for data transmission, and Autoware as an algorithm framework. The document provides information on simulating sensor data, controlling vehicles in simulation, and testing autonomous driving algorithms within a simulated environment before using real-world testing.

1. Autonomous Driving on Your Developer PC.
Technologies, Approaches, and the Future.
2021

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I want to work on
autonomous driving.
Should I join Google,
Uber, or Tesla?

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Autonomous Driving Algorithms
● Object detection and recognition
● Object classification
● Mapping and localization
● Unsupervised laser calibration
● Trajectory planning
● Dynamic modeling and control

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Machine Learning in AD algorithms

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Autonomous Driving Levels

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How to Train and Test AD Algorithms
● Option 1: Build a city and pay people to actively create risky situations
● Option 2: Test in real cities
● Option 3: Simulate a city and simulate all required scenarios

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Simulation and Other AD Modules

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Simulation Instead of Real World Testing

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Technology Stacks to Use:
● LG Simulator to simulate of the environment
○ Unity for map creation
○ C# for specific sensor creation
● ROS2 for transferring data
● Autoware as algorithm bank and infrastructure
○ Docker for isolation
○ Python for automation
○ C++ for algorithm creation
● Any machine learning framework you like

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Robotic Operation System
Same transport, data types, and interfaces for real hardware and simulated data.

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LG Simulator
● Unity to model the world
● Controlled by Python
● Publish sensor data to ROS
● Already available sensors:
○ Camera
○ Lidar
○ GPS
○ Radar
● Provide ground truth
● Support custom sensors

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LG Simulator Python Control
To create various scenarios

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Grab Sensor Data from LG Simulator via ROS
To create various scenarios

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Control EGO Vehicle with Commands Over ROS
To create various scenarios

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Autoware Framework
To create various scenarios
● Contains a bunch of algorithms for AD
● Follows microservice approaches
● Uses ROS2 as the main interface for communication
● Works with real-world hardware
● Works with simulated sensors
● Has distributed applications and calculations

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Autoware Quick Start
To create various scenarios
● Provides a bunch of Docker containers and tools for
management
● Already contains an LG Simulator build
● Step-by-step guides are available
○ https://autowarefoundation.gitlab.io/autoware.auto/Auto
wareAuto/avpdemo.html

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Testing Algo Infrastructure
Simulator
Algo Test script
Scenario
Create env
Control
Sensor data
Ground truth data
Predicted

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Training Algo Infrastructure (Close-Ended)
Scenario
generator
Scenario 1
Scenario 2
Scenario 3
Scenario N
Cluster
Simulator 1
Simulator 2
Simulator N
Train data
Test data
ML algo

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Training Algo Infrastructure (Open-Ended)
Scenario
generator
Scenario 1
Scenario 2
Scenario 3
Scenario N
Cluster
Simulator 1
Simulator 2
Simulator N
ML algo
Sensor data
Ground truth

20. Thank You!