nnop / autonomous_vehicles

Autonomous Vehicles

Acknowledgements

Contents

Part: Basics

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• Item: Introduction to Self-Driving Cars
• Item: Automotive Functional Components
• Item: Dynamics of Automobiles

Applications

• Item: Vehicle Modeling with Chronos
• Item: Longitudinal Vehicle Model Simulation

Part: Environment Perception

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• Item: Hardware for Self-driving Cars
• Item: LiDAR Principles
• Item: LiDAR Point Clouds
• Item: Iterative Closest Point Algorithm
• Item: Inertial Measurement Unit
• Item: Global Navigation Satelite Systems
• Item: The Camera Sensor
• Item: The Camera Calibration
• Item: Visual Depth Perception
• Item: Image Filtering
• Item: Image Features and Feature Detectors
• Item: Image Feature Descriptors
• Item: Image Feature Matching
• Item: Image Feature Matching-Handle Ambiguity in Matching
• Item: Outlier Rejection RANSAC Algorithm
• Item: Visual Odometry
• Item: Introduction to Multiple Object Tracking
• Item: Sensor Noise and Aliasing
• Item: Environment Representation
• Item: Sensor Calibration

Applications

• Item: Using PCL
• Item: Read and Display Image With OpenCV
• Item: Managing Videos with OpenCV
• Item: Lane Detection
• Item: Apply Stereo Depth to a Driving Scenario
• Item: Visual Odometry for Localization in Autonomous Driving
• Item: Computer Vision with Raspberry Pi
• Item: Ultrasonic Sensor Control with Arduino
• Item: OpenCV Object Tracking

Part: Environment Mapping

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• Item: Occupancy Grids
• Item: Simultaneous Localization and Mapping (SLAM)
• Item: FastSLAM

Part: Motion Planning & Navigation

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• Item: Motion Planning
• Item: Driving Missions, Scenarios, and Behaviour
• Item: Motion Planning Constraints
• Item: Trajectory Propagation
• Item: Collision Checking
• Item: Trajctory Rollout Algorithm
• Item: Dynamic Windowing
• Item: Parametric Curves
• Item: Path Planning Optimization
• Item: Conformal Lattice Planning
• Item: Velocity Profile Generation

Part: Control

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• Item: PID and LQR Control
• Item: Longitudinal Control
• Item: Lateral Control
• Item: Model Predicitve Control

Applications

• Item: PI Cruise Controller
• Item: Longitudinal Control
• Item: Kinematic Bicycle Model Control
• Item: PID Motor Control in Raspberry Pi

Part: State Estimation

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• Item: Kalman Filter
• Item: Non-linear Filters
• Item: Extended Kalman Filter
• Item: Error State Extended Kalman Filter
• Item: Unscented Kalman Filter
• Item: Particle Filters
• Item: Monte Carlo Sampling
• Item: Importance Sampling
• Item: Sequential Importance Sampling (SIS)
• Item: Rao-Blackwellized Particle Filters
• Item: Sensor Fusion

Applications

• Item: Kalman Filter
• Item: Extended Kalman Filter

Part: Machine Learning for Autonomous Vehicles

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• Item: Deep Learning for Autonomous Vehicles
• Item: Feed Forward Neural Networks
• Item: Output Layers and Loss Functions
• Item: Neural Netwrok Training with Gradient Descent
• Item: Data Splits and Neural Network Performance Evaluation
• Item: Neural Network Regularization
• Item: Colvolutional Neural Networks
• Item: The Object Detection Problem
• Item: 2D Object Detection with CNNs
• Item: Trainign vs Inference
• Item: The Semantic Segmentation Problem
• Item: Convolutional NN for Semantic Segmentation
• Item: Semantic Segmentation for Road Scene Understanding
• Item: Deep Reinforcement Learning for Autonomous Vehicles

Applications

• Item: Getting Started With PyTorch
• Item: Automatic Differentiation PyTorch
• Item: Neural Networks in PyTorch
• Item: Environment Perception for Self-Driving Cars

Part: Software Architecture & Safety

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• Item: Software Architecture for Autonomous Driving Stack
• Item: Safety Assurance for Autonomous Vehicles
• Item: Introduction to AUTOSAR
• Item: Testing Autonomous Vehicles

Part: Appendices

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• Item: Coordinate Transformations
• Item: Reference Frames
• Item: Use SFTP to Transfer Code to Raspberry Pi
• Item: Use Flask to Establsh an Image Server

Material

• Useful Posts
• Useful Videos
• Courses
• Links
• Books & Papers
• Useful Notebooks & Projects

Contact