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Robotics for Autonomous Vehicles by University of Toronto

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Autonomous_Vehicles_Specialization

Projects while studying the Autonomous Vehicles special course by University of Toronto

Course 1 : Mechanical Modeling & Control Methodologies

  • kinematic, dynamic modeling of the autonomous vehicle model
  • system modeling of the environment: rule-based, real-time
  • error dynamics and optimization
  • longitudinal, lateral controllers (Pure pursuit, Stanley, MPC algorithm)

Course 2: Localization

  • state estimation using Least Squares method and KalmanFilter Algorithm
  • sample question for extended KF algorithm
  • state estimation and path finding using ErrorState-ExtendedKalmanFilter(ESEKF) Algorithm with SensorFusion (LiDAR, GNSS, IMU) + Visualization

Course 3: Perception

  • 3D Camera Perception via geometry and disparity algorithm
  • Estimate drivable plane with semantic segmentation and RANSAC algorithm
  • Lane estimation with Canny Edge Detection and Hough Tranformation algorithm with threshold filtering & merging
  • Computing minimum distance for collision avoidance with object detection and semantic segmentation

Course 4: Motion Planning

  • Mapping the gathered sensor data of external environment into proper map type in real-time (e.g. Ocupancy Grid)
  • Find the most efficient route with search algorithm
  • Total Motion Planning:
    • Local Planner
    • Collision Checker
    • Behaviour Planner
    • Path Optimization
    • Velocity Planner