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🤖 SelfDriveSuite: Vehicle Control and Scene Understanding

Note: This project is a culmination of my work for the "ROB 535: Self-driving Cars: Perception to Control" course, conducted from September to December 2023. The course provided a solid foundation in autonomous driving technologies, from perception to control, and this repository represents the integration and application of those concepts.

Simulation 1 Simulation 2 Simulation 3 Simulation 4

🎯 Goal

This a comprehensive project focused on developing algorithms for self-driving cars. This repository encompasses several critical components of autonomous driving technology, including:

  • Adaptive Cruise Control: Implementing an adaptive cruise controller to maintain safe following distances and speeds.
  • Model Predictive Control (MPC): Designing both linear and non-linear MPC for tracking reference trajectories and optimizing vehicle paths.
  • All-Weather Scene Understanding: Achieving robust real-time, object recognition, detection, and scene segmentation in challenging conditions, such as low visibility, fast-paced scenario, and/or adverse weather.

⚙️ Prerequisites

  • Libraries/Frameworks:
    • Numpy
    • Matplotlib
    • Scipy
    • CVXPY (for Convex optimization)
    • CasADi
    • pytorch

🛠️ Test/Demo

  • Adaptive Cruise Control
    • Go to the directory 'Vehicle Control\Adaptive Cruise Control', and launch the jupyter notebook
  • Trajectory tracking using Linear MPC
    • Go to the directory 'Vehicle Control\Trajectory tracking', and launch the jupyter notebook
  • Car Overtaking using Non-Linear MPC
    • Go to the directory 'Trajectory Optimization\CarOvertaking', and launch the jupyter notebook
  • Drag Racing using Non-Linear MPC
    • Go to the directory 'Trajectory Optimization\DragRacing', and launch the jupyter notebook
  • Image Classification
    • Go to the directory 'Image Classification', and launch the jupyter notebook
  • Object Detection
    • Go to the directory 'Object Detection', and run 'inference.py' or 'inference_video.py' following README there.
  • Scene Segmentation
    • Go to the directory 'Scene Segmentation', and launch the jupyter notebook

📊 Results

📈 Adaptive Cruise Control

📈 Trajectory tracking (using Linear MPC)

📈 Car Overtaking (using Non-Linear MPC)

📈 Drag Racing (using Non-Linear MPC)

  • Case 1:
  • Case 2:
  • Case 3:
  • Case 4:
  • Case 5:

📈 Image Classification (Cars vs Person vs None)

  • Challenging because low-resolution 32x32 blurry RGB images...still achieved 90% accuracy

📈 Object Detection (Traffic sign Detection)

  • Achieved ~48 mAP with an average FPS of 43

📈 Scene segmentation (Comprising 14 classes from urban scenario + background class)

  • Achieved 81.2 mIoU

Simulation 1 Simulation 2