🧩 Obstacle Prediction Puzzle: Predict obstacles in front of self driving car

🛠 Start Solving

🗃 Explore Dataset

🕵🏼‍♀️ What Is Obstacle Prediction Puzzle About??

In this puzzle, you will have the radar sensor points mounted on a self-driving car moving through a town. Your goal will be to classify if there we any moving obstacles in front of the self-driving car. These obstacles can be pedestrians or another car within a pre-defined distance threshold. In this binary classification task, an output of 1 means an obstacle, and 0 means there is no obstacle.

💪🏼 What You’ll Learn

In this puzzle, you will learn

1. Binary Classification using various sklearn classifiers

Let’s get started! 🚀

📝 The Task

In this puzzle, you will use the car radar from the dataset to build an automated algorithm to predict the presence of dynamic objects in front of the self-driving car. This is a binary classification task where an output of 1 means there is an obstacle and 0 means there is no obstacle.

👩🏽‍💻 Explore Dataset

The dataset contains 8000 samples. The training and testing samples are both contained in data.npz. The dataset contains information about the radars points generated from a Carla Simulator. It contains information on whether a dynamic object (pedestrian or vehicle) was within a distance threshold or not.

The individual radar points of single training sample features contain below metadata -

🗂 Dataset Files

• data.npz - ( 5000 training and 3000 testing samples )- This npy file contains the training and testing sets. You can use the NumPy function np.load to read the file
• sample_submission.csv - It contains the random labels for testing data used for testing purposes and to make sure that your submission format is correct.

🔏 Evaluation Criteria

The evaluation metrics for this competition are F1 Score (Primary Score) and Accuracy (Secondary Score)

🚀 Getting Started

The starter kit breaks down everything from downloading the dataset, loading the libraries, processing the data, creating, training, and testing the model.

Click here to access the basic starter kit. This will share in-depth instructions to

1. Download the necessary files
2. Setup the AIcrow-CLI environment that will help you make a submission directly via a notebook
3. Downloading dataset & importing libraries
4. Preprocessing the dataset
5. Creating the model
6. Setting the model
7. Training the model
8. Submitting the result
9. Uploading the results

Check out the starter kit here! 🚀

📨 How To Make A Submission

1. Creating a submission directory
2. Use test.csv and fill the corresponding labels.
3. Save the test.csv in the submission directory. The name of the above file should be submission.csv.
4. Inside a submission directory, put the .ipynb notebook from which you trained the model and made inference and save it as original_notebook.ipynb.
5. Overall, this is what your submission directory should look like

submission
├── submission.csv
└── original_notebook.ipynb

🤫 Hint to get started

1. Try using various sklearn classifiers and see which one gives the best score.
2. Check out the notebook using one such classifier, Random Forest.

📚 Resource Circle

Check all the sklearn classifiers here.

👯‍♀️ Get Help From Community

Hop over to the AIcrowd Blitz discord server to see ongoing discussions about this puzzle.

🙋‍♀️ Subscription Queries

This is one of the many free Blitz puzzles you can access forever. To access more puzzles from various domains from the Blitz Library and receive a special new puzzle in your inbox every 2 weeks, you can subscribe to AIcrowd Blitz here.