The 3rd place solution for competition "Lyft Motion Prediction for Autonomous Vehicles" at Kaggle

Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th place solution

Project on 3D Object Detection using Lyft's level5 dataset. Obtained mAP of 0.045 on the private leader board on kaggle and ranked in the top 20% among all teams participated in the competition.

Training code for kaggle competition, Lyft Motion Prediction for Autonomous Vehicles

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RNN model applied to "Lyft Motion Prediction for Autonomous Vehicles" on kaggle

Training and prediction code for the kaggle competition of Lyft 3d Object Detection for autonomous vehicles

WPI CS534 Term Project - Lyft Motion Prediction for Autonomous Vehicles where we build motion prediction models for self-driving vehicles

Solution for Kaggle competition "Lyft Motion Prediction for Autonomous Vehicles"

Description of the solution for the Lyft challenge https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles

Repo for the Kaggle competition - Level 5 Lyft Motion prediction

27th in 2019 Lyft 3D Object Detection Kaggle Competition

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#### Kaggle Code - Lyft

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Silver Medal in Kaggle Competition, Lyft Motion Prediction for Autonomous Vehciles

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Source code for Lyft 3D Object Detection (https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles)

Lyft Motion Prediction for Autonomous Vehicles: Kaggle kernels

Kaggle-Featured Code Competition

Analyzing Lyft rides data. Data set obtained from Kaggle

For Kaggle competition: Lyft 3D Object Detection for Autonomous Vehicles

Code of Kaggle Competition "Lyft Motion Prediction for Autonomous Vehicles"

This is a complete statistical breakdown of the Uber-Lyft dataset from Kaggle.

My 121st place solution to the Lyft Motion Prediction Competition hosted on Kaggle 🚗

Bike-sharing demand prediction is crucial for companies like Uber, Lyft, and DoorDash to manage service spikes and enhance customer experience. This project employs the AutoGluon library to develop models for Kaggle's Bike Sharing Demand competition, aiding in efficient resource allocation and reduced wait times.

As a part of my Advanced Predictive Modeling coursework at McCombs, my team and I participated in a live Kaggle competition for Lyft. The project was 3D Object Detection for Autonomous vehicles and focuses on detecting objects (vehicles and humans), enabled using the UNet Modeling architecture.

Repo for autonomous vehicle prediction

Lyft Motion Prediction for Autonomous Vehicles at Kaggle