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

Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th place solution

Deep learning models for self-driving vehicles to predict other car/cyclist/pedestrian (called "agent")'s motion.

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

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

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"

Autonomous vehicles (AVs) are expected to dramatically redefine the future of transportation. However, there are still significant engineering challenges to be solved before one can fully realize the benefits of self-driving cars. One such challenge is building models that reliably predict the movement of traffic agents around the AV, such as cars, cyclists, and pedestrians. The ridesharing company Lyft started Level 5 to take on the self-driving challenge and build a full self-driving system (they’re hiring!). Their previous competition tasked participants with identifying 3D objects, an important step prior to detecting their movement. Now, they’re challenging you to predict the motion of these traffic agents. In this competition, you’ll apply your data science skills to build motion prediction models for self-driving vehicles. You'll have access to the largest Prediction Dataset ever released to train and test your models. Your knowledge of machine learning will then be required to predict how cars, cyclists,and pedestrians move in the AV's environment. Lyft’s mission is to improve people’s lives with the world’s best transportation. They believe in a future where self-driving cars make transportation safer, environment-friendly and more accessible for everyone. Their goal is to accelerate development across the industry by sharing data with researchers. As a result of your participation, you can have a hand in propelling the industry forward and helping people around the world benefit from self-driving cars sooner. This is a Code Competition. Refer to Code Requirements for

Repo for the Kaggle competition - Level 5 Lyft Motion prediction

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Lyft Motion Prediction MPC path optimization

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

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

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PyTorch code for training, testing and inference on the Lyft Level5 dataset.

Kaggle-Featured Code Competition

Lyft Motion Prediction for Autonomous Vehicles: Kaggle kernels

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

test

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Use data from lyft's self driving cars to predict the motion of objects

We are predicting the motion of the objects in a given scene. For test, you will have 99 frames of objects moving around will be asked to predict their location in the next 50.

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Lyft Motion Prediction Challenge

https://www.kaggle.com/c/lyft-motion-prediction-autonomous-vehicles

Code for Lyft Lyft Motion Prediction for Autonomous Vehicles

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