Lyft Motion – 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 Lyft Motion 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.
Awards:-
Participants with the best score on the private leaderboard are eligible to receive
- 1st Place – $12,000
- 2nd Place – $8,000
- 3rd Place – $6,000
- 4th Place – $4,000
Additional Opportunity
Beat the benchmark and you can receive $300 in GCP credits! Competitors who score higher than the host benchmark (when available) can fill out this survey form and receive a GCP coupon code. Request deadline is October 30, 2020. Limited coupons available, one coupon per user.
Deadline:- 18-11-2020
