Estimating Uncertainty For Vehicle Motion Prediction on Yandex Shifts Dataset

12/15/2021
by   Alexey Pustynnikov, et al.
0

Motion prediction of surrounding agents is an important task in context of autonomous driving since it is closely related to driver's safety. Vehicle Motion Prediction (VMP) track of Shifts Challenge focuses on developing models which are robust to distributional shift and able to measure uncertainty of their predictions. In this work we present the approach that significantly improved provided benchmark and took 2nd place on the leaderboard.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/08/2021

Transformer based trajectory prediction

To plan a safe and efficient route, an autonomous vehicle should anticip...
research
12/02/2021

3rd Place Solution for NeurIPS 2021 Shifts Challenge: Vehicle Motion Prediction

Shifts Challenge: Robustness and Uncertainty under Real-World Distributi...
research
06/20/2022

MPA: MultiPath++ Based Architecture for Motion Prediction

Autonomous driving technology is developing rapidly and nowadays first a...
research
06/26/2020

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

Out-of-training-distribution (OOD) scenarios are a common challenge of l...
research
06/30/2022

Shifts 2.0: Extending The Dataset of Real Distributional Shifts

Distributional shift, or the mismatch between training and deployment da...
research
11/05/2020

Uncertainty-Aware Vehicle Orientation Estimation for Joint Detection-Prediction Models

Object detection is a critical component of a self-driving system, taske...
research
11/01/2020

Beelines: Evaluating Motion Prediction Impact on Self-Driving Safety and Comfort

The commonly used metrics for motion prediction do not correlate well wi...

Please sign up or login with your details

Forgot password? Click here to reset