MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving

Abstract

To plan a safe and efficient route, an autonomous vehicle should anticipate future motions of other agents around it. Motion prediction is an extremely challenging task that recently gained significant attention within the research community. In this work, we present a simple and yet very strong baseline for multimodal motion prediction based purely on Convolutional Neural Networks. While being easy-to-implement, the proposed approach achieves competitive performance compared to the state-of-the-art methods and ranks 3rd on the 2021 Waymo Open Dataset Motion Prediction Challenge. Our source code is publicly available at GitHub

CVPR Workshop on Autonomous Driving 2021. Waymo Motion Prediction Challenge 2021

Cited by in corpus (2)

• MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction
• Motion Perceiver: Real-Time Occupancy Forecasting for Embedded Systems