Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference Speed
This work addresses the need for efficient motion prediction in autonomous vehicles, particularly for deployment on embedded hardware, though it is incremental in improving efficiency over existing methods.
The paper tackles the problem of high computational demands in motion prediction models for autonomous driving by proposing a lightweight model that achieves competitive benchmark results with only a few hours of training on a single GPU and low inference latency.
For efficient and safe autonomous driving, it is essential that autonomous vehicles can predict the motion of other traffic agents. While highly accurate, current motion prediction models often impose significant challenges in terms of training resource requirements and deployment on embedded hardware. We propose a new efficient motion prediction model, which achieves highly competitive benchmark results while training only a few hours on a single GPU. Due to our lightweight architectural choices and the focus on reducing the required training resources, our model can easily be applied to custom datasets. Furthermore, its low inference latency makes it particularly suitable for deployment in autonomous applications with limited computing resources.