Motion Prediction Performance Analysis for Autonomous Driving Systems and the Effects of Tracking Noise

TL;DR

This paper investigates how tracking noise impacts motion prediction accuracy in autonomous driving, emphasizing the importance of robust tracking systems and the potential benefits of tracking-free models under noisy conditions.

Contribution

It systematically analyzes the sensitivity of motion prediction to tracking errors and compares tracking-based and tracking-free models across various scenarios.

Findings

Tracking information improves prediction in noise-free settings

Tracking noise can significantly degrade prediction performance

Tracking-free models may be advantageous under noisy conditions

Abstract

Autonomous driving consists of a multitude of interacting modules, where each module must contend with errors from the others. Typically, the motion prediction module depends upon a robust tracking system to capture each agent's past movement. In this work, we systematically explore the importance of the tracking module for the motion prediction task and ultimately conclude that the overall motion prediction performance is highly sensitive to the tracking module's imperfections. We explicitly compare models that use tracking information to models that do not across multiple scenarios and conditions. We find that the tracking information plays an essential role and improves motion prediction performance in noise-free conditions. However, in the presence of tracking noise, it can potentially affect the overall performance if not studied thoroughly. We thus argue practitioners should be mindful of noise when developing and testing motion/tracking modules, or that they should consider tracking free alternatives.