# Beelines: Motion Prediction Metrics for Self-Driving Safety and Comfort

arXiv: https://arxiv.org/abs/2011.00393 · HTML: https://mlsanity.com/paper/2011.00393/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Control-Aware Prediction Objectives for Autonomous Driving](https://mlsanity.com/paper/2204.13319/) (2022)
- [Measuring What Matters: Scenario-Driven Evaluation for Trajectory Predictors in Autonomous Driving](https://mlsanity.com/paper/2512.12211/) (2025)

## Abstract (arXiv)

The commonly used metrics for motion prediction do not correlate well with a self-driving vehicle's system-level performance. The most common metrics are average displacement error (ADE) and final displacement error (FDE), which omit many features, making them poor self-driving performance indicators. Since high-fidelity simulations and track testing can be resource-intensive, the use of prediction metrics better correlated with full-system behavior allows for swifter iteration cycles. In this paper, we offer a conceptual framework for prediction evaluation highly specific to self-driving. We propose two complementary metrics that quantify the effects of motion prediction on safety (related to recall) and comfort (related to precision). Using a simulator, we demonstrate that our safety metric has a significantly better signal-to-noise ratio than displacement error in identifying unsafe events.