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.
@article{arxiv.2011.00393,
title = {Beelines: Motion Prediction Metrics for Self-Driving Safety and Comfort},
author = {Skanda Shridhar and Yuhang Ma and Tara Stentz and Zhengdi Shen and Galen Clark Haynes and Neil Traft},
journal= {arXiv preprint arXiv:2011.00393},
year = {2021}
}
Comments
Accepted at 2021 IEEE International Conference on Robotics and Automation (ICRA 2021)