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

• URL: http://arxiv.org/abs/2011.00393v2
• Date: Sun, 18 Apr 2021 15:56:28 GMT
• Title: Beelines: Motion Prediction Metrics for Self-Driving Safety and Comfort
• Authors: Skanda Shridhar, Yuhang Ma, Tara Stentz, Zhengdi Shen, Galen Clark Haynes, Neil Traft
• Abstract summary: We propose two complementary metrics that quantify the effects of motion prediction on safety. Our safety metric has a significantly better signal-to-noise ratio than displacement error in identifying unsafe events.
• Score: 4.670814682436471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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