Beelines: Motion Prediction Metrics for Self-Driving Safety and Comfort
This addresses the need for better evaluation metrics in self-driving to improve safety and comfort, though it is incremental as it builds on existing prediction frameworks.
The paper tackles the problem that standard motion prediction metrics (ADE/FDE) poorly correlate with self-driving system performance, proposing new safety and comfort metrics specific to self-driving. Using a simulator, they show their safety metric has a significantly better signal-to-noise ratio than displacement error for identifying unsafe events.
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.