Scenario-based Evaluation of Prediction Models for Automated Vehicles

TL;DR

This paper advocates for scenario-based evaluation of prediction models in automated vehicles, demonstrating that current methods can lead to misleading conclusions and potentially unsafe decisions.

Contribution

It introduces a scenario-based assessment framework for prediction models in AVs and categorizes trajectories to evaluate models' performance across different situations.

Findings

Standard evaluation methods can be misleading.

Model performance varies significantly across trajectory types.

Scenario-based evaluation provides clearer insights into model suitability.

Abstract

To operate safely, an automated vehicle (AV) must anticipate how the environment around it will evolve. For that purpose, it is important to know which prediction models are most appropriate for every situation. Currently, assessment of prediction models is often performed over a set of trajectories without distinction of the type of movement they capture, resulting in the inability to determine the suitability of each model for different situations. In this work we illustrate how standardized evaluation methods result in wrong conclusions regarding a model's predictive capabilities, preventing a clear assessment of prediction models and potentially leading to dangerous on-road situations. We argue that following evaluation practices in safety assessment for AVs, assessment of prediction models should be performed in a scenario-based fashion. To encourage scenario-based assessment of prediction models and illustrate the dangers of improper assessment, we categorize trajectories of the Waymo Open Motion dataset according to the type of movement they capture. Next, three different models are thoroughly evaluated for different trajectory types and prediction horizons. Results show that common evaluation methods are insufficient and the assessment should be performed depending on the application in which the model will operate.