Scenario-based Evaluation of Prediction Models for Automated Vehicles
This addresses the safety-critical issue of improper model evaluation for automated vehicles, which is incremental as it builds on existing safety assessment practices.
The paper tackles the problem of evaluating prediction models for automated vehicles by showing that standardized methods lead to incorrect conclusions about model suitability, and it demonstrates through evaluation on the Waymo Open Motion dataset that assessment must be scenario-based, with results varying by trajectory type and prediction horizon.
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.