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• W3185687458 abstract "Forecasting the behavior of other agents is an integral part of the modern robotic autonomy stack, especially in safety-critical scenarios with human-robot interaction, such as autonomous driving. In turn, there has been a significant amount of interest and research in trajectory forecasting, resulting in a wide variety of approaches. Common to all works, however, is the use of the same few accuracy-based evaluation metrics, e.g., displacement error and log-likelihood. While these metrics are informative, they are task-agnostic and predictions that are evaluated as equal can lead to vastly different outcomes, e.g., in downstream planning and decision making. In this work, we take a step back and critically evaluate current trajectory forecasting metrics, proposing task-aware metrics as a better measure of performance in systems where prediction is being deployed. We additionally present one example of such a metric, incorporating planning-awareness within existing trajectory forecasting metrics." @default.
• W3185687458 created "2021-08-02" @default.
• W3185687458 creator A5050003000 @default.
• W3185687458 creator A5091869385 @default.
• W3185687458 date "2021-07-21" @default.
• W3185687458 modified "2023-09-27" @default.
• W3185687458 title "Rethinking Trajectory Forecasting Evaluation." @default.
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• W3185687458 hasPublicationYear "2021" @default.
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