Interaction-aware Maneuver Prediction for Autonomous Vehicles using Interaction Graphs

Gomes, Iago Pachêco; Premebida, Cristiano; Wolf, Denis F.

doi:10.1109/iv55152.2023.10186811

Cited by 2 publications

(1 citation statement)

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“…The Negative Log Likelihood (NLL) outputs the standard deviation to quantify the uncertainty of a trajectory [21][22][23]. In the case of maneuver predictions, the specification of probabilities [24] can be used. However, both approaches can not differentiate between the case of several possible options for the future motion or if the model can not understand the current scenario, which both lead to high uncertainty.…”

Section: Related Workmentioning

confidence: 99%

Self-Evaluation of Trajectory Predictors for Autonomous Driving

Karle,

Furtner,

Lienkamp

2024

Electronics

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Driving experience and anticipatory driving are essential skills for humans to operate vehicles in complex environments. In the context of autonomous vehicles, the software must offer the related features of scenario understanding and motion prediction. The latter feature of motion prediction is extensively researched with several competing large datasets, and established methods provide promising results. However, the incorporation of scenario understanding has been sparsely investigated. It comprises two aspects. First, by means of scenario understanding, individual assumptions of an object’s behavior can be derived to adaptively predict its future motion. Second, scenario understanding enables the detection of challenging scenarios for autonomous vehicle software to prevent safety-critical situations. Therefore, we propose a method incorporating scenario understanding into the motion prediction task to improve adaptivity and avoid prediction failures. This is realized by an a priori evaluation of the scenario based on semantic information. The evaluation adaptively selects the most accurate prediction model but also recognizes if no model is capable of accurately predicting this scenario and high prediction errors are expected. The results on the comprehensive scenario library CommonRoad reveal a decrease in the Euclidean prediction error by 81.0% and a 90.8% reduction in mispredictions of our method compared to the benchmark model.

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