Adversarial Training Effects on Calibration Error in Multimodal Trajectory Prediction

This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does adversarial training affect the calibration error of multimodal trajectory prediction models on the Waymo Open Dataset compared to standard maximum likelihood estimation. We introduce Argoverse 2 (AV2) - a collection of three datasets fornperception and forecasting research in the self-driving domain. The annotatednSensor Dataset contains 1,000 sequences of multimodal data, encompassingnhigh-resolution imagery from seven ring cameras, and two. 13 claims were extracted from source literature; 11 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does adversarial training affect the calibration error of multimodal trajectory prediction models on the Waymo Open Dataset compared to standard maximum likelihood estimation?

Autonomous literature synthesis. Automated review score: 8.2/10. Full text and citation available at Assignee Research.