Adversarially Trained Trajectory Prediction Models: Latency and Accuracy Trade-offs in Autonomous Driving

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How do adversarially trained trajectory prediction models compare in inference latency and accuracy trade-offs when evaluated on standard autonomous driving planning benchmarks. We introduce a motion forecasting (behavior prediction) method that meets the latency requirements for autonomous driving in dense urban environments without sacrificing accuracy. A whole-scene sparse input representation allows StopNet to scale to predicting trajectories for. 6 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 5.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How do adversarially trained trajectory prediction models compare in inference latency and accuracy trade-offs when evaluated on standard autonomous driving planning benchmarks?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance