Super Agents and Confounders: Influence of surrounding agents on vehicle trajectory prediction

The paper analyzes state‑of‑the‑art vehicle trajectory predictors and finds that many surrounding agents actually worsen prediction accuracy. Using Shapley attribution, it shows models learn unstable, non‑causal decision schemes that vary across training runs. The authors introduce a Conditional Information Bottleneck that compresses agent features and ignores unhelpful ones, improving accuracy and robustness across datasets and architectures.

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• agents
• benchmarks