AI trajectory predictors fail when given too much data from surrounding vehicles

Researchers found that state-of-the-art autonomous vehicle prediction models actually perform worse when including information from nearby cars and pedestrians, revealing unstable decision-making patterns. A new Conditional Information Bottleneck method filters out unhelpful data, improving prediction accuracy and robustness across datasets—critical for safe autonomous driving systems.