Super Agents and Confounders: Influence of surrounding agents on vehicle trajectory prediction
arXiv:2604.03463v1 Announce Type: new Abstract: In highly interactive driving scenes, trajectory prediction is conditioned on information from surrounding traffic participants such as cars and pedestrians. Our main contribution is a comprehensive analysis of state-of-the-art trajectory predictors, which reveals a surprising and critical flaw: many surrounding agents degrade prediction accuracy rather than improve it. Using Shapley-based attribution, we rigorously demonstrate that models learn unstable and non-causal decision-making schemes that vary significantly across training runs. Building on these insights, we propose to integrate a Conditional Information Bottleneck (CIB), which does not require additional supervision and is trained to effectively compress agent features as well as ignore those that are not beneficial for the prediction task. Comprehensive experiments using multiple datasets and model architectures demonstrate that this simple yet effective approach not only imp
arXiv:2604.03463v1 Announce Type: new Abstract: In highly interactive driving scenes, trajectory prediction is conditioned on information from surrounding traffic participants such as cars and pedestrians. Our main contribution is a comprehensive analysis of state-of-the-art trajectory predictors, which reveals a surprising and critical flaw: many surrounding agents degrade prediction accuracy rather than improve it. Using Shapley-based attribution, we rigorously demonstrate that models learn unstable and non-causal decision-making schemes that vary significantly across training runs. Building on these insights, we propose to integrate a Conditional Information Bottleneck (CIB), which does not require additional supervision and is trained to effectively compress agent features as well as ignore those that are not beneficial for the prediction task. Comprehensive experiments using multiple datasets and model architectures demonstrate that this simple yet effective approach not only improves overall trajectory prediction performance in many cases but also increases robustness to different perturbations. Our results highlight the importance of selectively integrating contextual information, which can often contain spurious or misleading signals, in trajectory prediction. Moreover, we provide interpretable metrics for identifying non-robust behavior and present a promising avenue towards a solution.
Executive Summary
This article challenges the conventional understanding of trajectory prediction in highly interactive driving scenes by revealing a surprising flaw in state-of-the-art models: surrounding agents often degrade prediction accuracy rather than improve it. The authors propose a Conditional Information Bottleneck (CIB) approach, which effectively compresses agent features and ignores non-beneficial information. Experimental results demonstrate improved trajectory prediction performance and robustness to perturbations. The article highlights the importance of selective contextual information integration and provides interpretable metrics for identifying non-robust behavior. The findings suggest a promising avenue towards a solution and emphasize the need for more nuanced approaches to trajectory prediction.
Key Points
- ▸ State-of-the-art trajectory predictors are flawed in highly interactive driving scenes.
- ▸ Surrounding agents can degrade prediction accuracy rather than improve it.
- ▸ CIB approach effectively compresses agent features and ignores non-beneficial information.
Merits
Strength in Methodology
The use of Shapley-based attribution to rigorously demonstrate unstable and non-causal decision-making schemes is a significant methodological strength.
Strength in Novelty
The CIB approach offers a novel solution to the problem of selective contextual information integration in trajectory prediction.
Demerits
Limitation in Generalizability
The article's findings and approach may not be generalizable to all types of driving scenes or agent interactions.
Expert Commentary
The article makes a significant contribution to the field of trajectory prediction in highly interactive driving scenes by highlighting a critical flaw in state-of-the-art models and proposing a novel solution. The use of Shapley-based attribution and the CIB approach demonstrate a depth of understanding of the underlying issues and a commitment to developing more robust and effective solutions. The article's findings and implications have far-reaching consequences for the development of autonomous vehicles and the integration of contextual information in AI applications.
Recommendations
- ✓ Future research should investigate the generalizability of the CIB approach to different types of driving scenes and agent interactions.
- ✓ Developers of autonomous vehicles should prioritize the incorporation of the CIB approach or similar methods into their systems to ensure robustness and safety in highly interactive driving scenes.
Sources
Original: arXiv - cs.LG