arXiv:2603.09420v3 Announce Type: replace-cross Abstract: Motion forecasting enables autonomous vehicles to anticipate scene evolution by predicting the future trajectories of dynamic agents. However, existing approaches typically assume a closed-world setting with a fixed object taxonomy and access to high-quality perception, limiting their applicability in the real world where perception is imperfect, and new object classes may emerge over time. In this work, we introduce class-incremental motion forecasting, a novel setting in which new object classes are sequentially introduced over time a
The increasing complexity and dynamism of real-world autonomous systems necessitates more robust and adaptive AI models that can handle unexpected inputs and new object types. This paper addresses a critical limitation in current motion forecasting, pushing AI towards more generalized intelligence.
This work is crucial for strategic readers interested in autonomous systems as it addresses a fundamental weakness in current AI deployment: the inability to adapt to novel situations and imperfect data, which is rife in real-world scenarios.
Current motion forecasting models, typically designed for fixed object sets and perfect perception, will evolve to incorporate 'class-incremental' learning, allowing them to adapt to new object classes and imperfect real-world data without retraining from scratch.
- · Autonomous vehicle developers
- · Robotics companies
- · AI research institutions
- · Logistics and delivery services
- · Companies relying on static AI models
- · Developers with rigid perception systems
Autonomous vehicles will become more resilient and versatile in unpredictable environments, leading to safer and more reliable operation.
Reduced deployment costs and faster iteration cycles for AI models in robotics and automotive industries, as they won't require full retraining for every new object or scenario.
Acceleration of general-purpose AI development, as class-incremental learning is a step towards AI that can continuously learn and adapt in diverse, open-world settings, influencing future AI agents.
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Read at arXiv cs.AI