arXiv:2606.18824v1 Announce Type: cross Abstract: Pedestrian trajectory prediction from an ego-centric camera is challenging since it depends on complex interactions with vehicles and scene context, as well as the intention of the pedestrian. By modelling correlation and intent from the historical and future trajectories of the pedestrian, it will usually result in a multimodal (i.e. multiple modes) distribution. Existing stochastic predictors often sample multiple futures from a single unimodal distribution, which can yield sub-optimal 'mixed-mode' trajectories that lie between distinct motio
The proliferation of ego-centric cameras and advancements in AI inference capabilities are driving the need for sophisticated pedestrian prediction models.
Improved pedestrian behavior prediction is critical for the safe and effective deployment of autonomous systems, impacting transportation, robotics, and smart cities.
Existing predictive models are shown to be sub-optimal, highlighting a need for a new generation of multimodal AI to accurately forecast human actions in complex environments.
- · Autonomous vehicle developers
- · Robotics companies
- · Smart city infrastructure
- · AI model developers
- · Developers reliant on unimodal prediction models
- · Legacy ADAS systems
- · Insurance companies (potentially reduced accidents, shifting risk models)
More robust and safer autonomous vehicle navigation in urban environments.
Faster public acceptance and regulatory approval for autonomous systems due to enhanced safety.
The development of more human-like, intuitive AI agents capable of anticipating complex human interactions across various applications.
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Read at arXiv cs.LG