CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining
arXiv:2607.04541v1 Announce Type: cross Abstract: Camera-radar (CR) fusion is a practical sensing configuration for autonomous driving, but existing models are typically trained with task-specific supervision, limiting reusable representation learning. We present CRISP, a spatiotemporal CR backbone pretrained through forecasting-based representation learning. Given historical multi-view images and radar sweeps, CRISP learns a unified bird's-eye-view (BEV) representation by predicting future LiDAR point clouds. LiDAR is used only as privileged supervision during pretraining; the deployed model
The continuous drive for more robust and generalized autonomous driving systems, coupled with advancements in multi-modal sensor fusion, makes this an opportune moment for research into scalable pretraining methods.
This development proposes a novel pretraining method that could significantly improve the reusability and efficiency of representation learning for camera-radar fusion in autonomous vehicles, potentially accelerating deployment and reliability.
Current task-specific training for camera-radar fusion could be augmented or replaced by a more generalized, forecasting-based pretraining approach, leading to more adaptable and robust autonomous driving models.
- · Autonomous vehicle developers
- · AI hardware manufacturers
- · Sensor manufacturers
- · Logistics and transportation sectors
- · Companies reliant on highly specialized, non-reusable training models
- · Legacy sensor suppliers
Improved perception systems for autonomous driving through generalizable pretraining using camera-radar data.
Faster development and deployment cycles for L4/L5 autonomous vehicles due to more robust and transferable AI models.
Reduced cost and increased safety of autonomous transportation, potentially unlocking new business models and urban designs centered on mobility as a service.
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Read at arXiv cs.AI