arXiv:2606.24096v1 Announce Type: cross Abstract: Robust perception underpins autonomous driving, and most recent progress comes from scaling the model-larger backbones, foundation models, and cooperative multi-agent fusion. We pursue a complementary, upstream question: what should the camera itself measure? Using a differentiable RAW-to-task pipeline, we decompose which sensor degrees of freedom benefit dense prediction. Learning the spectral colour-filter-array (CFA) weights is the dominant lever, improving mIoU by +0.017 (KITTI-360) and +0.023 (ACDC) over a fixed camera. In contrast, point-
The continuous push for more robust autonomous driving systems combined with advanced differentiable rendering techniques allows for co-design optimization that was previously infeasible.
This research provides a complementary pathway to improving autonomous vehicle perception beyond traditional model scaling, enabling more robust and efficient systems closer to deployment.
Autonomous vehicle perception can now be optimized from the sensor hardware up, rather than solely relying on post-capture software improvements.
- · Autonomous Vehicle Developers
- · Sensor Manufacturers
- · AI Hardware Manufacturers
- · Traditional Camera Manufacturers (slow to adapt)
- · Pure Software-based Perception Solutions
Improved safety and reliability of autonomous driving systems due to optimized sensor inputs.
Reduced computational load for perception tasks on autonomous vehicles, leading to lower power consumption or higher performance.
The methodology could extend to other vision-reliant AI applications, creating more efficient and task-optimal edge AI devices.
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Read at arXiv cs.AI