FALL réduit de 74,8% la latence de perception de plusieurs véhicules sans perte de précision

Up to 74.8% latency reduction in multi-vehicle collaborative perception without any precision loss — that’s what the FALL scheme delivers in simulation.

Multi-vehicle collaborative perception (MvCP) is supposed to be a key enabler for reliable automated driving, but real-time operation under limited compute and bandwidth has always been the bottleneck. Intermediate-fusion (IF) approaches — where agents exchange feature maps rather than raw sensor data — hit a wall: you either fuse early and burn bandwidth or fuse late and burn compute.

Conditional Additivity Lets You Slide the Fusion Point

The paper formalizes a property called conditional additivity and shows it holds for various DNN linear layers. That property means you can shift the position of feature fusion forward or backward through the network without changing the final output — provided you stay within linear layers. The authors prove that the precision of the forward and backward fusion position adjustments is consistent for additive feature fusion, which is the common IF pattern.

FALL: Shift Fusion into the Cheapest Spot

FALL (Fusion Position Adjustment among Linear Layers) exploits that property to move the fusion point to whatever layer minimizes the bottleneck — communication or computation — while guaranteeing exact mathematical equivalence. No retraining, no approximation, no trade-off.

Simulation Results That Matter for Deployment

In simulation, FALL cuts MvCP latency by up to 74.8% when communication is the constraint (e.g., limited bandwidth) and by up to 30.3% when computation is the constraint (e.g., limited onboard compute). Those are not asymptotic promises; they are measured reductions from their proposed scheme. The scheme is theoretically lossless — every fused output matches the original network’s output down to the bit.

What this means in practice: autonomous vehicle fleets can run cooperative perception with tighter latency budgets, opening room for higher sensor resolution or more complex downstream planning. FALL won’t fix every bottleneck, but it buys back latency without asking for more hardware or sacrificing accuracy.

Source: Lightweight Multi-Vehicle Collaborative Perception Acceleration with Fusion Position Adjustment
Domain: arxiv.org