Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving
arXiv:2605.29138v1 Announce Type: cross Abstract: Latency-accuracy tradeoffs are fundamental in real-time applications of deep neural networks (DNNs) for cyber-physical systems. In autonomous driving, in particular, safety depends on both prediction quality and the end-to-end delay from sensing to actuation. We observe that (1) when latency is accounted for, the latency-optimal network configuration varies with scene context and compute availability; and (2) a single fixed-resolution model becomes suboptimal as conditions change. We present a multi-resolution, end-to-end deep neural network fo
The rapid advancement of deep neural networks and real-time cyber-physical systems, specifically in autonomous driving, necessitates innovative solutions to address performance limitations.
This development is crucial for strategic readers because it directly improves the reliability and safety of autonomous systems, impacting their widespread adoption and economic potential.
The ability to dynamically optimize latency and accuracy in DNNs based on context changes how autonomous systems process information and react to real-world conditions, making them more adaptable.
- · Autonomous Vehicle Manufacturers
- · AI Software Developers
- · Robotics Industry
- · Compute Infrastructure Providers
- · Companies relying on fixed-resolution models
- · Less adaptive AI system providers
Autonomous vehicles become significantly safer and more efficient due to dynamic latency and accuracy optimization.
Increased consumer trust and faster regulatory approval for autonomous driving leads to accelerated market penetration.
This optimization framework could be adapted to other safety-critical AI applications, broadening the impact of flexible AI systems.
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Read at arXiv cs.LG