CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving
arXiv:2606.06219v1 Announce Type: cross Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints. While diffusion models successfully capture diverse driving behaviors, their iterative denoising process incurs unacceptable latency for safety-critical deployment. To address this, we propose CLEAR (Cognition and Latent Evaluation for Adaptive Routing), a framework that combines ultra-fast generative planning with deep semantic reasoning. CLEAR employs Drive-JEPA as the visual encoder and replaces the multi-step
The increased maturity of diffusion models for diverse behavior generation, coupled with the critical need for real-time inference in autonomous driving, pushes research towards balancing these competing requirements.
This development indicates progress towards overcoming a key technical hurdle for safe and widespread deployment of end-to-end autonomous driving systems, potentially accelerating their commercialization.
The proposed CLEAR framework suggests a method to achieve both complex maneuver generation and real-time inference, which could enable more robust and adaptive autonomous vehicles than previously possible.
- · Autonomous vehicle manufacturers
- · AI chip developers
- · Logistics and transportation sectors
- · Generative AI researchers
- · Companies reliant on less sophisticated autonomous driving approaches
Improved performance and safety metrics for autonomous driving systems in complex scenarios.
Accelerated adoption of autonomous vehicles for both personal use and commercial fleets due to enhanced reliability and reduced latency.
Significant reshaping of urban infrastructure and transportation models as autonomous fleets become a dominant mode of movement.
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Read at arXiv cs.AI