Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mamba
arXiv:2607.05669v1 Announce Type: cross Abstract: Reliable localization in GNSS-denied environments remains a fundamental challenge for intelligent vehicles, as inertial navigation systems accumulate unbounded drift without external correction. Existing approaches provide drift correction through dedicated infrastructure, expensive external sensors, or complex multi-sensor fusion, each introducing practical deployment barriers. We propose Evidential Velocity Correction using Mamba (EVC-Mamba), a learning-based architecture that transforms onboard vehicle sensor data into a virtual velocity sen
The continuous drive for reliable autonomous systems in challenging environments, coupled with advancements in AI models like Mamba, makes this development timely.
This development offers a potential breakthrough for autonomous vehicle localization in GNSS-denied areas, reducing reliance on expensive external sensors or dedicated infrastructure and enhancing operational capabilities.
Localization for intelligent vehicles could become more robust and less costly, expanding the viable deployment scenarios for autonomous systems beyond well-mapped and signal-rich environments.
- · Autonomous vehicle developers
- · Logistics and transportation companies
- · Defence sector
- · AI hardware manufacturers
- · Manufacturers of highly specialized, expensive localization sensors
- · GNSS-dependent service providers
- · Companies without strong AI integration
Increased reliability and broader adoption of autonomous vehicles in diverse environments.
Reduced operational costs for autonomous fleets, leading to more competitive prices for services and goods.
Potential for new autonomous applications in remote, disaster-stricken, or contested regions, boosting resilience and strategic capabilities.
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Read at arXiv cs.LG