Autonomous rail systems, including driverless trams, are gaining traction due to their potential to enhance efficiency, capacity, and operational cost-effectiveness. A central requirement for safe operation of autonomous tram vehicles is achieving ultra-reliable positioning accuracy, which traditionally relies on costly, infrastructure-heavy solutions like trackside beacons or GPS, alongside sensor fusion algorithms that often fail to meet the stringent requirements of Safety Integrity Level 4 (SIL4). These limitations create a significant barrier to the widespread adoption of autonomous light rail systems. This paper introduces the Consistency Check and Best performance Selection (CCBS) algorithm, a novel fully onboard solution that enhances data reliability for rail positioning and velocity estimation systems. Our method validates and combines outputs from multiple sensors, leveraging a sophisticated consistency check and a data performance selection mechanism to achieve a SIL4 level – even when individual data streams do not. This entirely onboard approach significantly improves positioning and velocity estimation accuracy and offers substantial cost and maintenance efficiencies by eliminating the need for trackside infrastructure.