A method of determining from candidate speed-limits for the current location a current location speed-limit in a road-vehicle speed-limit information system, the method comprising: receiving, by the road vehicle speed limit information system, one or more signals (3, 4) corresponding to the respective candidate speed-limits for the current location; applying a tuned parametrized heuristic algorithm with an associated cost function to decide which candidate speed-limit (3, 4) is applicable for the current location; the method further comprising: tuning the parametrized heuristic algorithm through: receiving, by the road vehicle speed limit information system, a cloud service supplied estimated true speed-limit for the current location and an associated confidence in this estimate; using an online learning or a reinforcement learning method, like stochastic gradient descent, incremental stochastic gradient descent, or recursive least squares, to, based on the cloud service supplied estimated true speed-limit for the current location and the associated confidence in that estimate, constantly fit the heuristic's parametrization to tune the parametrized heuristic algorithm to, with a high confidence, reproduce the cloud service supplied estimated true speed-limit for the current location based on the respective candidate speed-limits for the current location; and outputting a speed-limit information signal corresponding to the decision of the tuned parametrized heuristic algorithm of the candidate speed-limit (3, 4) decided applicable for the current location. receiving, by the road vehicle speed limit information system, one or more signals (3, 4) corresponding to the respective candidate speed-limits for the current location; applying a tuned parametrized heuristic algorithm with an associated cost function to decide which candidate speed-limit (3, 4) is applicable for the current location; the method further comprising: tuning the parametrized heuristic algorithm through: receiving, by the road vehicle speed limit information system, a cloud service supplied estimated true speed-limit for the current location and an associated confidence in this estimate; using an online learning or a reinforcement learning method, like stochastic gradient descent, incremental stochastic gradient descent, or recursive least squares, to, based on the cloud service supplied estimated true speed-limit for the current location and the associated confidence in that estimate, constantly fit the heuristic's parametrization to tune the parametrized heuristic algorithm to, with a high confidence, reproduce the cloud service supplied estimated true speed-limit for the current location based on the respective candidate speed-limits for the current location; and outputting a speed-limit information signal corresponding to the decision of the tuned parametrized heuristic algorithm of the candidate speed-limit (3, 4) decided applicable for the current location. tuning the parametrized heuristic algorithm through: receiving, by the road vehicle speed limit information system, a cloud service supplied estimated true speed-limit for the current location and an associated confidence in this estimate; using an online learning or a reinforcement learning method, like stochastic gradient descent, incremental stochastic gradient descent, or recursive least squares, to, based on the cloud service supplied estimated true speed-limit for the current location and the associated confidence in that estimate, constantly fit the heuristic's parametrization to tune the parametrized heuristic algorithm to, with a high confidence, reproduce the cloud service supplied estimated true speed-limit for the current location based on the respective candidate speed-limits for the current location; receiving, by the road vehicle speed limit information system, a cloud service supplied estimated true speed-limit for the current location and an associated confidence in this estimate; using an online learning or a reinforcement learning method, like stochastic gradient descent, incremental stochastic gradient descent, or recursive least squares, to, based on the cloud service supplied estimated true speed-limit for the current location and the associated confidence in that estimate, constantly fit the heuristic's parametrization to tune the parametrized heuristic algorithm to, with a high confidence, reproduce the cloud service supplied estimated true speed-limit for the current location based on the respective candidate speed-limits for the current location; and outputting a speed-limit information signal corresponding to the decision of the tuned parametrized heuristic algorithm of the candidate speed-limit (3, 4) decided applicable for the current location.