Driving with confidence: Local dynamic maps that provide los for the Gulliver Test-bed

The design of automated driving systems aims at reducing the human error and increasing the fuel efficiency by letting the vehicles map their surroundings and drive autonomously. One of the system challenges on the road is that at any time the environment can stop meeting the system's operational conditions (and then resume meeting the requirements at some later point in time). Thus, as vehicles map their surroundings, they should also provide information that can help the vehicles to know whether the operational conditions are met with respect to the confidence that they have about the mapped information. We design and implement key services of Local Dynamic Maps (LDMs) that are based on on-board and remote sensory information. The LDM provides the position of all nearby noticeable objects along with the LDM's confidence about these positions. The design also includes an extension that allows the vehicular system to agree on the lowest common ability to meet the operational conditions. We evaluate the performance of a key component in our pilot implementation together with a set of test cases that validate the proposed design. Our current findings show that the presented ideas can accelerate the deployment of automated driving systems.