Rules or specifications for autonomous vehicles are currently formulated on a case-by-case basis, and put together in a rather ad-hoc fashion. As a step towards eliminating this practice, we propose a systematic method for ordering an autonomous vehicle's set of specifications so its decision-making process is both transparent and safe. Moreover, we introduce these profiles in the context of assume-guarantee profiles (behavioral contracts) that agents are expected to behave according to.
Publication shortly forthcoming.
Conventional simultaneous localization and mapping (SLAM) algorithms rely on geometric measurements and require loop-closure detections to correct for drift accumulated over a vehicle trajectory. Semantic measurements can add measurement redundancy and provide an alternative form of loop closure. We propose two different estimation algorithms that incorporate semantic measurements provided by vision-based object classifiers. An a priori map of regions where the objects can be detected is assumed.