Multiagent Sensor Fusion for Connected & Autonomous Vehicles to Enhance Navigation Safety

Abstract

Today, autonomous vehicle (AV) navigation systems rely solely on local sensor data feed for safe & reliable navigation. However, it is not uncommon for sensor data to contain erroneous measurements resulting in false predictions, classified as either false positives (predict non-existent obstacle) or false negatives (e.g., missed obstacle). In this paper, we propose a methodology to identify and minimize false negatives in autonomous vehicle navigation, since these are arguably the most dangerous. According to the methodology, each autonomous agent simultaneously localizes and maps its local environment. This map, in turn, is encoded into a low-resolution message and shared with nearby agents via DSRC, a wireless vehicle communication protocol. Next, the agents distributively fuse this information together to construct a world interpretation. Each agent then statistically analyzes its own interpretation with respect to the world interpretation for the common regions of interest. The proposed statistical algorithm outputs a measure of similarity between local and world interpretations and identifies false negatives (if any) for the local agent. This measure, in turn, can be used to inform the agents to update their kinematic behavior in order to account for any errors in local interpretation. The efficacy of this methodology in resolving false negatives is shown in simulation.