Environment Modeling Based on Generic Infrastructure Sensor Interfaces Using a Centralized Labeled-Multi-Bernoulli Filter

Urban intersections put high demands on fully automated vehicles, in particular, if occlusion occurs. In order to resolve such and support vehicles in unclear situations, a popular approach is the utilization of additional information from infrastructure-based sensing systems. However, a widespread use of such systems is circumvented by their complexity and thus, high costs. Within this paper, a generic interface is proposed, which enables a huge variety of sensors to be connected. The sensors are only required to measure very few features of the objects, if multiple distributed sensors with different viewing directions are available. Furthermore, a Labeled Multi-Bernoulli (LMB) filter is presented, which can not only handle such measurements, but also infers missing object information about the objects' extents. The approach is evaluated on simulations and demonstrated on a real-world infrastructure setup

Sensor tasking utilizing deep reinforcement learning in a random finite set framework

There is a growing need to increase the capabilities of existing sensor arrays to monitor a large amount of space objects orbiting the Earth with a limited number of opportunities to observe these objects. Due to geopolitical considerations and financial cost, it is infeasible to create an array of sensors that can monitor each space object and accurately describe its state. Instead of brute force techniques by increasing the number of sensors worldwide, the current advancements in computational capability along with new algorithms for multi-target filtering and reinforcement learning has allowed a pathway to begin solving the non-myopic, heterogenous sensor tasking problem. This work employs the labeled multi-Bernoulli filter in conjunction with advanced, deep reinforcement learning techniques such as the policy gradient Q-learning algorithm and deep Q-networks. The filter and reinforcement learning techniqures are used together to track ten targets in geosynchronous orbit, while a linear Kalman filter and the reinforcement learning techniques are used to evaluate their effectiveness in multi-agent learning scenarios. The future deployment of these algorithms and their specific logistical considerations are also discussed with potential solutions.Aerospace Engineerin