A random finite set approach for dynamic occupancy grid maps with real-time application

Reliable vehicle environment perception is a basic prerequisite for advanced driver assistant systems and autonomously driving cars. A common environment representation form is an occupancy grid map. It divides the environment into single grid cells and estimates for each cell whether the space it represents is occupied or free, assuming grid cells are independent of each other. The mathematical framework is based on the binary Bayes filter (BBF), which combines sensor measurements from different sensors and potentially from different points in time. Since an occupancy grid map does not employ a concept of individual objects, it is able to represent arbitrarily shaped obstacles. A classical occupancy grid map is not eligible for estimating dynamic environments, because it does not apply a process model. A much-noticed extension to a static occupancy grid map is the Bayesian occupancy filter (BOF). In contrast to a classical occupancy grid, the BOF estimates a velocity distribution for the occupancy of each grid cell based on a histogram filter. Since the BOF is computationally extremely demanding, recent publications suggest to represent the dynamic state of grid cells with particles. This allows to calculate dynamic grid maps in real-time applications with increased grid cell size and resolution. Today, dynamic occupancy grid maps are still a younger research area and not as well-studied as object-tracking approaches are. Up to now, the BOF has been addressed as a research field with little connection to other tracking methods. This work presents a new concept of dynamic grid mapping as an approximation of a random finite set (RFS) filter. A random finite set is a general, probabilistic representation of a random but limited number of objects and their states. The finite set statistics (FISST) describe Bayesian filtering of random finite sets and are basis for a number of multi-object tracking approaches like the probability hypothesis density (PHD) filter. Describing the grid as a random finite set allows transferring advanced concepts from the well-established field of random finite set filtering to the field of dynamic grid mapping. The thesis derives a filter called probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter, which represents and propagates the dynamic grid map in alternating forms as a PHD and as multiple instances of Bernoulli filters. Additionally, the thesis presents a sequential Monte Carlo (SMC) realization of the PHD/MIB filter and an approximation in the Dempster-Shafer domain called Dempster-Shafer PHD/MIB (DS-PHD/MIB) filter, which requires a smaller number of particles than the original PHD/MIB filter. The thesis describes in detail an efficient, massively parallel implementation of the DS-PHD/MIB filter and outlines the algorithm in pseudo code. Finally, the thesis describes characteristics of the DS-PHD/MIB filter and discusses its advantages and disadvantages compared to object-based tracking approaches using practical application examples. A quantitative evaluation with real-world data shows that the DS-PHD/MIB filter provides consistent state estimation results and that it appropriately models the stochastic multi-object transition process and the stochastic multi-object observation process. Furthermore, the evaluation confirms the real-time capability of the parallelized implementation of the DS-PHD/MIB filter and its usefulness for state estimation of a dynamic vehicle environment.