Uncertainty-Aware Online Merge Planning with Learned Driver Behavior

Safe and reliable autonomy solutions are a critical component of next-generation intelligent transportation systems. Autonomous vehicles in such systems must reason about complex and dynamic driving scenes in real time and anticipate the behavior of nearby drivers. Human driving behavior is highly nuanced and specific to individual traffic participants. For example, drivers might display cooperative or non-cooperative behaviors in the presence of merging vehicles. These behaviors must be estimated and incorporated in the planning process for safe and efficient driving. In this work, we present a framework for estimating the cooperation level of drivers on a freeway and plan merging maneuvers with the drivers' latent behaviors explicitly modeled. The latent parameter estimation problem is solved using a particle filter to approximate the probability distribution over the cooperation level. A partially observable Markov decision process (POMDP) that includes the latent state estimate is solved online to extract a policy for a merging vehicle. We evaluate our method in a high-fidelity automotive simulator against methods that are agnostic to latent states or rely on $\textit{a priori}$ assumptions about actor behavior

Time-varying Pedestrian Flow Models for Service Robots

We present a human-centric spatiotemporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples’ routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling

Nonlinear Methods for Spatiotemporal Mapping in Unstructured Environments

In order for a robot to intelligently interact in real-world environments, it is essential to understand its surrounding. The surrounding can be represented as a metric map. However, most of the current mapping techniques assume the environment is static, and therefore, the control policies cannot effectively account for the changing and unpredictable nature of the real world environments. Therefore, it is essential to model the uncertainties in dynamic environments. In this thesis, three types of mapping techniques to characterize patterns that change in both space and time are proposed. Firstly, long-term occupancy maps to model variations of the occupancy state of the environment observed over a period is proposed. Such maps are developed with the intention of providing prior information for path planning algorithms. Secondly, short-term occupancy maps to model the immediate changes in the occupancy level are developed. The short-term maps are useful for safe real-time navigation in dynamic environments by making predictions into the future. Finally, directional grid maps are introduced to model the angular uncertainty inherent in robotics. In order to captures the highly nonlinear spatiotemporal patterns in the real-world environments, kernel methods that operate in a rich high-dimensional feature space is utilized. In order to quantify the uncertainty in dynamic environments, Bayesian inference and directional statistics are mainly used. The proposed methods are suitable for both big data and data-scarce settings. Experiments were conducted on simulated and real-world robotic systems, mainly using data obtained from range sensors such as LIDAR. Besides having attractive theoretical properties, the proposed learning algorithms are superior in both speed and accuracy. Further, because the proposed techniques in this thesis can effectively model the uncertainty in changing environments, they can be used for exploration and path planning in dynamic environments