Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Adaptive Informative Path Planning with Multimodal Sensing

Adaptive Informative Path Planning (AIPP) problems model an agent tasked with obtaining information subject to resource constraints in unknown, partially observable environments. Existing work on AIPP has focused on representing observations about the world as a result of agent movement. We formulate the more general setting where the agent may choose between different sensors at the cost of some energy, in addition to traversing the environment to gather information. We call this problem AIPPMS (MS for Multimodal Sensing). AIPPMS requires reasoning jointly about the effects of sensing and movement in terms of both energy expended and information gained. We frame AIPPMS as a Partially Observable Markov Decision Process (POMDP) and solve it with online planning. Our approach is based on the Partially Observable Monte Carlo Planning framework with modifications to ensure constraint feasibility and a heuristic rollout policy tailored for AIPPMS. We evaluate our method on two domains: a simulated search-and-rescue scenario and a challenging extension to the classic RockSample problem. We find that our approach outperforms a classic AIPP algorithm that is modified for AIPPMS, as well as online planning using a random rollout policy.Comment: First two authors contributed equally; International Conference on Automated Planning and Scheduling (ICAPS) 202

Engineering Algorithms for Route Planning in Multimodal Transportation Networks

Practical algorithms for route planning in transportation networks are a showpiece of successful Algorithm Engineering. This has produced many speedup techniques, varying in preprocessing time, space, query performance, simplicity, and ease of implementation. This thesis explores solutions to more realistic scenarios, taking into account, e.g., traffic, user preferences, public transit schedules, and the options offered by the many modalities of modern transportation networks

City Transport Analyzer: A Powerful Qgis Plugin For Public Transport Accessibility And Intermodality Analysis

This study presents the City Transport Analyzer (CTA) QGIS plug-in designed for analyzing urban public transportation (PT) networks, exploiting GTFS data to investigate their multimodal nature. GTFS, a standardized format for PT schedules and geographic information, enables accurate data sharing, crucial for service analysis. The plug-in extracts GTFS data and converts it into GIS layers for analysis. It constructs a graph data structure using NetworkX, representing PT routes, with edges indicating travel time and transport type. Two key analyses are conducted: Service Area for accessibility and Nearby Stops Path for intermodality. The accessibility analysis determines reachable areas within specified time frames via PT and walking from one or more source points. It provides comprehensive insights into service coverage and the potential reach of PT networks within urban areas. The intermodality analysis assesses street paths for modal changes beyond PT nodes, offering valuable information on connectivity and potential PT usage patterns. Tested on case studies in Milan and Rio de Janeiro, the plug-in demonstrates utility in understanding metropolitan PT networks, but also room for improving the overall accuracy, usability and performances of the tool. The CTA plugin offers a flexible and user-friendly tool for studying service coverage, accessibility, and connectivity, aiding urban infrastructure analysis and planning. Future work aims to extend analyses to the whole city, including other city contexts. Ultimately, the CTA plugin contributes to a deeper understanding of urban PT networks and areas, facilitating informed decision-making in urban planning and infrastructure management

Heterogeneous network flow and Petri nets characterize multilayer complex networks

Interacting subsystems are commonly described by networks, where multimodal behaviour found in most natural or engineered systems found recent extension in form of multilayer networks. Since multimodal interaction is often not dictated by network topology alone and may manifest in form of cross-layer information exchange, multilayer network flow becomes of relevant further interest. Rationale can be found in most interacting subsystems, where a form of multimodal flow across layers can be observed in e.g., chemical processes, energy networks, logistics, finance, or any other form of conversion process relying on the laws of conservation. To this end, the formal notion of heterogeneous network flow is proposed, as a multilayer flow function aligned with the theory of network flow. Furthermore, dynamic equivalence is established with the framework of Petri nets, as the baseline model of concurrent event systems. Application of the resulting multilayer Laplacian flow and flow centrality is presented, along with graph learning based inference of multilayer relationships over multimodal data. On synthetic data the proposed framework demonstrates benefits of multimodal flow derivation in critical component identification. It also displays applicability in relationship inference (learning based function approximation) on multimodal time series. On real-world data the proposed framework provides, among others, multimodal flow interpretation of U.S. economic activity, uncovering underlying empirical steady state probability distribution, as well as inherent network (economic) robustness

Human-Robot Gym: Benchmarking Reinforcement Learning in Human-Robot Collaboration

Deep reinforcement learning (RL) has shown promising results in robot motion planning with first attempts in human-robot collaboration (HRC). However, a fair comparison of RL approaches in HRC under the constraint of guaranteed safety is yet to be made. We, therefore, present human-robot gym, a benchmark for safe RL in HRC. Our benchmark provides eight challenging, realistic HRC tasks in a modular simulation framework. Most importantly, human-robot gym includes a safety shield that provably guarantees human safety. We are, thereby, the first to provide a benchmark to train RL agents that adhere to the safety specifications of real-world HRC. This bridges a critical gap between theoretic RL research and its real-world deployment. Our evaluation of six environments led to three key results: (a) the diverse nature of the tasks offered by human-robot gym creates a challenging benchmark for state-of-the-art RL methods, (b) incorporating expert knowledge in the RL training in the form of an action-based reward can outperform the expert, and (c) our agents negligibly overfit to training data

Flight Safety Assessment and Management.

This dissertation develops a Flight Safety Assessment and Management (FSAM) system to mitigate aircraft loss of control risk. FSAM enables switching between the pilot/nominal autopilot system and a complex flight control system that can potentially recover from high risk situations but can be hard to certify. FSAM monitors flight conditions for high risk situations and selects the appropriate control authority to prevent or recover from loss of control. The pilot/nominal autopilot system is overridden only when necessary to avoid loss of control. FSAM development is pursued using two approaches. First, finite state machines are manually prescribed to manage control mode switching. Constructing finite state machines for FSAM requires careful consideration of possible exception events, but provides a computationally-tractable and verifiable means of realizing FSAM. The second approach poses FSAM as an uncertain reasoning based decision theoretic problem using Markov Decision Processes (MDP), offering a less tedious knowledge engineering process at the cost of computational overhead. Traditional and constrained MDP formulations are presented. Sparse sampling approaches are also explored to obtain suboptimal solutions to FSAM MDPs. MDPs for takeoff and icing-related loss of control events are developed and evaluated. Finally, this dissertation applies verification techniques to ensure that finite state machine or MDP policies satisfy system requirements. Counterexamples obtained from verification techniques aid in FSAM refinement. Real world aviation accidents are used as case studies to evaluate FSAM formulations. This thesis contributes decision making and verification frameworks to realize flight safety assessment and management capabilities. Novel flight envelopes and state abstractions are prescribed to aid decision making.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133348/1/swee_1.pd