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Balancing the competing, and often conflicting, needs of people and wildlife in shared landscapes is a major challenge for conservation science and policy worldwide. Connectivity is critical for wildlife persistence, but dispersing animals may come into conflict with people, leading to severe costs for humans and animals and impeding connectivity. Thus, conflict mitigation and connectivity present an apparent dilemma for conservation. We present a framework to address this dilemma and disentangle the effects of barriers to animal movement and conflict-induced mortality of dispersers on connectivity. We extend random-walk theory to map the connectivity–conflict interface, or areas where frequent animal movement may lead to conflict and conflict in turn impedes connectivity. We illustrate this framework with the endangered Asian elephantElephas maximus, a species that frequently disperses out of protected areas and comes into conflict with humans. We mapped expected movement across a human-dominated landscape over the short- and long-term, accounting for conflict mortality. Natural and conflict-induced mortality together reduced expected movement and connectivity among populations. Based on model validation, our conflict predictions that explicitly captured animal movement better explained observed conflict than a model that considered distribution alone. Our work highlights the interaction between connectivity and conflict and enables identification of location-specific conflict mitigation strategies that minimize losses to people, while ensuring critical wildlife movement between habitats. By predicting where animal movement and humans collide, we provide a basis to plan for broad-scale conservation and the mutual well-being of wildlife and people in shared landscapes.
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Learning and Leveraging Conventions in the Design of Haptic Shared Control Paradigms for Steering a Ground Vehicle
The main objective of this paper is to establish a framework to study the co-adaptation between humans and automation systems in a haptic shared control framework. We specifically used this framework to design control transfer strategies between humans and automation systems to resolve a conflict when co-steering a semi-automated ground vehicle. The proposed framework contains three main parts. First, we defined a modular structure to separate partner-specific strategies from task-dependent representations and use this structure to learn different co-adaption strategies. In this structure, we assume the human and automation steering commands can be determined by optimizing cost functions. For each agent, the costs are defined as a combination of a set of hand-coded features and vectors of weights. The hand-coded features can be selected to describe task-dependent representations. On the other hand, the weight distributions over these features can be used as a proxy to determine the partner-specific conventions. Second, to leverage the learned co-adaptation strategies, we developed a map connecting different strategies to the outputs of human-automation interactions by employing a collaborative-competitive game concept. Finally, using the map, we designed an adaptable automation system capable of co-adapting to human driver’s strategies. Specifically, we designed an episode-based policy search using the deep deterministic policy gradients technique to determine the optimal weights vector distribution of automation’s cost function. The simulation results demonstrate that the handover strategies designed based on co-adaption between human and automation systems can successfully resolve a conflict and improve the performance of the human automation teaming.
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- Award ID(s):
- 2238268
- PAR ID:
- 10514876
- Publisher / Repository:
- springer
- Date Published:
- Journal Name:
- International Journal of Control, Automation and Systems
- Volume:
- 21
- Issue:
- 10
- ISSN:
- 1598-6446
- Page Range / eLocation ID:
- 3324 to 3335
- Subject(s) / Keyword(s):
- Seamless Control Transfer, Haptic Shared Control, Human-Machine Interaction, Assistive Driving Technologies, Co-Adaptions and Conventions
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s12555-022-0509-6
