WILD ESCAPE: A Unity-Based Rule-Driven Wildlife Survival Simulation for Human-Wildlife Conflict Awareness

Abstract

Human-wildlife conflict is often represented in digital games through enemy-oriented animal behavior, while the ecological reason behind conflict is reduced to background text. WILD ESCAPE proposes a Unity-based wildlife survival simulation in which the animal is represented as a displaced survivor rather than a natural aggressor. The prototype uses a 3D environment, player-controlled animal movement, human threat detection, health feedback, combat/contact responses, level selection and cinematic camera handoff. The implementation is based on rule-driven game AI rather than machine learning: detection radius, target tags, attack range, NavMeshAgent movement, Animator clip control and collision/trigger events define the behavior loop. The ZenG Ultra scene and MMW scripts demonstrate a practical prototype with animal control, human chase behavior, menu flow and checkpoint-based gameplay start. The paper presents the system design, methodology, implementation modules and observed results, and identifies future directions such as smarter NPC behavior, multi-animal selection and possible data-driven analytics.

Introduction

The text describes WILD ESCAPE, a Unity-based 3D game prototype designed to raise awareness about human–wildlife conflict by letting the player control a wild animal navigating forests, villages, and cities under human pressure.

Unlike many games that portray animals as enemies, this project emphasizes that animal aggression often results from habitat loss, fear, and survival stress. The system uses a rule-based AI approach (not machine learning), where behavior is controlled through scripted logic such as detection radius, attack range, collision events, and animation triggers.

The game is built in Unity using C#, with components like NavMeshAgent navigation, Animator systems, Cinemachine cameras, UI controls, and physics-based interactions. The architecture includes modules for animal control, human AI behavior, menu flow, and game bootstrap, enabling movement, combat, health tracking, and level selection.

Testing shows that:

• The player can successfully control animal movement and actions
• Human NPCs detect and attack the animal
• Health and damage systems work correctly
• Menu flow and camera transitions operate smoothly

Conclusion

WILD ESCAPE presents a Unity-based wildlife survival simulation that uses rule-driven game AI to explain human-wildlife conflict from the animal perspective. The prototype integrates player movement, human threat detection, health feedback, collision damage, UI controls, level selection and Cinemachine-based camera flow. The work shows that simple transparent AI rules can be effective for awareness-oriented gameplay. Future work can add multiple playable animals, richer ecosystems, patrol-based human behavior, animal stamina, rescue objectives, mobile optimization and analytics. If an ML extension is required, future gameplay logs such as position, health, collision count, route choice and escape/failure outcome could become a dataset for behavior prediction or adaptive difficulty. That dataset is not part of the present implementation.

References

[1] IUCN SSC Human-Wildlife Conflict and Coexistence Specialist Group, IUCN SSC Guidelines on Human-Wildlife Conflict and Coexistence, 2023. [Online]. Available: https://www.hwctf.org/guidelines [2] M. Prensky, Digital Game-Based Learning. New York, NY, USA: McGraw-Hill, 2001. [3] Unity Technologies, Unity Manual: NavMesh Agent. [Online]. Available: https://docs.unity.cn/2019.1/Documentation/Manual/class-NavMeshAgent.html [4] Unity Technologies, Unity Manual: Animator Controller. [Online]. Available: https://docs.unity3d.com/Manual/AnimatorControllers.html [5] Unity Technologies, Cinemachine FreeLook Camera Documentation. [Online]. Available: https://docs.unity.cn/Packages/com.unity. cinemachine@ 2.9/manual/CinemachineFreeLook.html [6] Microsoft, C# Language Reference. [Online]. Available: https://learn.microsoft.com/en-us/dotnet/csharp/language-reference/ [7] WILD ESCAPE project files, Assets/MMW/ZenG Ultra.unity and Assets/MMW scripts, local Unity project workspace, 2026. [8] WILD ESCAPE project documentation, WILD_ESCAPE_PROJECT_DOCUMENTATION_TOC_WITH_SUBTOPICS_FINAL.docx, local project report, 2026.

Copyright

Copyright © 2026 K. Manikanta, K. Suma, Y. Jagadeesh Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.