Geofenced Predictive Safety for Autonomous Logistics Vehicles: MATLAB Simulation and Arduino Hardware-in-the-Loop Validation in High-Risk Urban Zones

Abstract

The dynamic advancement of intelligent transportation systems holds the promise of a better future with higher levels of road safety and efficiency. Nevertheless, the interaction between intelligent logistics vehicles and other vulnerable road users in high-risk areas remains a major challenge. This project brings the promise of a better future concerning road safety to life through the design of an intelligent system for “geofencing.” Geofencing describes an intelligent system that uses intelligent boundaries to improve road safety. It involves establishing boundaries in areas of greater road-safety concern. Often, such boundaries intended to alert drivers of impending danger. However, in the project\'s design, the predictive intelligent system ensures that, once intelligent transportation systems detect an intelligent boundary layer, the vehicle slows down as it drives through it. The predictive intelligent system was developed using simulations in MATLAB, integrated with an Arduino hardware-in-the-loop. This research postulates that a layer of automated protection woven into the transportation network does not constrain mobility but provides additional reliability for autonomous logistics, especially in protecting the most vulnerable sectors of the population.

Introduction

The paper addresses a critical safety challenge in autonomous vehicle (AV) deployment: ensuring proactive speed compliance in sensitive urban zones such as schools and residential areas. Although highway autopilot systems are relatively mature, urban environments require greater contextual awareness. In many regions, including India, mandated low speed limits (25–30 km/h near schools) are often ignored, leading to accidents and highlighting the inadequacy of passive signage and reactive safety systems.

Problem Statement

Traditional traffic and driver-assistance systems are primarily reactive, reducing speed only after detecting obstacles. This is insufficient for preventive safety, where speed must be reduced before potential hazards appear.

Key limitations of existing systems:

• Sensor limitations (poor visibility, bad weather).

• Latency issues (late braking after obstacle detection).

• Human override risks (fatigue, cultural driving behaviors).

Proposed Solution: Predictive Safety Geofencing System

The paper proposes a GPS-based geofencing framework that enforces speed limits automatically when vehicles enter predefined “sensitive zones.”

Main Contributions:

1. Proactive Control Framework – A supervisory algorithm overrides throttle control to enforce a strict speed cap (e.g., 20 km/h) upon geofence entry.

2. Simulation & Hardware Validation – System tested using MATLAB’s Automated Driving Toolbox and validated via an Arduino-based hardware-in-the-loop (HIL) prototype.

3. Enhanced Situational Awareness – Real-time mapping and status logging for operators.

Unlike traditional PID or path-following controllers, this system introduces a higher-authority safety governor layer that imposes speed constraints based on geolocation.

Methodology

The system consists of three subsystems:

1. Perception Module

• Simulated GPS using MATLAB toolboxes.

• Continuously tracks vehicle position.

2. Decision Logic (Geofencing Core)

• Maintains database of sensitive zones (center + radius).

• Uses Haversine distance formula to detect entry.

• If inside zone → clamp target velocity (e.g., 20 km/h).

3. Control Actuation

• Enforces reduced speed via vehicle interface.

• Arduino Mega 2560 + PWM control of DC motor (via L293D IC).

• LEDs indicate “Safety Mode” activation.

Results and Evaluation

Simulation Features:

• GUI-based virtual map with circular geofences.

• Predefined routes (Highway, City, Circular).

• Adjustable weather and traffic conditions.

Hardware Validation:

• MATLAB communicates with Arduino via serial port.

• Motor speed proportionally reduced when geofence detected.

• Visual feedback confirms correct actuation.

Evaluation Metrics:

• Response latency (zone entry → deceleration).

• Speed adherence percentage.

• Trajectory stability (no lateral instability due to speed drop).

• Hardware synchronization accuracy.

Practical Implications

• Enables digitized and dynamic speed limits without changing physical infrastructure.

• Supports smart urban planning (temporary zones near construction or time-based school zones).

• Aligns with the concept of the Internet of Autonomous Vehicles (IoAV).

• Improves traffic flow by enabling gradual deceleration rather than abrupt human braking.

Limitations

1. GPS Accuracy Issues – Urban canyon effects may cause localization errors.

2. Cybersecurity Risks – Geofence spoofing could manipulate vehicle behavior.

3. Mixed Traffic Conflicts – Autonomous vehicles strictly adhering to 20 km/h may cause tailgating or aggression from human drivers.

Ethical Considerations

• Autonomy vs. Paternalism – System overrides driver control, potentially limiting emergency decision-making.

• Liability Issues – Determining responsibility in accidents within geofenced zones remains complex.

Conclusion

The idea has been successfully implemented as a project, proving not only its feasibility but also its necessity for ensuring predictive safety in autonomous logistics, especially given its implementation as a combination of MATLAB programming and Arduino. Essentially, it was able to show, as a proof of concept, how a vehicle could be made to slow down to a speed of only 20 km/h once it is inside a high-risk zone, thus proving how it could be interpreted as a fundamental step towards a transport system that prioritizes, first and foremost, safety, especially for those that it is most needed – at a time when there is a clearly defined gap in how to most effectively address safety concerns, especially in areas where there is a high rate of accidents, especially around schools and hospitals.

References

[1] Zheng, Yang, Wang, Jiawei, Li, Keqiang, “Smoothing Traffic Flow via Control of Autonomous Vehicles,” IEEE Internet of Things Journal (Volume: 7, Issue: 5, May 2020), 2018. doi:10.1109/JIOT.2020.2966506 [2] Dong, X., Cappuccio, M., Alnajjar, F., Ali, L., Jassmi, H. A., Debie, E., Ghasrikhouzani, M., Lanteri, A., McGregor, T., Molloy, O., Plebe, A., Regan, M., Sandoval, E., & Zhang, D. (2025). Why autonomous vehicles are not ready yet: A Multi?Disciplinary review of problems, attempted solutions, and future directions. Journal of Field Robotics. https://doi.org/10.1002/rob.70108. [3] Michieli, U., & Badia, L. (2018). Game Theoretic Analysis of Road User Safety Scenarios Involving Autonomous Vehicles. Game Theory Analysis of Road User Safety Scenarios. https://doi.org/10.1109/pimrc.2018.8580679 [4] Muzammel, C. S., Spichkova, M., & Harland, J. (2024). Cultural influence on autonomous vehicles acceptance. In Lecture notes of the Institute for Computer Sciences, SocialInformatics and Telecommunications Engineering (pp. 538–547). https://doi.org/10.1007/978-3-031-63992-0_37 [5] Jain, Harshit, Babel, Priyal, “A Comprehensive Survey of PID and Pure Pursuit Control Algorithms for Autonomous Vehicle Navigation,” 2024. https://arxiv.org/pdf/2409.09848v1 [6] D’Andréa-Novel, B., Menhour, L., Fliess, M., & Mounier, H. (2016). Some remarks on wheeled autonomous vehicles and the evolution of their control design**Work partially supported by the French national project IN-OVE/ANR 2010 BLANC 308. IFAC-PapersOnLine, 49(15), 199–204. https://doi.org/10.1016/j.ifacol.2016.07.732 [7] Charlton, John, Gonzalez, Luis Rene Montana, Maddock, Steve, Richmond, Paul, “Simulating Crowds and Autonomous Vehicles,” Transactions on Computational Science XXXVII, 2020, 129-143, 2020. doi:10.1007/978-3-662-61983-4 [8] Jasour, A., Huang, X., Wang, A., & Williams, B. C. (2021). Fast nonlinear risk assessment for autonomous vehicles using learned conditional probabilistic models of agent futures. Autonomous Robots, 46(1), 269–282. https://doi.org/10.1007/s10514-021-10000-1 [9] Jameel, F., Chang, Z., Huang, J., & Ristaniemi, T. (2019). Internet of Autonomous Vehicles: Architecture, Features, and Socio-Technological Challenges. IEEE Wireless Communications, 26(4), 21–29. https://doi.org/10.1109/mwc.2019.1800522 [10] Bhemavarapu, Sai Varun Reddy, “Cybersecurity for Autonomous Vehicles,” 2025. https://arxiv.org/pdf/2504.20180v

Copyright

Copyright © 2026 Ranjitha K, Vijayakumar J, Mervin Paul Raj M. 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.