Sustainable and Ethical Edge AI in Autonomous Vehicle Networks

Abstract

The development of autonomous vehicles (AVs) has brought about a new era of smart transportation. These self-driving cars use advanced software to understand their surroundings and make decisions, which helps create a smarter way of moving people and goods. To make safe and quick decisions, AVs need strong data processing with little delay. However, traditional cloud computing systems have problems like slow response times, high energy use, and privacy issues. This delay can be very dangerous when quick decisions are needed on the road. Moreover, cloud systems use a lot of energy and may raise privacy concerns because all data must be sent to outside servers. Edge Artificial Intelligence (Edge AI) offers a better solution by processing information close to the source, either directly on the vehicle or on nearby roadside units, instead of relying on far-off cloud servers. This paper looks at how sustainable and ethical Edge AI can be used in autonomous vehicle networks. It concludes that combining eco-friendly computing with responsible AI practices can help build a smarter, safer, and more trustworthy autonomous driving system.

Introduction

Autonomous vehicles function as intelligent “computers on wheels,” using sensors like cameras, LiDAR, radar, and GPS to make real-time driving decisions. Traditional cloud computing methods cause issues such as high latency, energy consumption, and unstable network connections, making them unsafe for critical operations.

To address this, Edge AI enables vehicles to process data locally through onboard AI chips, allowing faster responses, better reliability, and reduced energy use. However, it also raises ethical challenges such as data privacy, fairness, and accountability. The study aims to design an ethical and sustainable Edge AI ecosystem that ensures safety, transparency, and eco-friendliness in autonomous vehicle networks.

Key Findings from Literature:

• Muslim et al. (2023): Emphasized ethical, safe behavior in sudden traffic situations.

• McEnroe et al. (2022): Found Edge AI reduces delay and energy use.

• Khatiri et al. (2024): Advocated for realistic and ethical simulations for safer AI testing.

• Zhang et al. (2023): Proposed green edge computing and federated learning for sustainability.

• SAKURA Project (Japan): Developed ethical testing frameworks using real-world data.
  Overall, combining sustainability, ethics, and Edge AI leads to trustworthy and eco-friendly systems.

Proposed Three-Layer Architecture:

1. Edge/Vehicle Layer:
   Processes data locally for lane detection and obstacle recognition using onboard AI chips.

2. Network Layer:
   Uses 5G/6G and Federated Learning for secure, collaborative model training.

3. Cloud Layer:
   Manages large-scale updates and ensures ethical oversight and transparency.

Ethical and Sustainable Practices:

• Privacy Protection: Encryption and anonymization secure user data.

• Transparency: Explainable AI (XAI) clarifies decision-making.

• Fairness: Diverse datasets prevent bias.

• Sustainability: Energy-efficient AI reduces carbon impact.

• Accountability: System logs ensure traceability of actions.

Results:

Comparative studies show Edge AI outperforms Cloud AI:

Conclusion

Sustainable and ethical Edge AI represents the future of smart and responsible autonomous vehicle technology. By processing data locally within the vehicle and using energy-efficient algorithms, cars can make faster, safer, and fairer decisions. The combination of sustainability and ethical responsibility ensures both technological progress and public trust, along with protecting the environment. Overall, this creates a balanced system that values speed, safety, and social good, leading to a cleaner and more trustworthy future for intelligent transportation.

References

[1] Muslim, H. et al. (2023). Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range for Autonomous Vehicle Assessment. IEEE Access. [2] McEnroe, P., Wang, S., & Liyanage, M. (2022). Convergence of Edge Computing and AI for UAVs. IEEE IoT Journal. [3] Khatiri, S. et al. (2024). Simulation-Based Testing of Unmanned Aerial Vehicles with Aerialist. ACM ICSE Companion. [4] Zhang, X. et al. (2023). Green Edge AI for Connected Vehicles. IEEE Transactions on Intelligent Transportation Systems. [5] SAKURA Project (2023). Reliable and Ethical Autonomous Systems Evaluation. Japan Automobile Research Institute

Copyright

Copyright © 2025 Miss. Vaishnavi Nilate, Prof. Shafiya Sayyad. 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.