‍Abstract:

Traditional autonomous driving technologies often rely on deterministic programming models, which reveal limitations in safety and usability when faced with the complexity and variability of the real world. This paper draws inspiration from the multi-dimensional adaptive intelligent system design of Bitcoin and proposes a decentralized autonomous driving architecture based on Bitcoin technology. This architecture adopts the Bitcoin individual model to bind humans and vehicles, utilizes the global blockchain consensus ledger to construct a world navigation map, and incorporates P/NP emergent adaptive driving logic. The goal is to build a continuously learning, safe, and reliable autonomous driving ecosystem.

1. Introduction

Autonomous driving technology is one of the most promising applications in the field of artificial intelligence. However, current technological development still faces many challenges. The core issue is that the road traffic environment in the real world is highly dynamic and non-deterministic, whereas traditional autonomous driving programs are often based on pre-set rules and models, making it difficult to fully cover and respond to all unexpected situations. Just as Bitcoin is not a simple deterministic computing tool but a distributed system that operates securely through consensus mechanisms in a complex network, autonomous driving also needs to go beyond deterministic thinking and embrace adaptive and emergent intelligence.

2. Limitations of Existing Autonomous Driving Technologies

Current mainstream autonomous driving solutions usually rely on high-precision maps, sensor fusion, and complex control algorithms. However, these approaches have the following shortcomings:

• Dependence on high-precision maps: The update frequency and coverage of high-precision maps cannot match real-world changes in real time.
• Limitations of perception systems: Sensors perform poorly under adverse weather, occlusion, and other conditions, and have difficulty fully understanding complex traffic scenarios.
• Rigidity of deterministic logic: Predefined rules and algorithms struggle to cope with unforeseen situations and lack autonomous learning and adaptability.
• Ambiguity of safety responsibility: The disconnection between vehicle ownership and driving behavior can lead to unclear accountability.

3. An Autonomous Driving System Inspired by Bitcoin’s Design

To overcome the above limitations, this paper proposes to draw on Bitcoin’s multi-dimensional adaptive intelligent system design model to build the next-generation autonomous driving program. The core ideas of this approach include the following three aspects:

3.1. Human-Vehicle Binding via the Bitcoin Individual Model

Inspired by Bitcoin’s UTXO ledger and the 1:1 mapping between keys and assets, we bind each autonomous vehicle to a specific human individual. Each individual possesses a unique “key” to their vehicle, controlling its usage rights and data ownership. This model enables individualized management of vehicle assets and driving behavior, laying the foundation for responsibility tracing, personalized services, and data privacy protection.

3.2. Constructing a Global Navigation Map Using the Blockchain Consensus Ledger

We store key data such as road information, traffic rules, and real-time traffic conditions on a global blockchain consensus ledger. This ledger is not a static map dataset but is continuously updated and refined through collective learning and contributions from all autonomous vehicles in operation. Each vehicle acts as a distributed “sensor” during driving, perceiving its surroundings in real-time and submitting sensed data—such as new road changes, traffic jams, or accident information—to the network.

Drawing on Bitcoin’s “longest chain” as a fact-verifying oracle model (Proof-of-Work), all autonomous vehicles in the network use consensus algorithms to verify and sort the submitted information. The longest chain with the most consensus is considered the most reliable world navigation map. This decentralized mechanism—jointly maintained and updated by all participants—ensures that the navigation map remains real-time, accurate, and robust.

3.3. Adaptive Driving Logic Based on P/NP Emergence

Each autonomous vehicle can be viewed as a “miner” executing driving tasks under the guidance of the latest global navigation map. During driving, a vehicle’s perception system continuously compares actual road conditions with the global map to detect discrepancies. Other vehicles on the same or similar routes perform similar comparisons, generating their own discrepancy data.

When a sufficient number of vehicles perceive consistent deviations from the existing map in a certain area, it is recognized as a “new fact” that needs to be updated—similar to a new transaction in the Bitcoin network. Through consensus mechanisms, this new fact is verified and added to the blockchain, thus updating the global navigation map. This “emergent” intelligence, formed through massive individual perceptions and consensus, enables the autonomous driving system to continuously learn and adapt to new road conditions and traffic patterns—overcoming the inability of traditional deterministic systems to handle unknown scenarios.

4. Technical Challenges and Future Prospects

Applying Bitcoin technology to the autonomous driving field undoubtedly faces significant technical challenges, such as:

• Real-time requirements: Autonomous driving is highly sensitive to data transmission and processing latency, requiring optimization of Bitcoin technology to meet real-time needs.
• Data throughput: The massive amount of data generated by autonomous vehicles demands a high-throughput Bitcoin system to support it.
• Consensus efficiency: Efficient consensus algorithms must be designed to quickly reach agreement on road condition data.
• Privacy protection: Effective privacy protection measures are needed while sharing driving data.
• Regulatory framework: This new type of autonomous driving system requires corresponding laws, regulations, and industry standards for governance.

Despite these challenges, the adaptive autonomous driving system inspired by Bitcoin design offers a new direction for future development. By adopting Bitcoin’s decentralization, consensus mechanism, and incentive models, we may construct a more secure, reliable, and intelligent autonomous driving ecosystem—ultimately achieving truly driverless vehicles.

5. Conclusion

Traditional deterministic autonomous driving models are inadequate for coping with the complex and dynamic real world. By drawing on Bitcoin’s multi-dimensional adaptive intelligent system design—using the Bitcoin individual model, constructing a global navigation map via a blockchain consensus ledger, and applying P/NP emergent adaptive logic—we can build an autonomous driving system capable of continuous learning and self-evolution. Although realizing this vision still faces many challenges, this cross-domain integration of technologies provides new ideas and possibilities for overcoming current bottlenecks in autonomous driving development.