World Artificial Consciousness Association Smart Transportation Specialized Committee Established in Guangzhou

White Paper on High-Risk AI Testing, Evaluation and Governance in Smart Transportation (2026) and Initiative on the Development of Artificial Consciousness in Smart Transportation Released

On April 23, 2026, the founding ceremony of the Smart Transportation Specialized Committee of the World Artificial Consciousness Association was held in Guangzhou. The event was also incorporated into the agenda of the Members' Representative Assembly of the Guangdong Intelligent Transportation Association. At the meeting, the World Artificial Consciousness Association presented a plaque to the Guangdong Intelligent Transportation Association and issued an appointment certificate to Zhendong Xie, president of the Guangdong Intelligent Transportation Association, appointing him director of the Smart Transportation Specialized Committee of the World Artificial Consciousness Association. Experts and scholars, enterprise representatives and association colleagues from the fields of smart transportation, autonomous intelligence, artificial intelligence governance, and testing and evaluation attended the event and jointly witnessed the establishment of the Committee. The meeting also released the White Paper on High-Risk AI Testing, Evaluation and Governance in Smart Transportation (2026) and the Initiative on the Development of Artificial Consciousness in Smart Transportation, providing important references for subsequent research, application, and cooperation in related fields.

Addressing Real-World Needs: Promoting Deep Integration between Frontier Research and Industry Scenarios

At present, artificial intelligence is accelerating its transition from technological breakthroughs to industrial applications, while increasingly entering a new stage in which rule-building, scenario validation, and governance assessment are advanced in parallel. For smart transportation, a field with high complexity, strong coupling, and stringent safety requirements, the industry's focus is shifting further from simply being "more intelligent" to being "more reliable, more assessable, more auditable, and more governable."

Against this backdrop, smart transportation is not only an important field for AI application, but also a key practical setting for building evaluation systems, improving governance mechanisms, clarifying responsibility boundaries, and forming social trust in high-risk AI scenarios. Whether in vehicle-road-cloud integration, collaborative autonomous driving, low-altitude intelligent transportation, traffic emergency response, or the coordinated optimization of complex transportation networks, higher requirements are being placed on system safety, stability, explainability, transparency, and sustainable governance capacity.

The establishment of the Smart Transportation Specialized Committee of the World Artificial Consciousness Association is intended precisely to build a more sustained and organized cooperation platform between frontier research and real-world application, and to promote more systematic working mechanisms in the smart transportation field in areas such as evaluation, governance, auditing, training, pilots, and standards research.

Taking Artificial Consciousness as a Frontier Direction and Evaluation/Governance as the Practical Lever

The World Artificial Consciousness Association was jointly initiated and established in 2023 by international experts, scholars, industrial institutions, and other parties. It has held three consecutive World Artificial Consciousness Conferences and has continued to organize exchanges on issues such as human-centered autonomous intelligence development, governance, evaluation, and application.

Exploration related to artificial consciousness represents one of the frontier directions in which future intelligent systems may continue to evolve in perception, understanding, decision-making, collaboration, and intent constraints. For smart transportation, the significance of this type of research does not lie only in enhancing systems so that they become "more human-like" or "more intelligent." More importantly, it offers new perspectives and tools for complex autonomous systems to form operating mechanisms that are more reliable, assessable, auditable, and governable in high-risk scenarios.

At the current stage, if frontier exploration is to generate genuine social and industry value, it must be further translated into real capacity building. In other words, artificial consciousness may remain a frontier banner, but practical work must be further grounded in specific directions such as evaluation systems, governance mechanisms, governance auditing, education and training, scenario assessment, and pilot demonstrations in high-risk AI settings. The establishment of the Committee and the release of the white paper are an important embodiment of this approach in the field of smart transportation.

The White Paper Focuses on Building Evaluation and Governance Capabilities for High-Risk AI Applications

The White Paper on High-Risk AI Testing, Evaluation and Governance in Smart Transportation (2026), released at this meeting, focuses on the key issues facing high-risk AI applications in the smart transportation field. It puts forward systematic recommendations around high-risk scenario identification, construction of an evaluation framework, improvement of governance mechanisms, pilot demonstration pathways, and talent capability development.

The white paper emphasizes that, in the field of smart transportation, the focus of future work should not only be on improving the level of intelligence, but also on building a governance capability system that covers the entire process of system design, testing, deployment, operation, and audit. Around this objective, the white paper proposes that priority should be given to the following aspects:

1. High-risk scenario identification and grading. Focusing on key directions such as vehicle-road-cloud integration, collaborative autonomous driving, low-altitude intelligent transportation, and traffic emergency response, a scenario classification, grading, and risk identification framework should be established to provide a unified object basis for evaluation and governance work.

2. Evaluation framework and key indicator development. For high-risk AI systems, a more operable, verifiable, and reusable evaluation methodology should gradually be developed around dimensions such as safety, reliability, explainability, robustness, auditability, compliance, and alignment.

3. Governance mechanisms and governance auditing. A closed-loop mechanism should be promoted from development, testing, deployment, and operation to incident reporting, responsibility tracing, review, and improvement. Mechanisms such as third-party evaluation, governance auditing, and thematic assessment should also be explored to enhance system governance capacity and the transparency of industry governance.

4. Education, training, and talent capability development. The application of high-risk AI in smart transportation requires not only technical talent, but also interdisciplinary talent that understands scenarios, evaluation, governance, and rules. The white paper recommends gradually forming a talent support system through curriculum development, case library development, thematic seminars, and capability training.

5. Pilot demonstrations and standards research. Through focused, verifiable, and replicable scenario pilots, theoretical research, technical validation, and application needs should be coordinated and linked. Experience should be gradually accumulated in practice to form technical guidelines, group standards, work specifications, and cooperation mechanisms.

DIKWP and Other Methodological Explorations Can Provide Supporting Perspectives for Smart Transportation Evaluation and Governance

At the methodological level, the white paper proposes that DIKWP (Data, Information, Knowledge, Wisdom, and Purpose/Intent) and other methodological explorations may be incorporated to provide analytical frameworks and evaluation perspectives for the testing, evaluation, and governance of high-risk AI systems in smart transportation, from data governance, information fusion, knowledge rules, and decision optimization to intent constraints.

In this framework, the data layer focuses on collection quality, label bias, privacy protection, and spatiotemporal synchronization. The information layer focuses on perception fusion, situation recognition, and anomaly detection. The knowledge layer focuses on rule expression, knowledge constraints, and explainability. The wisdom layer focuses on decision optimization, risk trade-offs, and emergency anticipation. The intent layer emphasizes goal constraints, value alignment, responsibility boundaries, and audit accountability. The white paper states that the practical significance of such methods does not lie in increasing conceptual complexity, but in providing more engineering-oriented, structured, and auditable analytical tools for high-risk AI applications.

Yucong Duan: Frontier Exploration Should Be Further Grounded in Practical Capabilities That Can Be Evaluated, Trained, and Governed

At the founding ceremony, Yucong Duan stated that exploration related to artificial consciousness remains one of the frontier directions for the future development of autonomous intelligence. However, for smart transportation, an application scenario characterized by high complexity, strong coupling, and stringent safety requirements, what matters more at the current stage is to further transform frontier research into real capacity building, especially by forming executable, verifiable, and scalable methodological systems in areas such as evaluation, governance, auditing, training, and pilots.

He said that the Committee may focus its future work on three areas:

First, it should promote research on technical frameworks, evaluation indicators, governance auditing, and ethical norms around high-risk AI scenarios in smart transportation. Through scenario-oriented problem definition and evaluation framework construction, related research can be advanced from conceptual discussion toward methodological implementation and industry adaptation.

Second, it should focus on key directions such as vehicle-road-cloud integration, collaborative autonomous driving, and low-altitude intelligent transportation, and promote coordinated linkage among theory, technology, industry, and pilots. Through interdisciplinary cooperation and focused pilots, a closed-loop mechanism can be formed from research to validation and from validation to promotion.

Third, relying on the platform resources of the World Artificial Consciousness Association, it should strengthen exchanges and cooperation among domestic and international academia, industry, and application departments. It should promote the sharing of outcomes, mutual learning from experience, and alignment of rules, thereby improving the level of cooperation in high-risk AI evaluation and governance in the smart transportation field.

Zhendong Xie: Focusing on Practical Needs to Deliver Implementable, Replicable, and Scalable Outcomes

Zhendong Xie, the newly appointed director of the Smart Transportation Specialized Committee of the World Artificial Consciousness Association and president of the Guangdong Intelligent Transportation Association, said that after its establishment, the Committee will rely on the industry resources and organizational foundation of the Guangdong Intelligent Transportation Association. Oriented toward the practical needs of smart transportation development, it will focus on advancing research on evaluation systems for high-risk AI scenarios, construction of governance mechanisms, scenario demonstrations, education and training, and transformation of research outcomes. It will strive to form a number of practical results that are implementable, replicable, and scalable, thereby supporting the high-quality development of smart transportation.

He stated that the development of smart transportation has entered a new stage that places greater emphasis on system reliability and governance capacity. The Committee will work to better integrate frontier research with industry needs, better combine the construction of an exchange platform with the solution of practical problems, and better connect research, pilots, training, standards, and cooperation. In doing so, it aims to become an important platform for capacity building in high-risk AI evaluation and governance in the smart transportation field.

Using the Committee as a Platform to Form a Virtuous Cycle of Research, Evaluation, Governance, Training, and Pilots

Participants noted that the establishment of the Committee and the release of the two outcomes will help further connect AI-related research capabilities with smart transportation industry resources, and will respond to the industry's practical needs in scenario implementation, standards research, coordination mechanisms, and governance capacity building. In particular, against the backdrop of AI applications moving faster toward both industrialization and rule-based development, the establishment of the Committee is expected to provide the smart transportation field with a more pragmatic cooperation mechanism and a more operable working pathway.

Going forward, the Committee will continue to advance exchanges and cooperation, subject research, pilot demonstrations, education and training, governance auditing, and capacity building around the priority directions proposed in the white paper. It will promote the formation of a mutually reinforcing virtuous cycle of research, evaluation, governance, training, and pilots, and support the development of smart transportation toward greater safety, efficiency, convenience, and trustworthiness.