QNX and Ouye Adapt SDP 8.0 for Longquan 560 ADAS SoCs
QNX, a business unit of BlackBerry, and Ouye Semiconductor have announced a collaboration to bring QNX Software Development Platform (SDP) 8.0 to Ouye’s Longquan 560 series automotive SoCs. The combined platform targets advanced driver assistance systems (ADAS), integrating QNX’s safety-focused automotive software foundation with Ouye’s automotive-grade AI silicon.
Announced on October 28, 2026, the adaptation has already been deployed in a forward-facing smart camera project for a major domestic automotive manufacturer. The announcement marks an important step in combining automotive software and semiconductor platforms for production-oriented ADAS development in China.
The collaboration comes as automakers transition toward software-defined vehicles (SDVs), centralized computing, and zonal electrical/electronic (E/E) architectures. These changes increase the need for integrated platforms that can deliver AI processing performance while maintaining deterministic execution, functional safety, and reliable real-time operation.
Rather than requiring automakers and Tier 1 suppliers to integrate the operating-system foundation and silicon platform independently, the joint solution provides a pre-integrated starting point for ADAS development.
🚗 Software-Defined Vehicles Are Reshaping Automotive Computing #
The automotive industry is moving away from traditional architectures built around numerous independent electronic control units (ECUs). Increasingly, manufacturers are consolidating computing resources into fewer, more capable processors and adopting centralized computing and zonal control architectures.
This transition is driven by several requirements: reducing hardware and development costs, simplifying vehicle software integration, improving system scalability, and supporting increasingly sophisticated intelligent-driving functions.
ADAS is one of the most demanding applications in this environment. Functions such as object detection and collision avoidance require continuous processing of sensor data, often with strict timing and reliability requirements.
Raw AI compute performance is only part of the equation. An automotive computing platform must also provide:
- Deterministic execution: Critical workloads need predictable response times.
- Functional safety: Software and hardware must support the engineering processes required for safety-related vehicle functions.
- Real-time reliability: Sensor processing and system responses must operate within defined timing constraints.
- Scalability: The platform should accommodate additional functions and more complex vehicle architectures.
- Integration efficiency: Automakers and suppliers need to reduce duplicated engineering and validation work.
These requirements make the combination of automotive-grade SoCs and established embedded software platforms increasingly important.
⚙️ QNX SDP 8.0 Meets Ouye’s Longquan 560 Platform #
The core of the partnership is the adaptation of QNX SDP 8.0 for the Longquan 560 series SoC platform.
QNX contributes its automotive software expertise and safety-oriented foundational technologies, while Ouye provides the automotive-grade semiconductor platform and AI processing capabilities.
The result is a pre-integrated hardware-software foundation intended to help automakers and Tier 1 suppliers develop ADAS applications with less integration overhead.
QNX’s role: A reliable automotive software foundation #
QNX has decades of experience supplying embedded software for automotive and other safety-critical environments. Its technology is used in systems where predictable execution, isolation, and long-term software reliability are essential.
SDP 8.0 is designed for high-performance and compute-intensive embedded applications, including automotive systems and robotics. Its architecture provides a foundation for building applications that need to take advantage of modern processors while maintaining controlled access to system resources.
For the Longquan 560 integration, QNX provides the software baseline that customers can use when developing ADAS applications.
It is important to distinguish the general development platform from individual safety-certified components and configurations. Safety certification applies to the relevant QNX products and implementations rather than automatically covering every application built on SDP 8.0. Automakers still need to complete the required system-level engineering, integration, and validation for their specific vehicle programs.
For additional context on QNX’s automotive role, see KAD’s coverage of QNX and neueHCT’s global ADAS platform collaboration.
Ouye’s role: Automotive AI silicon #
Ouye Semiconductor supplies the Longquan 560 series SoC platform, bringing automotive-grade processing capabilities to the joint solution.
The company’s broader automotive product portfolio includes three major chip families:
- Longquan
- Gongbu
- Chunjun
According to Ouye, these families encompass more than ten chip models that have entered mass production and are deployed in multiple Chinese passenger vehicle models.
The company describes its strategy as building a full-domain chip foundation for Physical AI, with an initial focus on smart vehicles. Its approach targets the computing requirements of increasingly integrated automotive E/E architectures.
The combination of QNX software and Ouye silicon provides customers with a more integrated foundation for developing intelligent-driving systems.
🧩 A Pre-Integrated Platform Can Reduce ADAS Development Complexity #
One of the primary benefits of the collaboration is the availability of a pre-integrated software and hardware baseline.
Automotive development projects often involve multiple suppliers responsible for processors, operating systems, middleware, perception algorithms, and vehicle-level integration. Bringing these components together can require substantial engineering effort, especially when teams must validate compatibility and timing behavior across different hardware and software layers.
With QNX SDP 8.0 adapted for the Longquan 560, automakers and Tier 1 suppliers can start from a platform where the foundational software has already been brought into alignment with the target SoC.
This can help reduce several sources of development overhead:
| Development area | Potential benefit |
|---|---|
| Hardware-software integration | A pre-adapted software baseline for Longquan 560 |
| ADAS development | A common foundation for application development |
| System validation | Less initial integration work between the platform components |
| Time to market | Reduced effort before application-level development and validation |
| Platform scalability | A foundation for additional customer projects and vehicle programs |
The pre-integration does not eliminate the need for vehicle-specific development or safety validation. Each automaker still needs to adapt the platform to its sensors, algorithms, vehicle architecture, and functional requirements.
Nevertheless, establishing compatibility between the SoC and foundational software earlier in the development process can simplify a significant part of the engineering workflow.
📷 First Deployment: Forward-Facing Smart Camera #
The partnership is already moving beyond platform adaptation.
QNX and Ouye confirmed that their combined solution has been deployed in a forward-facing smart camera project for a major domestic automotive manufacturer.
A forward-facing smart camera is an important component in modern ADAS systems. It processes visual information from the road ahead and can provide inputs for functions such as object detection and collision avoidance.
Such applications need to combine AI inference with predictable system behavior. The platform must process camera data reliably while ensuring that software components operate within their defined timing and safety constraints.
The announcement does not disclose the automaker’s identity, the specific camera hardware configuration, or the detailed ADAS functions supported by the project. It also does not provide chip-level performance figures or benchmark results for the Longquan 560.
The confirmed deployment nevertheless demonstrates that the QNX-Ouye collaboration has progressed beyond a development proposal into an actual customer project.
🏭 From Chinese Vehicle Programs to Potential European Production #
Ouye says its Longquan, Gongbu, and Chunjun product families already cover more than ten mass-produced chip models used in Chinese passenger vehicles.
The company also described its collaboration with QNX as a way to provide production-oriented hardware and software combinations for next-generation ADAS and integrated monitoring systems.
Wei Wei, Ouye Semiconductor’s Vice President and General Manager of the Smart Vehicle Business Unit, said the partnership is intended to provide customers with a clearer path from development to mass production.
The company also expressed expectations that related products would enter mass production in Europe in 2027.
This is a forward-looking target rather than confirmation that European production has already begun. Realizing it will depend on customer programs, regional requirements, validation, and production schedules.
For the automotive industry, the international dimension is significant. A reusable software and hardware platform can help suppliers adapt products for different vehicle programs and markets, although regional compliance and vehicle-level validation remain essential.
🧠 Physical AI and the Next Generation of Automotive SoCs #
Ouye positions its broader semiconductor strategy around Physical AI: applying AI capabilities to systems that interact with the physical world.
In the automotive sector, this means combining computing hardware, perception algorithms, networking, and software into an integrated platform.
The company’s architectural vision is based on three levels:
- Central Brain: Centralized computing resources that coordinate major vehicle functions.
- Zonal Cerebellum: Regional computing and control components that connect vehicle subsystems.
- Edge Limbs: Distributed endpoints and devices responsible for interacting with sensors and physical components.
This model reflects the broader movement toward centralized computing and zonal E/E architectures. Instead of treating every vehicle function as an isolated ECU, manufacturers can organize computing resources into a more integrated hierarchy.
ADAS is a natural application for this approach because it relies on continuous interaction between sensors, AI processing, vehicle systems, and real-time software.
The QNX-Ouye partnership addresses one part of this architecture: combining an automotive SoC with a software foundation suitable for developing reliable intelligent-driving applications.
Similar collaborations illustrate the wider trend. For example, PUDN’s coverage of QNX and AXERA’s ADAS platform describes another effort to combine automotive AI silicon with QNX software for production-oriented intelligent-driving systems.
🔮 Outlook: Software and Silicon Integration Becomes a Competitive Advantage #
The QNX and Ouye Semiconductor partnership highlights an important development in China’s automotive semiconductor market: competition is increasingly about complete hardware-software platforms rather than processor performance alone.
As automakers adopt centralized computing and zonal architectures, they need platforms that combine AI processing, deterministic behavior, functional-safety engineering, and scalable software integration.
QNX brings an established automotive software foundation, while Ouye contributes its Longquan 560 SoC platform and broader automotive chip portfolio. Their collaboration is designed to reduce integration work and provide a more direct route from ADAS development to production deployment.
The forward-facing smart camera project provides an initial real-world application, while Ouye’s stated plans for European production in 2027 point toward potential international expansion.
Important technical details remain undisclosed, including the Longquan 560’s computing specifications, supported AI workloads, power consumption, and the exact safety certification scope of the final customer system. These details will be necessary to evaluate the platform’s capabilities more comprehensively.
Even so, the collaboration demonstrates how pre-integrated automotive software and silicon can help address the growing complexity of next-generation vehicle architectures. As Physical AI and software-defined vehicles continue to evolve, partnerships that bridge semiconductor design and safety-oriented software development are likely to become increasingly important.
