AI-Powered Vehicles: Software-Defined as an Innovation Platform

1. Introduction

• Software-Defined Vehicles (SDVs) are shifting the automotive industry from hardware-driven to software-driven innovation. A new kind of autonomous and AI-powered vehicle technology is emerging, representing a significant shift in both consumer awareness and industry innovation.
• Artificial Intelligence (AI) integration is accelerating this transformation, enabling smarter, safer, and more adaptive vehicles. The concept of AI-powered vehicles is central to the ongoing transformation of the automotive industry.
• Global OEMs are seeking solutions to improve user experience, reduce maintenance costs, and support the evolution of advanced mobility.
• XEPS, as a technology-driven company specializing in Electric Power Steering (EPS) systems, plays a critical role by combining steering expertise with AI-driven software platforms.

These innovations ultimately aim to benefit customers by improving safety, experience, and reliability.

2. Value of AI-Powered SDVs

1. Enhanced User Experience

• Real-time diagnostics and AI assistants replace complex manuals.
• Personalized driving support and predictive maintenance.
• AI enables the introduction of new feature sets tailored to individual drivers.

2. Operational Efficiency for OEMs

• Automated workflows reduce aftersales costs.
• Advanced data-driven insights speed up innovation.
• Over-the-air updating features keep vehicles current and efficient.

3. Improved Safety & Reliability

• Edge intelligence ensures quick response to critical faults.
• AI-enhanced predictive systems reduce breakdown risks.
• AI integration leads to ongoing optimization of vehicle performance throughout the vehicle’s lifecycle.

These advancements enable new services for both customers and manufacturers, such as enhanced connectivity, ride-sharing, and integrated smart city solutions.

3. Industry Pain Points

In the past, traditional diagnostics were more manageable, but these methods are now insufficient for handling the complexity of modern vehicles.

Increasing vehicle complexity, with various aspects such as advanced driver assist systems and integrated software, makes traditional diagnostics inefficient.

• Overreliance on manual expertise leads to high costs.
• Data fragmentation across systems limits efficiency.
• Customers are experiencing declining satisfaction due to slow service cycles.

4. Software-Defined Vehicle Architecture

A software-defined vehicle (SDV) architecture represents a fundamental shift in how vehicles are designed, operated, and updated. At its core, this architecture is built around a network of electronic control units (ECUs) that manage everything from navigation and driver assistance to critical vehicle safety features. Unlike traditional vehicles, where hardware dictated capabilities, a software defined vehicle relies on software as the primary driver of innovation and functionality. This modular and scalable system allows manufacturers to introduce new features and technologies through software updates, keeping vehicles at the cutting edge long after they leave the factory.

The integration of artificial intelligence and machine learning is closely linked to the evolution of SDV architecture. These technologies enable vehicles to process vast amounts of data from sensors and other systems, allowing them to navigate safely and efficiently in complex scenarios. Whether it’s adapting to new driving environments or responding to unexpected situations, the software defined vehicle architecture empowers vehicles to operate with a level of intelligence and adaptability that was previously unattainable. As a result, SDVs are not only enhancing vehicle safety and driver assistance, but also paving the way for the next generation of autonomous driving capabilities.

5. Vehicle Communication and Connectivity

Vehicle communication and connectivity are at the heart of modern autonomous driving systems. For autonomous vehicles to navigate safely and deliver optimal performance, they must continuously exchange data with other vehicles, infrastructure, and cloud-based platforms. This real-time communication is made possible by a range of technologies, including cellular networks, Wi-Fi, and dedicated short-range communication (DSRC) systems.

Through these connections, vehicles share critical information about traffic conditions, road hazards, and the movements of other vehicles. This data exchange not only enhances vehicle safety but also supports advanced driver assistance systems (ADAS) functions such as lane departure warning and adaptive cruise control. By leveraging connectivity, autonomous vehicles can anticipate and respond to changing driving scenarios, improving both safety and efficiency. As the automotive industry moves toward full self driving capabilities, robust vehicle communication and connectivity will be essential for realizing the full potential of autonomous driving systems and delivering a seamless, intelligent driving experience.

6. AI Use Cases in SDVs

These use cases not only demonstrate the practical application of AI in SDVs but also help develop more advanced and reliable SDV platforms.

1. Smart Diagnostics (AI Technician)

• Leverages design documents, historical faults, and live sensor data.
• The function of the AI technician is to provide actionable driver guidance like “safe to continue driving” decisions.

2. Dynamic Data & Edge Processing

• On-demand data transfer reduces network and storage overhead.
• These solutions are implemented to enable real-time support for ADAS and Steer-by-Wire (SBW) systems.

3. Automated Operations & OTA Updates

• Remote updates for both software and control modules (EPS, ECUs).
• Ability to introduce new feature sets remotely.
• No-code automation simplifies OEM engineering workflows.

7. Three Pillars for AI Adoption in Automotive

1. Flexible E/E Architecture (SDN, SDDS, SDC) is a key pillar that the industry is actively developing to support AI adoption.
2. Precise Data Collection & Transmission for steering systems, sensors, and ADAS is being developed to ensure reliable and accurate information flow.
3. Deep AI Integration with generative AI, LLMs, and predictive algorithms is another area under continuous development.

The aim of developing these pillars is to improve safety, enhance connectivity, and drive innovation in the automotive industry.

8. XEPS Contribution to AI-Driven SDVs

• Core expertise in EPS systems: As a company, XEPS specializes in Steering Columns, Steering Racks, and advanced electronic control units.
• XEPS continues to develop pioneering Steer-by-Wire (SBW) solutions that require ultra-reliable, AI-enhanced diagnostics and edge computing.
• Integration of ADAS steering control with SDV platforms, where AI-driven diagnostics are implemented to enable autonomous and semi-autonomous driving features.
• Strong synergy between mechanical steering hardware and AI-powered software platforms, ensuring safety, precision, and innovation.

9. Future Outlook & Challenges

Trends:

• AI + SDV will become a competitive advantage for automakers.
• Cloud–edge–vehicle ecosystems will accelerate SBW adoption.
• Predictive steering diagnostics will reshape safety standards.
• Updating features over-the-air will be crucial for maintaining competitiveness, allowing real-time improvements and integration with V2X technology.

Challenges:

• Ensuring data privacy across connected systems, including controlling access to vehicle data and systems for privacy and security.
• Cross-platform compatibility for EPS and SDV software.
• Continuous model training required for high-accuracy performance.

The concepts behind AI-powered vehicles are influencing the future of mobility, shaping how vehicles interact, learn, and adapt. Additionally, the impact of AI-powered vehicles extends to various industries beyond automotive, driving innovation and transformation across multiple sectors.

10. Conclusion

• AI-powered SDVs represent the next frontier in intelligent mobility.
• With expertise in EPS, ADAS steering, and SBW, XEPS contributes to building the world’s most advanced AI-powered vehicle technologies, driving innovation in the autonomous vehicle industry and shaping the future of mobility.
• The broader adoption of self driving vehicles is transforming how people and goods move, making transportation safer and more efficient.
• The convergence of steering innovation and AI-driven software platforms is redefining cars through advanced, software-defined technologies and will continue to define the future of connected and autonomous vehicles.