In September 2024, the Auto.AI Europe conference brought together over 200 experts in deep learning, neural networks, and perception systems for SAE Level 4 & 5 automated vehicles. At the conference, industry leaders gathered to discuss self-supervised learning, behavioral learning concepts, scalable machine learning approaches, and benchmarking perception systems. Auto.AI Europe is defining the future of mobility in a rapidly evolving landscape driven by cutting-edge AI solutions.
N-iX at Auto.AI Europe 2024
N-iX blends its deep expertise in AI and machine learning with experience in the automotive industry. As a global software solutions and engineering services company, N-iX has worked with Tier 1 and Tier 2 automotive companies, as well as OEM and other automotive-related businesses.
By leveraging AI, Data Analytics, IoT, and other advanced technologies, N-iX enables automotive companies to unlock the full potential of connected vehicle technology, optimize operations, and drive innovation. Our expertise also spans EV solutions, ADAS, advanced automotive testing services, and more.
This year, N-iX was represented by:
- Dmytro Humennyi, N-iX Expert in Robotics, Automation, and Automotive Systems
- Christian Svensson, Director of N-iX Nordic
- Solomiia Diug, N-iX Customer Engagement Manager
Following this exciting event, our experts want to share some insights regarding the future of the industry and its potential developments.
Growing global competition and the European automotive market
The European automotive sector is experiencing a significant wake-up call. The emergence of highly competitive global players like BYD Auto, offering quality products at a lower cost, is pushing European automakers to rethink their approach to innovation. To stay competitive, the industry must improve product quality while keeping costs down—a challenge driving several new trends, particularly in Advanced Driver Assistance Systems (ADAS).
The direction is clear: automotive manufacturers need to view vehicles not just as mechanical products but as sophisticated software engineering systems. This path towards digitalization aims to optimize costs, improve vehicle functionality, and ultimately create software-driven value for customers.
Core technologies framing the future of automotive
Among the top trends discussed during various panels of this conference,many experts agreed that these technologies have become the pillars of modern software engineering for autonomous vehicles:
1. Big Data
The heartbeat of autonomous vehicle technology is data. Every aspect of a car's behavior—from sensor readings to component status—generates large volumes of data. This data fuels everything from real-time decision-making to predictive maintenance.
2. Cloud Computing
Handling such vast amounts of data requires robust cloud solutions. Cloud platforms serve as a backbone for storing, processing, and analyzing vehicle data offline, thereby supporting advanced functionalities like predicting maintenance needs or optimizing routes.
3. Artificial Intelligence & Machine Learning (AI & ML)
AI and ML are at the core of autonomous driving systems, operating in two major modes:
- Offline ML (via the cloud): Processes the collected Big Data to identify trends, predict maintenance, or even suggest nearby services to the driver.
- Online ML (on-board the vehicle): This real-time computation uses integrated chips to predict events and assist in active decision-making, such as emergency braking or navigating obstacles.
Keep reading: AI in the automotive industry: Fueling a smarter, safer driving experience
4. Sensor Fusion
The most prominent topic discussed at the conference was the role of sensor fusion. Combining data from Lidar, radar, and cameras provides a comprehensive view of the vehicle's surroundings. The challenge is effectively using this data to make critical driving decisions—an area ripe for innovation and new solutions.
If you are interested in this topic, follow up with our article Sensor Fusion explained
5. Heterogeneous systems
One of the more futuristic concepts discussed is the development of heterogeneous systems, which integrate different technologies—like cloud computing, microcontrollers, and FPGAs (Field Programmable Gate Arrays)—to work in parallel and solve complex problems. These systems offer real-time control, emergency decision-making, and advanced data analytics capabilities and are likely to play a vital role in the vehicles of tomorrow.
6. Emerging trend: The rise of software-defined vehicles
To remain competitive, automotive companies are increasingly adopting a software-defined vehicle (SDV) approach. An SDV relies on a powerful, unified processor capable of handling vast data volumes, ensuring seamless connectivity to other vehicles and the cloud, and implementing AI-driven decision-making. This paradigm shift turns cars into dynamic, upgradable systems rather than static products.
The must-have technologies that power the development of such heterogeneous systems like SDVs:
- Big Data to power intelligent systems with comprehensive insights.
- Connectivity to facilitate communication between vehicles, infrastructure, and the cloud.
- AI & ML are used to drive both real-time and predictive analytics.
- Sensor Fusion integrates multiple data sources for a holistic understanding of the environment.
- System on Chip (SoC) for the computing power required for onboard data processing. Side note: we discussed the computer chip revolution in our article Will RISC-V adoption in automotive challenge traditional paradigms?
- Cybersecurity to ensure the integrity and safety of data and vehicle operations.
Addressing the known challenges
While the automotive industry is undergoing rapid digital transformation, challenges remain. For instance, the question of how best to utilize sensor data for effective, real-time decision-making is still open. The conference also highlighted a need for more holistic solutions to turn raw sensor data into actionable insights directly onboard the vehicle.
Moreover, as the move toward heterogeneous computing gains traction, combining cloud, MCU, and FPGA technologies presents a new opportunity to balance real-time processing with cloud-driven analytics, ensuring both high performance and cost efficiency.
Conclusion
The Auto.AI Europe conference provided a glimpse into the future of mobility—deeply interconnected, software-defined, and adaptive.
Events like this facilitate collaboration and knowledge sharing between top-tier industry stakeholders, leading to innovations that improve automated vehicles' safety, efficiency, and intelligence. Auto.AI Europe sets industry standards, addresses current challenges, and inspires breakthroughs in AI-driven vehicle technologies.
The shift toward embracing Big Data, AI, cloud computing, and heterogeneity indicates that the automotive industry is not only keeping pace with the digital revolution but is also leading it. For companies aiming to thrive in this environment, investing in these key technologies will be essential to stay competitive, satisfy user demands, and redefine what it means to drive.
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