White Paper: Electronics & Computers
Centralized Architecture for Automotive ADAS/AD Radar Based on Raw-ADC-data
SPONSORED BY: ![]()
Key points
Traditional edge-based radar architectures limit performance, prompting a shift toward centralized processing in modern vehicles.
Streaming raw radar data to a central computer enables superior sensor fusion and environmental mapping.
Infineon provides high-performance radar chips and platforms to simplify this transition for software-defined vehicles.
The automotive industry faces a radar revolution as traditional edge-based architectures struggle to meet ADAS and autonomous driving demands. Centralized processing offers the solution by streaming raw-ADC data to a central ECU, eliminating bottlenecks and enabling advanced sensor fusion like Deep and Early Fusion. This approach delivers sharper detection, greater accuracy, and a scalable foundation for Software Defined Vehicles. Download this whitepaper to explore why edge architectures are limited, how centralized raw-ADC processing redefines performance, and key design considerations for scalable, cost-efficient ADAS/AD systems. Stay ahead of the curve.
Don't have an account?
Overview
This whitepaper by Infineon Technologies explores the transition in automotive radar systems from traditional edge-based processing to a centralized radar architecture designed to meet the rising demands of Advanced Driver Assistance Systems (ADAS) and autonomous driving (AD).
Radar sensors are critical for ADAS/AD due to their robustness in adverse weather and lighting, offering reliable range, speed, and angle measurements. Conventional radar modules perform signal processing locally at the sensor ("edge processing"), which limits computational power, memory, and ultimately the richness of environmental perception. This edge approach constrains advanced processing techniques like AI and machine learning and leads to lower resolution, less dense radar point clouds, diminishing performance compared to LiDAR and vision systems.
To overcome these limitations, the industry is shifting toward centralized radar processing where raw ADC (Analog-to-Digital Converter) data from multiple radar satellite sensors is streamed directly to a powerful central ECU. This model leverages high-performance System-on-Chips (SoCs) with extensive compute power (exceeding 1000 TOPS) and memory, supporting complex signal processing, multi-sensor fusion ("deep fusion"), and advanced algorithms. Centralized processing improves object detection, classification (especially for vulnerable road users), multi-target tracking, system latency, and free space estimation, thereby enhancing safety, comfort, and scalability of next-gen ADAS.
Infineon supports this paradigm through its CTRX8191F and CTRX8188F radar MMICs offering best-in-class RF performance with up to 8Tx8Rx configurations for high-resolution sensing. These MMICs combine with the company's Carkit platform, a modular, validated radar module solution facilitating simplified integration of raw ADC satellite radars into centralized ADAS ECUs. This reduces development time and costs, supporting the industry's move toward Software Defined Vehicles.
Key benefits of centralized radar architecture include enhanced perception quality, reduced power consumption at radar nodes (improving EV range), streamlined software lifecycle with unified updates and cybersecurity, and cost-efficient hardware using standard PCB materials. Ultimately, by centralizing radar processing, Infineon’s solutions enable higher fidelity environmental mapping and reliable, scalable ADAS/AD systems that can evolve via software without hardware changes.
The document concludes that Infineon’s centralized radar approach is foundational for the future of autonomous driving, offering improved detection capabilities, lower latency, better fusion, and comprehensive situational awareness critical to advanced vehicle safety and performance. The whitepaper invites stakeholders to engage with Infineon for radar development support aligned to these emerging architectural trends.