How a Standardized Approach Can Accelerate Development of Safety and Security in Automotive Imaging Systems
By Philip Hawkes, Rick Wietfeldt and Hiroo Takahashi (MIPI Alliance)
EETimes Europe
Imaging systems, which use cameras, radar, and LiDAR, are essential to advanced driver assistance and autonomous driving systems. Today, the systems used to deliver SAE Level 2+ capabilities may leverage 10 or more image sensors, and that number is projected to increase as systems supporting higher levels of driving assistance and autonomy are introduced.
Imaging systems play a safety-critical role in vehicles, and the failure or compromise of even the most basic applications, such as a simple backup camera, can potentially lead to severe consequences. Protecting such systems from safety and security risks is therefore paramount to the safety and security of the whole vehicle, its passengers, and their surroundings. Developers and system designers must mitigate against risks from system failures, installation of substandard or unauthorized image system components, malicious manipulation of image data, and violations of occupant privacy.
This article explains how a standardized, industry-led framework of specifications provides a blueprint for developing functionally safe and secure automotive imaging systems.
To read the full article, click here
Related Semiconductor IP
- MIPI SWI3S Manager Core IP
- MIPI I3C Target Device
- MIPI CSI-2 IP
- MIPI C/D Combo PHY RX - GlobalFoundries 22FDX
- Controller for MIPI Soundwire
Related White Papers
- How Low Can You Go? Pushing the Limits of Transistors - Deep Low Voltage Enablement of Embedded Memories and Logic Libraries to Achieve Extreme Low Power
- Integrating VESA DSC and MIPI DSI in a System-on-Chip (SoC): Addressing Design Challenges and Leveraging Arasan IP Portfolio
- Role of Embedded Systems and its future in Industrial Automation
- The Growing Imperative Of Hardware Security Assurance In IP And SoC Design
Latest White Papers
- Fault Injection in On-Chip Interconnects: A Comparative Study of Wishbone, AXI-Lite, and AXI
- eFPGA – Hidden Engine of Tomorrow’s High-Frequency Trading Systems
- aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on RawAudio
- Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
- Hardware Acceleration of Kolmogorov-Arnold Network (KAN) in Large-Scale Systems