AUTOMOTIVE


power efficiency, the chief concern for ADAS engineers is minimising latency. The need for low latency to deliver high levels of both functional safety and driver experience, as well as low power consumption requirements, makes ADAS applications inextricably bound to the capability of the silicon hardware. Single-chip strategy


One example of a system created using this single-chip approach is Aisin’s next- generation Automated Park Assist (APA) system that combines signals from four surround view cameras and 12 ultrasonic sensors. By also using high-accuracy machine learning models, this APA can understand and react to the dynamic environment around the vehicle. It can support autonomous control of the steering, brakes, transmission, and speed, to deliver a smooth hands-free parking experience.


It’s worth noting that the AMD suite of functional safety developer tools help development teams target ISO 26262 ASIL-B certification. Also, the AMD Zynq UltraScale+ is fully certified for use in functional safety applications, in accordance with ISO 26262, IEC 61508, and ISO 13849 up to ASIL B/SC2. This means AMD offers automotive T1 suppliers and OEMs a portfolio of automotive-grade FPGAs and adaptive SoCs, which deliver the high reliability and low latency required by ADAS on


the same 16nm silicon.


In concept, ADAS combines information from various sources to improve perception and decision-making. Utilising multiple sources of data lets the system derive richer information that can enhance the safety and performance of the driver assistance features. This provides the foundation for the system’s ability to detect and track objects, identify potential hazards, and make informed decisions like adaptive cruise control, lane-keeping assistance, and automatic


emergency braking. Techniques like ‘early fusion’ are also gaining interest and the next generation AMD 7nm Versal AI Edge family combines the DAPD capabilities of its 16nm silicon with AI accelerations to combine sensors and supplement the synchronised data stream with object lists and other information.


Efficient aggregation and processing of multiple streams of sensor data has a critical role in enabling more reliable and sophisticated autonomous driving capabilities.


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