Abstract
Vehicles with features for an advanced driver-assistance system (ADAS) are already available on the market. Technologies for image recognition are essential elements for ADAS applications, and several SoCs performing image recognition for ADAS have been developed [1–3]. As vehicles that support ADAS become more complicated, these technologies need improvement. Recently, deep neural networks (DNNs) have achieved higher recognition accuracy than traditional feature-based matching algorithms, and can be applied to applications such as road detection. Consequently, several image recognition SoCs with DNN processors [4–5] have been developed. Functional safety is important for automotive applications, so several SoCs, such as [2], comply with ISO26262. The requirements for SoCs that support ADAS features are: high performance for detecting various objects, low power use to keep execution stable in the rapidly changing environment of moving vehicles, and high safety to avoid serious accidents.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
