๐ฆ๐ต๐ฎ๐ฟ๐ถ๐ป๐ด ๐ต๐ถ๐ด๐ต๐น๐ถ๐ด๐ต๐๐ ๐ณ๐ฟ๐ผ๐บ ๐๐ง๐ฆ ๐๐๐ฟ๐ผ๐ฝ๐ฒ๐ฎ๐ป ๐๐ผ๐ป๐ด๐ฟ๐ฒ๐๐ ๐ฎ๐ฌ๐ฎ๐ฒ ๐ถ๐ป ๐๐๐๐ฎ๐ป๐ฏ๐๐น!
At this yearโs event, Katrin Al Jezany from AVL represented the Cynergy4MIE project during the session: โ๐๐ฑ๐ฎ๐ฝ๐๐ถ๐๐ฒ ๐ข๐ฝ๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป๐ฎ๐น ๐๐ฒ๐๐ถ๐ด๐ป ๐๐ผ๐บ๐ฎ๐ถ๐ป๐: ๐๐ป๐๐ฒ๐ด๐ฟ๐ฎ๐๐ถ๐ป๐ด ๐ฉ๐ฒ๐ต๐ถ๐ฐ๐น๐ฒ ๐ฎ๐ป๐ฑ ๐๐ป๐ณ๐ฟ๐ฎ๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ ๐๐ป๐๐ฒ๐น๐น๐ถ๐ด๐ฒ๐ป๐ฐ๐ฒ ๐ณ๐ผ๐ฟ ๐ฆ๐ฎ๐ณ๐ฒ ๐ฎ๐ป๐ฑ ๐ฅ๐ฒ๐๐ถ๐น๐ถ๐ฒ๐ป๐ ๐๐๐๐ โ, moderated by Selim Solmaz from Virtual Vehicle Research GmbH.
During her presentation, Katrin introduced the Cynergy4MIE SC4 demonstrators, showcasing cutting-edge approaches for emerging automated driving systems.
Key focus areas included:
โซ๏ธTraffic flow optimization through cooperative sensing, radar-based V2V communication, and V2X connectivity.
โซ๏ธRoad condition monitoring using multi-source sensing, crowd-sourced vehicle data, and satellite observations.
โซ๏ธShared situational awareness and cooperative perception to enhance safety and efficiency in mixed traffic environments.
Cynergy4MIE is paving the way toward a robust technology stack for resilient mobility, demonstrating how energy-efficient electronics, Edge AI, and advanced sensor integration can drive scalable and sustainable infrastructure intelligence.