eNeural Technologies introduces its Self-Learning Design Methodology, a groundbreaking approach to AI model development. As an AI SW/HW design service provider, eNeural Technologies focuses on delivering embedded AI models of the highest quality. Their in-house toolchain automates the entire AI process flow, from labeling and modeling to training, augmentation, pruning, and quantization. With the addition of the Self-Learning Design Methodology, the toolchain utilizes a small number of labeled data to train a baseline inference model. The toolchain then leverages unlabeled data to quickly converge into a highly accurate model. This methodology has resulted in more accurate models in significantly faster time-to-market, benefiting various user applications.

The solution being offered is an AI-Powered Driver Monitoring System (DMS). This system is designed to improve road safety by real-time detection of dangerous behaviors such as fatigue driving, distraction, and lack of seatbelt use. Research indicates that driving after only 4 hours of sleep can increase the likelihood of an accident by 10 times. The DMS is positioned as an effective tool to prevent such accidents, and the entire system runs on a cost-effective computing platform.

eNeural Technologies' Sensor Fusion System is designed to enhance the safety of autonomous vehicles through reliable and accurate information about the driving environment. Developed as a solution to the discrepancies often encountered in autonomous vehicles, this sensor fusion system combines camera AI detection and mmWave radar detection results. It then fuses this information using an in-model hybrid fusion method, improving adaptability and reliability in challenging conditions. Ultimately, this technology aims to make autonomous vehicles safer and more efficient, by providing real-time information about the surrounding environment.