Navigating Deep Learning to Improve ADAS

How edge computing drives real-time decision making in AI for smarter, safer automated vehicles.

Automotive ‘big data’ is here. The immense scope of data generated by automated or ADAS-integrated vehicles spans the five SAE levels of autonomous driving, with reliance on high-resolution cameras, radar, lidar, ultrasonic sensors, GPS and other sensors for vehicles to see or perceive their surroundings. Ultimately, this sensory information - massive amounts of data - is used to navigate, avoid obstacles and read road markers necessary for safe driving. Artificial intelligence (AI) is at the heart of these operations, grounded in software algorithms and fueled by deep-learning training and deep-learning inference models that are essential to faultless performance.

Enabling these vital and instantaneous processes requires AI algorithms to be trained and then deployed on-vehicle. It's a process that has developers tapping into both sophisticated software design and smart hardware strategies to protect vehicle performance that could be a matter of life or death.