Blog Home Industry Insights   •   June 12, 2019

Three of the Most Innovative Automotive AI Applications at AutoSens Detroit

Appen recently exhibited at AutoSens Detroit, where we shared our approach to data collection and annotation for in-car navigation, infotainment, and monitoring, as well as autonomous vehicle solutions. Here are some of the coolest innovations in automotive AI, as spotted by our team:

Mercedes Benz’s Intelligent Interior

Volker Entenmann, Senior Manager of UI Functions at Daimler AG, shared how Mercedes-Benz is automating driving tasks and creating a more intuitive, connected user experience for both drivers and passengers. After releasing the MBUX (Mercedes-Benz User Experience, which included a touch input on the steering wheel, touchpad on the center console screen, and cutting-edge natural language recognition) in 2018, Mercedes wanted to assist drivers by automating functions both exterior and interior.

To establish an all-new category in seamless, intuitive user experience, the company recently launched MBUX Interior Assistant, now available in GLE and CLA models. Interior Assistant is a camera-based system that understands the body language of drivers and passengers, automating and providing direct access to functions like music, climate control, lighting, and seating. The cutting-edge technology provides proximity detection for the both the touch screen and the center console — when you put your hand close to the screen, it immediately responds by highlighting icons on the homescreen, showing the navigation bar on demand, and activating the radio and media cover flow. The system recognizes gestures, and can distinguish between the driver and different passengers.

Parallel Domain’s Synthetic World-Building

While autonomous vehicles need to be trained on millions of data points to learn tasks like object detection, collecting that data through real-world driving is a massive undertaking. Parallel Domain’s technology automatically generates virtual environments for testing autonomous vehicles, based on real-world, open street map data.

While there is not yet consensus that synthetic data a