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Biometrics, touch controls and AI for self-driving training enter growing automotive technology market

Biometrics, touch controls and AI for self-driving training enter growing automotive technology market

Gesture recognition is expected to blossom as a method of interaction between vehicles and the people traveling in them, with Global Market Insights forecasting the $750 million market size in 2019 to grow by a 17.5 percent CAGR from 2020 to 2026.

Lighting systems are expected to slightly outstrip hand and fingerprint-based systems in terms of growth rate. Gesture recognition will be deployed to enhance user experiences and allow more focus to remain on driving by simplifying interactions like scrolling through menus, according to the 216-page “Automotive Gesture Recognition Market…” report.

Technologies recently revealed by Samsara, Synaptics, and Mercedes Benz provide driver monitoring and interaction capabilities, while Oxbotica is applying advanced image-altering technology to the development of autonomous driving systems.

Fleet control with facial biometrics

Samsara has launched a suite of driver safety capabilities for its mobile platform and Samara AI Dash Cams, including biometric facial recognition for driver identification and tracking, the Commercial Carrier Journal reports.

The facial recognition function powers distracted driving detection, which along with tailgating detection issues preventative alerts and generates a configurable safety score. The system also provides an enhanced safety report, compiling data such as speeding and behavior trends across the fleet.

NYC commercial ambulance service SeniorCare EMS has trialled the system on its 200 vehicles since January, in part to ensure drivers stay off their mobile devices while on the road. Showing drivers footage of themselves succumbing to the temptation to look at a smartphone helps drivers avoid repeating the error, a representative of the ambulance company says.

The Camera ID feature is also helping the company divide unassigned hours of service, according to the article, and the company has used the technology to defend against false accusations.

In-car displays evolving

Synaptics TDDI display line has integrated touch controls for automotive display screens, Electronic Products reports

TDDI stands for “touch-and-display driver integration,” and LCD displays with the technology have become a preferred option for in-vehicle displays, according to the article. OLED displays, though popular in mobile devices, televisions and wearables, have been integrated with only a few car models.

A capacitive touch sensor enables user interactions, and could also be used for fingerprint biometrics, which is a focus for Synaptics.

The Mercedes Benz 2021 S-Class will come with improvements to its voice control system and a touchscreen interface with fewer buttons, according to The Verge, along with personal profiles accessible through fingerprint, face, or voice biometrics.

The MBUX infotainment system will bring in a 12.8-inch OLED touchscreen, with an onboard fingerprint sensor and voice recognition standard in the line’s main offering, while cameras to support facial recognition are optional. Voice controls include up to 20 different commands, activated by saying “Hey Mercedes,” with support for 27 languages. Several onboard microphones can also differentiate between speakers.

The system also includes an augmented reality system, with features like directions projected onto the windshield so that they appear to be floating in the driver’s field of view.

Deepfakes for training autonomous driving

Startup Oxbotica is applying deepfake techniques to the training of autonomous vehicles, Autocar writes, in the hopes of improving system decision-making without putting it through challenging situations in the real world.

Deepfake techniques are used to generate new scenarios, or alter footage to allow self-driving systems to learn from them without adding to the substantial number of miles the autonomous vehicle industry has already put on test automobiles. The idea is that like all machine-learning systems, the quantity and variety of training data is important to effective judgements.

“The use of deepfakes enables us to test countless scenarios, which will not only enable us to scale our real-world testing exponentially, it’ll also be safer,” says Oxbotica Co-founder Paul Newman.

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