Lip reading, emotion sensing, face biometrics vie for place in smart auto stack

Your car may soon know when you’re tipsy, stressed, or just mouthing words into the wind. From Ford’s lip-reading patent to radar systems that sense your mood and (of course) biometric driver identification, the latest wave of automotive AI is turning vehicles into surprisingly perceptive co-pilots.

Ford publishes patent for in-car lip reading

Vehicle voice control systems have been around for a while – but soon, they may be able to listen in on our most secretive conversations. Ford has filed a patent application for software that reads the occupant’s lips using cameras and sensors inside the vehicle.

The feature would not be used for lip-based biometric authentication. Instead, the automaker says the vehicle would turn on lip reading in situations where normal voice controls do not work due to wind or other noise, for example, while driving in a convertible with the top down.

​Aside from lip movement captured by cameras, the system could also rely on acoustic signals: the vehicle could emit inaudible sound waves and analyze the echoes bouncing back from the user’s lips and mouth with the help of machine learning.

“The captured video and sensor data are processed using machine learning algorithms that are trained on large datasets of lip movements and corresponding speech to learn the patterns and nuances of lip reading,” says patent 20260095520, recently published by the USPTO.

The patent also predicts the use of gesture- and facial-expression-detection software to determine whether the user is having difficulty interacting with the system. Nodding the head could be interpreted as acknowledging a verbal interaction with the vehicle, while shaking the head or appearing confused would lead the system to believe that the user is having difficulties. The vehicle could then choose to increase the audio volume or slow its speech.

The vehicle would determine that it is in a convertible state using image data or other sensors and measure the ambient noise level. If the noise level is above a certain threshold, the vehicle would enable lip-reading mode to determine the words spoken by the user.

Researchers explore radar-based emotion recognition in AVs

Another technology that we may see more of in vehicles is emotion detection. A group of international researchers recently tested a radar-based emotion-recognition framework for autonomous vehicles (AVs), called DriveEmo-FL, and published their results in Nature.

Understanding how drivers feel while behind the wheel has become an important area of research, as a driver’s emotional state can affect how self-driving cars make decisions, how vehicles adapt their behavior, and how comfortable the overall driving experience feels.

In the past, researchers used tools such as wearable devices and cameras that read facial expressions to detect emotions. While these approaches work to some degree, they come with drawbacks, such as privacy concerns, and cameras do not always work well across different environments. To address these problems, researchers have begun exploring radar-based technologies, especially mmWave radar.

DriveEmo-FL uses a compact mmWave radar sensor to capture and measure subtle upper-body gestures associated with different emotional states. The raw radar data is processed and is passed to EmoNet, a lightweight dual-stream deep learning model. The analysis allows the model to better capture the relationship between body movement patterns and emotional states.

“By linking emotional state detection to adaptive AV behaviors, DriveEmo-FL offers a proactive, intelligent interface for future emotion-aware intelligent transportation systems,” the researchers conclude.

DriveEmo-FL was tested under various driving conditions in a series of controlled driving tests, according to the researchers, who hail from Wuhan University, Ajou University in South Korea, Northern Border University in Saudi Arabia, and COMSATS University in Pakistan.

Scientists detect drunk driving and emotions with single AI model

Researchers from Australia and the UK are developing a driver monitoring system (DMS) based on facial recognition that can detect fatigue, emotional expression and blood alcohol simultaneously.

Most AI models are task-specific. This technology, however, relies on a single 3D deep learning model for multiple tasks, including recognizing expression and assessing the drivers’ physiological state, the researchers say. The system detected blood alcohol concentration with nearly 90 percent accuracy and drowsiness with 95 percent accuracy, while also classifying impairment across three levels — sober, moderate, or severe.

“This algorithm is smart, because it can tell the difference between whether a driver is sleepy, just making a facial expression, or affected by alcohol,” says Syed Zulqarnain Gilani from Edith Cowan University (ECU) in Perth

Gilani published a paper alongside colleagues from the University of Western Australia and Birmingham City University in the UK.

DriveBuddyAI patents facial recognition system for driver identification

Indian driver monitoring systems (DMS) developer DriveBuddyAI has been granted a patent for a facial recognition-based driver identification system designed for use in moving vehicles, Manufacturing Today India reports.

The camera-based technology uses computer vision and AI to identify drivers under varying lighting conditions and when accessories such as caps or scarves partially obscure the face. The company says the biometric approach is more accurate than traditional key-based identification methods.

The system is aimed at logistics operators and supports compliance with driving hours regulations, as well as driver attendance tracking, wage management, and fatigue monitoring. It connects to DriveBuddyAI’s existing product suite, which includes a previously patented driver scoring system called CARDs.

The patent comes as India moves to tighten regulations around truck drivers’ working hours in response to road accidents linked to fatigue.

CEO Nisarg Pandya said the technology ensures fleet operators know who is driving, for how long, and under what conditions, providing a basis for enforcing safe driving limits and supporting driver development.

Article Topics

automotive biometrics  |  biometric authentication  |  biometric identification  |  computer vision  |  DriveBuddyAI  |  face biometrics  |  Ford Motor Company  |  patents  |  research  |  USPTO