Dev Kit Enables Cars to Express Their Emotions

Renesas Electronics has announced that it has developed a development kit for its R-Car that takes advantage of “emotion engine”, an artificial sensibility and intelligence technology pioneered by cocoro SB Corp. The new development kit enables cars with the sensibility to read the driver’s emotions and optimally respond to the driver’s needs based on their emotional state.

The development kit includes cocoro SB’s emotion engine, which was developed leveraging its sensibility technology to recognize emotional states such as confidence or uncertainty based on the speech of the driver. The car’s response to the driver’s emotional state is displayed by a new driver-attentive user interface (UI) implemented in the Renesas R-Car system-on-chip (SoC). Since it is possible for the car to understand the driver’s words and emotional state, it can provide the appropriate response that ensures optimal driver safety.


As this technology is linked to artificial intelligence (AI) based machine learning, it is possible for the car to learn from conversations with the driver, enabling it to transform into a car that is capable of providing the best response to the driver. Renesas plans to release the development kit later this year.

Renesas  demonstrated its connected car simulator incorporating the new development kit based on cocoro SB’s emotion engine at the SoftBank World 2017 event earlier this month in held by SoftBank at the Prince Park Tower Tokyo.

Renesas considers the driver’s emotional state, facial expression and eyesight direction as key information that combines with the driver’s vital signs to improve the car and driver interface, placing drivers closer to the era of self-driving cars. For example, if the car can recognize the driver is experiencing an uneasy emotional state, even if he or she has verbally accepted the switch to hands free autonomous-driving mode, it is possible for the car to ask the driver “would you prefer to continue driving and not switch to autonomous-driving mode for now?” Furthermore, understanding the driver’s emotions enables the car to control vehicle speed according to how the driver is feeling while driving at night in autonomous-driving mode. By providing carmakers and IT companies with the development kit that takes advantage of this emotion engine, Renesas hopes to expand the possibilities for this service model to the development of new interfaces between cars and drivers and other mobility markets that can take advantage of emotional state information. Based on the newly-launched Renesas autonomy, a new advanced driving assistance systems (ADAS) and automated driving platform, Renesas enables a safe, secure, and convenient driving experience by providing next-generation solutions for connected cars.

Renesas Electronics America |

Natural Human-Computer Interaction

Recent innovations in both hardware and software have brought on a new wave of interaction techniques that depart from mice and keyboards. The widespread adoption of smartphones and tablets with capacitive touchscreens shows people’s preference to directly manipulate virtual objects with their hands.

Going beyond touch-only interaction, the Microsoft Kinect sensor enables users to play

This shows the hand tracking result from Kinect data. The red regions are our tracking results and the green lines are the skeleton tracking results from the Kinect SDK (based on data from the ChAirGest corpus:

This shows the hand tracking result from Kinect data. The red regions are our tracking results and the green lines are the skeleton tracking results from the Kinect SDK (based on data from the ChAirGest corpus:

games with their entire body. More recently, Leap Motion’s new compact sensor, consisting of two cameras and three infrared LEDs, has opened up the possibility of accurate fingertip tracking. With Project Glass, Google is pioneering new technology in the wearable human-computer interface. Other new additions to wearable technology include Samsung’s Galaxy Gear Smartwatch and Apple’s rumored iWatch.

A natural interface reduces the learning curve, or the amount of time and energy a person requires to complete a particular task. Instead of a user learning to communicate with a machine through a programming language, the machine is now learning to understand the user.

Hardware advancements have led to our clunky computer boxes becoming miniaturized, stylish sci-fi-like phones and watches. Along with these shrinking computers come ever-smaller sensors that enable a once keyboard-constrained computer to listen, see, and feel. These developments pave the way to natural human-computer interfaces.
If sensors are like eyes and ears, software would be analogous to our brains.

Understanding human speech and gestures in real time is a challenging task for natural human-computer interaction. At a higher level, both speech and gesture recognition require similar processing pipelines that include data streaming from sensors, feature extraction, and pattern recognition of a time series of feature vectors. One of the main differences between the two is feature representation because speech involves audio data while gestures involve video data.

For gesture recognition, the first main step is locating the user’s hand. Popular libraries for doing this include Microsoft’s Kinect SDK or PrimeSense’s NITE library. However, these libraries only give the coordinates of the hands as points, so the actual hand shapes cannot be evaluated.

Fingertip tracking using a Kinect sensor. The green dots are the tracked fingertips.

Our team at the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory has developed methods that use a combination of skin-color and motion detection to compute a probability map of gesture salience location. The gesture salience computation takes into consideration the amount of movement and the closeness of movement to the observer (i.e., the sensor).

We can use the probability map to find the most likely area of the gesturing hands. For each time frame, after extracting the depth data for the entire hand, we compute a histogram of oriented gradients to represent the hand shape as a more compact feature descriptor. The final feature vector for a time frame includes 3-D position, velocity, and hand acceleration as well as the hand shape descriptor. We also apply principal component analysis to reduce the feature vector’s final dimension.

A 3-D model of pointing gestures using a Kinect sensor. The top left video shows background subtraction, arm segmentation, and fingertip tracking. The top right video shows the raw depth-mapped data. The bottom left video shows the 3D model with the white plane as the tabletop, the green line as the arm, and the small red dot as the fingertip.

The next step in the gesture-recognition pipeline is to classify the feature vector sequence into different gestures. Many machine-learning methods have been used to solve this problem. A popular one is called the hidden Markov model (HMM), which is commonly used to model sequence data. It was earlier used in speech recognition with great success.

There are two steps in gesture classification. First, we need to obtain training data to learn the models for different gestures. Then, during recognition, we find the most likely model that can produce the given observed feature vectors. New developments in the area involve some variations in the HMM, such as using hierarchical HMM for real-time inference or using discriminative training to increase the recognition accuracy.

Ying Yin

Ying Yin is a PhD candidate and a Research Assistant at the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory. Originally from Suzhou, China, Ying received her BASc in Computer Engineering from the University of British Columbia in Vancouver, Canada, in 2008 and an MS in Computer Science from MIT in 2010. Her research focuses on applying machine learning and computer vision methods to multimodal human-computer interaction. Ying is also interested in web and mobile application development. She has won awards in web and mobile programming competitions at MIT.

Currently, the newest development in speech recognition at the industry scale is a method called deep learning. Earlier machine-learning methods require careful selection of feature vectors. The goal of deep learning is automatic discovery of powerful features from raw input data. So far, it has shown promising results in speech recognition. It can possibly be applied to gesture recognition to see whether it can further improve accuracy.

As component form factors shrink, sensor resolutions grow, and recognition algorithms become more accurate, natural human-computer interaction will become more and more ubiquitous in our everyday life.