A speech recognition method includes receiving audible data and user data. The audible data includes information about an utterance by the user. The user data includes information about movements by the user. The method further includes fusing the audible data and the user data to obtain fused data and determining at least one spoken word of the utterance based on the fused data.

An emotion determination device includes a memory and a processor connected to the memory. The processor is configured to acquire a facial image of a user, set an evaluation value for each of plural emotion classifications including a neutral state based on the acquired facial image, compute a correction value using an evaluation value set based on a neutral facial image acquired at a timing corresponding to a neutral state, and determine an emotion of the user by applying the computed correction value to the set evaluation values.

A vehicle outputs a driving sound generated based on the driver's preference rather than outputting a uniform driving sound. The vehicle includes a sensor that acquires at least one of a facial image, an audio signal, and a biometric signal. A database stores a plurality of sound sources classified according to driver information. A controller calculates the driver information based on at least one of the facial image, the audio signal, and the biometric signal, selects any one of the plurality of sound sources stored in the database based on the calculated driver information, generates a driving sound based on the selected sound source, and operates a speaker to output the generated driving sound.

A hardware processor of a mobile object executes the program stored in a storage device to acquire a state of at least one occupant getting on a mobile object capable of moving on both a roadway and a predetermined region different from the roadway; to recognize whether the mobile object is moving on the roadway or the predetermined region; to recognize presence of a contact portion between the predetermined region and the roadway in a traveling direction of the mobile object; to control the speed of the mobile object at least partially, and limit a speed at which the mobile object is moving on the roadway to a first speed and limit a speed at which the mobile object is moving on the predetermined region to a second speed slower than the first speed; and to bring a speed of the mobile object closer to the second speed when the mobile object is moving on the roadway, the contact portion is recognized within a predetermined range from the mobile object, and the state of the occupant is a predetermined state.

The present teaching relates to method, system, medium, and implementation of lane planning in an autonomous vehicle. Sensor data are received that capture ground images of a road the autonomous vehicle is on. Based on the sensor data, a current lane of the road that autonomous vehicle is currently occupying is detected. Lane control for the autonomous vehicle is planned based on the detected current lane and self-aware capability parameters in accordance with a driving lane control model. The self-aware capability parameters are used to predict operational capability of the autonomous vehicle with respect to a current location of the autonomous vehicle. The driving lane control model is generated based on recorded human driving data to achieve human-like lane control behavior in different scenarios.

The present teaching relates to method, system, medium, and implementation of lane planning in an autonomous vehicle. Sensor data are received that capture ground images of a road the autonomous vehicle is on. Based on the sensor data, a current lane of the road that autonomous vehicle is currently occupying is detected. Information indicating presence of a passenger in the vehicle is obtained and used to retrieve a personalized lane control model related to the passenger. Lane control for the autonomous vehicle is planned based on the detected current lane and self-aware capability parameters in accordance with the personalized lane control model. The self-aware capability parameters are used to predict operational capability of the autonomous vehicle with respect to a current location of the autonomous vehicle. The personalized lane control model is generated based on recorded human driving data.

A vehicle control device is provided in a vehicle and includes a microphone, a first voice recognition engine, a second voice recognition engine different from the first voice recognition engine, and a processor for executing the first voice recognition engine or the second voice recognition engine on the basis of the vehicle traveling mode when a command is received through the microphone.

The present teaching relates to method, system, medium, and implementation of lane planning in an autonomous vehicle. Sensor data are received that capture ground images of a road the autonomous vehicle is on. Based on the sensor data, a current lane of the road that autonomous vehicle is currently occupying is detected. Lane control for the autonomous vehicle is planned based on the detected current lane and self-aware capability parameters in accordance with a driving lane control model. The self-aware capability parameters are used to predict operational capability of the autonomous vehicle with respect to a current location of the autonomous vehicle. The driving lane control model is generated based on recorded human driving data to achieve human-like lane control behavior in different scenarios.

The present teaching relates to method, system, medium, and implementation of lane planning in an autonomous vehicle. Sensor data are received that capture ground images of a road the autonomous vehicle is on. Based on the sensor data, a current lane of the road that autonomous vehicle is currently occupying is detected. Information indicating presence of a passenger in the vehicle is obtained and used to retrieve a personalized lane control model related to the passenger. Lane control for the autonomous vehicle is planned based on the detected current lane and self-aware capability parameters in accordance with the personalized lane control model. The self-aware capability parameters are used to predict operational capability of the autonomous vehicle with respect to a current location of the autonomous vehicle. The personalized lane control model is generated based on recorded human driving data.

A vehicular cabin monitoring system includes a radar assembly disposed in a cabin of a vehicle and operable to capture radar data. The radar assembly is housed in an interior rearview mirror assembly of the vehicle and includes at least one radar transmit antenna that is operable to transmit radar waves and at least one radar receive antenna that is operable to receive radar waves. A control includes a data processor for processing radar data captured by the radar assembly. The control, via processing at the data processor of radar data captured by the radar assembly, detects movement of a body part of an occupant present in the cabin of the vehicle. The control, responsive to detecting movement of the body part of the occupant in the cabin of the vehicle, generates a control command associated with at least one operation of the vehicle.