A semiconductor device capable of performing filter processing while suppressing an increase in processing time is provided. The semiconductor device includes a microcontroller. The microcontroller comprises a CPU, a memory and a CAN-controller. The memory stores software. The CPU executes the software stored in the memory. The CAN controller is configured to add label information to the message information. The CAN routing software stored in the memory implements a filtering function for performing a filter processing for determining whether or not to route the message information by using the label information.

A semiconductor device capable of performing filter processing while suppressing an increase in processing time is provided. The semiconductor device includes a microcontroller. The microcontroller comprises a CPU, a memory and a CAN-controller. The memory stores software. The CPU executes the software stored in the memory. The CAN controller is configured to add label information to the message information. The CAN routing software stored in the memory implements a filtering function for performing a filter processing for determining whether or not to route the message information by using the label information.

To obtain a rider-assistance system capable of providing a rider of a straddle-type vehicle with a sense of comfort and safety during a turn, and a control method for such a rider-assistance system.

The present invention provides the rider-assistance system that assists with driving by the rider of the straddle-type vehicle and includes a controller. The controller includes: an object identification section that identifies an object approaching a side of the straddle-type vehicle on the basis of output of a communication device that wirelessly receives information output from infrastructure equipment or another vehicle; a body position information acquisition section that acquires position information of at least a part of a body of the rider on the turning straddle-type vehicle; a collision possibility determination section that determines a collision possibility of the rider with the object identified by the object identification section on the basis of the position information acquired by the body position information acquisition section; and a safety operation performing section that causes the rider-assistance system to perform safety operation in the case where the collision possibility determination section determines that the collision possibility is high.

Techniques are described in which sensor data is used to determine one or more of background noise or occupancy associated with a passenger cabin of a vehicle. The sensor data, in turn, is used to determine an operating state for one or more components of the vehicle (e.g., pumps, compressors, fans, blowers, etc.) such that an amount of background noise within the passenger cabin is reduced (e.g., when a passenger/occupant is present). In various examples, the operating state of the component may operate in a different, though louder, state (e.g., higher efficiency, greater power, etc.) when an occupant is not present or proximate the component.

Techniques are described in which sensor data is used to determine one or more of background noise or occupancy associated with a passenger cabin of a vehicle. The sensor data, in turn, is used to determine an operating state for one or more components of the vehicle (e.g., pumps, compressors, fans, blowers, etc.) such that an amount of background noise within the passenger cabin is reduced (e.g., when a passenger/occupant is present). In various examples, the operating state of the component may operate in a different, though louder, state (e.g., higher efficiency, greater power, etc.) when an occupant is not present or proximate the component.

A pedestrian tracking system includes: a buffer or a memory configured to store a trajectory sequence of a pedestrian; a step attention module and a control module. The step attention module iteratively performs a step attention process to predict states of the pedestrian. Each iteration of the step attention process includes the step attention module: learning the stored trajectory sequence to provide time-dependent hidden states, reshaping each of the time-dependent hidden states to provide two-dimensional tensors; condensing the two-dimensional tensors via convolutional networks to provide convolutional sequences; capturing global information of the convolutional sequences to output a set of trajectory patterns represented by a new sequence of tensors; learning time-related patterns in the new sequence and decoding the new sequence to provide one or more of the states of the pedestrian; and modifying the stored trajectory sequence to include the predicted one or more of the states of the pedestrian.

An apparatus includes a processor configured to be disposed with a vehicle and a memory coupled to the processor. The memory stores instructions to cause the processor to receive, at least two of: radar data, camera data, lidar data, or sonar data. The sensor data is associated with a predefined region of a vicinity of the vehicle while the vehicle is traveling during a first time period. At least a portion of the vehicle is positioned within the predefined region during the first time period. The method also includes detecting that no other vehicle is present within the predefined region. An environment of the vehicle during the first time period is classified as one state from a set of states that includes at least one of dry, light rain, heavy rain, light snow, or heavy snow, based on at least two of the sensor data to produce an environment classification. An operational parameter of the vehicle based on the environment classification is modified.

An apparatus includes a processor configured to be disposed with a vehicle and a memory coupled to the processor. The memory stores instructions to cause the processor to receive, at least two of: radar data, camera data, lidar data, or sonar data. The sensor data is associated with a predefined region of a vicinity of the vehicle while the vehicle is traveling during a first time period. At least a portion of the vehicle is positioned within the predefined region during the first time period. The method also includes detecting that no other vehicle is present within the predefined region. An environment of the vehicle during the first time period is classified as one state from a set of states that includes at least one of dry, light rain, heavy rain, light snow, or heavy snow, based on at least two of the sensor data to produce an environment classification. An operational parameter of the vehicle based on the environment classification is modified.

The present teaching relates to method, system, and medium, for generating an augmented alert in a hybrid vehicle. First information indicating an upcoming switch in an operating mode of the vehicle is received, which specifies a set of tasks, arranged in an order, to be completed by a driver in the vehicle to achieve the upcoming switch, and a task duration for each of the set of tasks by which the task is to be completed. A current state of the driver is obtained and used to determine a set of warnings to alert the driver to perform the set of tasks. Each warning corresponds to a task in the set of tasks and is created based on the current state of the driver. A warning schedule is generated based on the set of warnings in the order of the set of tasks and transmitted so that warnings in the warning schedule are delivered to the driver.

The present teaching relates to method, system, and medium, for generating an augmented alert in a hybrid vehicle. First information indicating an upcoming switch in an operating mode of the vehicle is received, which specifies a set of tasks, arranged in an order, to be completed by a driver in the vehicle to achieve the upcoming switch, and a task duration for each of the set of tasks by which the task is to be completed. A current state of the driver is obtained and used to determine a set of warnings to alert the driver to perform the set of tasks. Each warning corresponds to a task in the set of tasks and is created based on the current state of the driver. A warning schedule is generated based on the set of warnings in the order of the set of tasks and transmitted so that warnings in the warning schedule are delivered to the driver.